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The best headphones for running in 2024

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There’s nothing quite like getting into the zone during a run, and for many of us, the right soundtrack is a requirement. Whether you need classic rock, reggaeton or an immersive audiobook to properly settle into your morning jog, you’ll get the best listening experience with a good pair of wireless headphones for running. But not all wireless workout headphones are created equally, and runners need to consider specific needs before investing in a pair like how long your runs are, what type of music or other audio you prefer listening to and how much you want to block out the world during a session. If you’re just getting into a new running routine and need a pair of headphones that can keep up, or you’re a seasoned pro looking for an upgrade, you’ve come to the right place. We’ve tested more than a dozen Bluetooth headphones for running to come up with our top picks and help you decide which is right for you.

What to look for in running headphones

Design

Before diving in, it’s worth mentioning that this guide focuses on wireless earbuds. While you could wear over-ear or on-ear Bluetooth headphones during a run, most of the best headphones available now do not have the same level of durability. Water and dust resistance, particularly the former, is important for any audio gear you plan on sweating with or taking outdoors, and that’s more prevalent in the wireless earbuds world.

Most earbuds have one of three designs: in-ear, in-ear with hook or open-ear. The first two are the most popular. In-ears are arguably the most common, while those with hooks promise better security and fit since they have an appendage that curls around the top of your ear. Open-ear designs don’t stick into your ear canal, but rather sit just outside of it. This makes it easier to hear the world around you while also listening to audio, and could be more comfortable for those who don’t like the intrusiveness of in-ear buds.

Water resistance and dust protection

Water resistance and dust protection is crucial for running headphones since you’ll likely be sweating while wearing them. Also, if you have the unfortunate luck of getting caught in the rain during a run, at least your gear will survive. Here’s a quick rundown of ingress protection (IP) ratings, which you’ll see attached to many earbuds on the market today. The first digit after the abbreviation rates dust protection on a scale from one to six — the higher, the better. The second digit refers to water- resistance, or waterproofing in some cases, ranked on a scale from one to nine. A letter “X” in either position means the device isn’t rated for the corresponding material.

Check out this guide for an even more detailed breakdown. All of the earbuds we tested for this guide have at least an IPX4 rating (most have even more protection), which means they can withstand sweat and splashes but do not have dust protection.

Active noise cancellation and transparency mode

Active noise cancellation (ANC) is becoming a standard feature on wireless earbuds, at least in those above a certain price. If you’re looking for a pair of buds that can be your workout companion and continue to serve you when you’re off the trail, ANC is good to have. It adds versatility by allowing you to block out the hum of your home or office so you can focus, or give you some solitude during a busy commute on public transit.

But an earbud’s ability to block out the world goes hand in hand with its ability to open things back up should you need it. Many earbuds with ANC support some sort of “transparency mode” or various levels of noise reduction. This is important for running headphones because you don’t want to be totally oblivious to what’s going on around you when you’re exercising outside along busy streets. Lowering noise cancelation levels to increase your awareness will help with that.

Battery life

All of the earbuds we tested have a battery life of six to eight hours. In general, that’s what you can expect from this space, with a few outliers that can get up to 15 hours of life on a charge. Even the low end of the spectrum should be good enough for most runners, but it’ll be handy to keep the buds’ charging case on you if you think you’ll get close to using up all their juice during a single session.

Speaking of, you’ll get an average of 20-28 extra hours of battery out of most charging cases and all of the earbuds we tested had holders that provided at least an extra 15 hours. This will dictate how often you actually have to charge the device — as in physically connect the case with earbuds inside to a charging cable, or set it on a wireless charger to power up.

How we test headphones for running

When testing headphones for running, I wear each contender during as many runs as possible. I typically run three to five days each week, completing at least a 5K (3.01 miles) each time. I’m looking for comfort arguably most of all, because you should never be fussing with your earbuds when you’re on the tread or trail (as a note, I primarily run outside). I’m also paying attention to fit over time, particularly if the earbuds get slippery or loose while I sweat, or if they tend to pop out or feel less stable in my ears as I pick up speed or make quick movements.

I also use the earbuds when not running to take calls and listen to music, podcasts and the like throughout the day. Many people will want just one pair of earbuds that they can use while exercising and just doing everyday things, so I evaluate each pair on their ability to be comfortable and provide a good listening experience in multiple different activities.

While I am also listening for audio quality, I’m admittedly not an expert in this space. My colleague Billy Steele holds that title at Engadget, and you’ll find much more detailed information about sound quality for some of our top picks in his reviews and buying guides. Here, however, I will make note of audio-quality characteristics if they stood out to me (i.e. if a pair of earbuds had noticeably strong bass out of the box, weak highs, etc). Most of the wireless workout headphones we tested work with companion apps that have adjustable EQ settings, so you’re able to tweak sound profiles to your liking in most cases.

Photo by Valentina Palladino / Engadget

Connectivity: Wireless | Style: In-ear with wingtip | Assistant support: Google Assistant, Siri

Read our full review of the Beats Fit Pro

The Beats Fit Pro came out at the head of the pack thanks to their comfortable, secure design, good sound quality and transparency mode and general ease of use, among other things. As Billy detailed in his review, the Fit Pro’s wingtip design sets them apart from other Beats earbuds and makes them particularly good for running and other workouts. The buds are fairly small and light, and the wingtip on each is flexible enough to hug your ear snugly without too much pressure. This helps them feel more secure when you’re moving around a lot, be it during a morning jog or while taking a HIIT class. The buds are also rated IPX4 — not the highest level of protection out of everything I tested, but enough for even my sweatiest sessions.

As it were, the Beats Fit Pro stayed put during every single run and workout. However, adjusting their position on the fly can lead to one of my few gripes: accidental presses of the onboard controls. There were a number of times when I went to move a bud and I ended up pausing my music in the process because the buttons are so easy to trigger. While I do prefer the tactile feedback of a physical button as opposed to the touch controls on other earbuds, Beats should have changed their position on the buds or made them a bit harder to fully engage.

Sound quality is solid and, thanks to its punchy bass and overall balance, is especially suitable for exercising. Spatial audio support is great to have, and while Adaptive EQ means you can’t tweak the EQ yourself like you can with other buds, it does make for consistently good audio quality. It’s also one less thing to play with out of the box, which I expect many people will appreciate; these earbuds are a true unbox-and-go option.

ANC is also great on the Beats Fit Pro, but transparency mode is what really shines. Like I said, full ANC can be dangerous when you’re running outside, whether you’re in the middle of a city or on a trail. It pays to be able to hear what’s going on around you, at least a little bit, and the Fit Pro’s transparency mode was the best out of the earbuds I tried. Similar to Apple’s AirPods Pro, it sounded more natural than on our runner-up pick, and it’s easy to turn on or off either with onboard buttons or from the control panel on your iOS or Android smartphone.

Speaking of, the Beats Fit Pro works particularly well with iPhones thanks to the built-in H1 chip, but Android users can download the companion app to access things like quick pairing, control customizations and a battery status indicator. I didn’t get into detail about the setup process because, well, there isn’t much of one. As soon as you unbox the Fit Pro and open the case, the H1 chip communicates with iPhones and other iOS devices to almost instantly pair the buds and get them ready for use.

I will say, though, that I was surprised by one thing when I unboxed the Fit Pro: how cheap their charging case feels. While it provides an extra 21 hours on top of the buds’ promised six hours of life, the build quality of the case itself feels like a real step down compared to the buds themselves. Some charging cases on much less expensive alternatives felt more substantial than the Beats Fit Pro’s, but I recognize that it won’t deter most people (including me). Aside from that and the touch controls, the Beats Fit Pro offers a complete package for runners and other athletes alike, one that can be used all day, not just during training sessions. Plus, their standard $200 price isn’t too cost-prohibitive, and they can often be found on sale for less.

Pros

  • Comfortable IPX4 design
  • Great sound quality with Adaptive EQ
  • Effective ANC and useful transparency mode
  • Multipoint connectivity
  • Solid battery life
Cons

  • Onboard controls are easy to accidentally press
  • Charging case feels cheap

$180 at B&H Photo

Photo by Valentina Palladino / Engadget

Connectivity: Wireless | Style: In-ear| Assistant support: Siri, Google Assistant

The Jabra Elite 8 Active almost bested the Beats Fit Pro, but ultimately the latter won thanks to their wingtip design and more natural-sounding transparency mode. But aside from those two things, the Elite 8 Active are just as good, if not better than, the Fit Pro.

First and foremost, the Elite 8 Active has one of the highest durability ratings of any earbuds we tested. Rated IP68, it’s protected against all kinds of dust and debris and it’ll survive being submerged in water at high pressure. Jabra also subjected the Elite 8 Active to military-grade testing, protecting them from excessive humidity, high temperature, rain and altitude. This is more protection than most people need, but it will likely give some people peace of mind to know that these buds can take a beating. For runners, you won’t have to worry if you get caught in a sudden downpour during your final mile.

That extra protection doesn’t make the Elite 8 Active bulky or unattractive as one might assume. These buds are some of the most comfortable I tried, with a lightweight design and a secure fit. The soft-touch finish on the buds themselves and their charging case adds a level of luxury that most others I tested did not have, too. There are onboard controls as well, and they’re not as prone to accidental presses as those on the Beats Fit Pro.

As one of Jabra’s more expensive devices, the Elite 8 Active have a great sound profile out of the box but if you prefer, you can use the company’s app to tweak the EQ using a line graph that spans bass, mid and treble. You can also save customized presets. The app offers six preconfigured settings, and I found myself using Bass Boost and Energize most while exercising (they’re pretty similar with strong bass, but Energize emphasizes highs a bit more). These personalization options will give the Elite 8 Active an edge over the Beats Fit Pro for some. On top of all that, Jabra’s buds support spatial sound with Dolby Audio.

The Elite 8 Active offer adaptive noise cancellation, and they do a good job of analyzing your environment and blocking out interferences. “HearThrough” is Jabra’s version of transparency mode, and it’s useful when running outside in a city or an area with lots of traffic even if it’s not as natural-sounding as the same mode on the Beats Fit Pro. Jabra’s is designed to neutralize wind noise while also letting you stay aware of your surroundings, so you can hear your podcasts no matter how gusty the environment. I ran in some particularly windy weather while testing these out, and I had consistently good listening experiences with both HearThrough and ANC activated.

As for battery life, the Elite 8 Active will get eight hours on a charge with ANC on, and an additional 24 hours in the charging case. You can get up to 56 hours of total use if you’re not using ANC, which is remarkable. The case also feels more substantial than that of the Beats Fit Pro, and you can wirelessly charge it (a feature that’s left out on Beats’ buds).

Ultimately, the Beats Fit Pro and the Jabra Elite 8 Active are neck and neck in our top picks list. But while the Jabras offer a bit more customization and more durability, not everyone will need those bonuses. However, if you’re an athlete who likes to play around with sound profiles or you want some of the most durable wireless earbuds available today, the Jabra Elite 8 Active are the ones to get.

Pros

  • Comfortable fit
  • IP68 water and dust protection
  • Spatial sound with Dolby Audio
  • Strong ANC
  • Multipoint connectivity
  • Solid battery life
Cons

  • HearThrough doesn’t sound as natural as other transparency modes

$160 at Amazon

Photo by Valentina Palladino / Engadget

Connectivity: Wireless | Style: In-ear | Assistant support: Alexa, Siri, Google Assistant

The Jabra Elite 4 Active offer the best value for the money of any pair of earbuds on our list. For $120, you get an IP57-rated design, solid sound quality with adjustable EQ, good ANC, the same HearThrough transparency mode as the Elite 8 Active, app connectivity and a total of 28 hours of battery life. These were some of the easiest buds for me to “pick up and go” with, whether it was for an impromptu walk around the block, a sweaty HIIT session in my basement or an hour of work during which I really needed to block out distractions and get things done.

Like the Elite 8 Active, the Elite 4 Active is super comfortable and Jabra has really gotten the onboard controls right on this series. The buttons are not so easy to press that you accidentally trigger them whenever you adjust the fit, and they provide satisfying feedback when you actually do press them. Sound quality and ANC are impressive, and I basically never had to worry about running out of battery before I thought to myself, hey, you might want to top these up.

The main differences between the Elite 4 Active and the more-expensive Elite 8 Active are that the latter have a higher IP rating, spatial sound support with Dolby Audio, a longer overall battery life (56 hours with the charging case), voice guidance and that satisfying soft-touch finish. The IP rating and extended battery life are the two main features that could compel some to splurge on the Elite 8 Active instead. Also, spatial audio is nice if you have the buds semi-permanently placed in your ears constantly pumping out tunes, regardless of the activity. Otherwise, though, you’re getting a ton of excellent features with these $120 earbuds.

Pros

  • Great value for the money
  • Comfortable IP57-rated design
  • Good sound quality and ANC
  • Multipoint connectivity
  • Good battery life
Cons

  • No spatial sound with Dolby Audio like the Elite 8 Active has

$90 at Amazon

Photo by Valentina Palladino / Engadget

Connectivity: Wireless | Style: In-ear with hook | Assistant support: None

If you have less than $50 to spend, you can’t go wrong with the $30 JLab Go Air Sport. I didn’t have high expectations going into testing these, but I was quickly impressed by their fit and sound quality. Lots of devices billed as workout-friendly headphones have this hook that wraps around the top of your ear, and on the Go Air Sport it does help keep things securely attached to your head. The hooks on these buds in particular are quite flexible and have a soft-touch finish, which makes them more comfy (I tried a few similarly designed buds with much stiffer hooks that were a pain in more ways than one.) Admittedly, a hook design will take some getting used to if you’re new to it, but it’s a surefire way to get a little extra stability during intense workouts.

Sound quality is pretty good on these buds as well, although not nearly as balanced as the Jabra Elite 8 Active or the Beats Fit Pro. I also appreciate that you can cycle through three different EQ modes — Signature, Balanced and Bass Boost — using the onboard controls. There’s no app to fuss with, and that was a nice change of pace for me after mostly testing buds with some kind of software controls.

You can expect eight hours of playtime on the Go Air Sport, plus another 24 hours of battery life with its charging case. While the USB-A cable built into the bottom of the case is handy, really should be a USB-C connector instead (it’s the year 2024, after all). The case is also on the bulky side; you can still throw it into a backpack or purse easily, but it’s not as svelte as cases you’ll see with more expensive buds.

Pros

  • Affordable
  • Impressive sound quality for the price
  • IP55-rated design
  • Good battery life
Cons

  • Built-in USB-A charging cable is a bit outdated
  • Large case
  • Hook design won’t be for everyone

$25 at Amazon

Photo by Valentina Palladino / Engadget

Connectivity: Wireless | Style: Open-ear | Assistant support: None

I was apprehensive about trying open-ear headphones, especially while running. But the Shokz OpenFit pleasantly surprised me from the first time I put them on. Earbuds with open designs like this allow for more situational awareness, with the goal being to let noise in rather than block it out. The OpenFits do a great job of this without skimping on sound quality or comfort.

The buds themselves almost float over your ear cavern and Shokz’s soft-finish “dolphin arc” hook is flexible enough to securely wrap around the top of your ear without putting too much pressure on it. There’s a bud-like portion at the other end of the hook that acts as counterbalance, resulting in a reliable fit that never faltered during all sorts of activities including running, strength training and indoor cycling. Granted, none of those exercises involve shaking your head too much; maybe don’t wear the OpenFit to listen to head-banging death metal (if you can’t control yourself).

Sound quality is solid considering the design, and the OpenFit gets pretty loud as well. These buds have Shokz’s Direct Pitch technology, which uses reverse sound waves to optimize the distance and angle to your ear canal. The company claims this helps reduce sound leakage. In my testing, I found that true to a certain extent. The OpenFit had the best sound quality and overall volume out of all of the open-ear devices I tried, but if you crank the volume up to the max (or close), the person next to you will definitely hear what you’re listening to.

As I alluded to previously, these aren’t for anyone who wants to block the world out during exercise (or otherwise). But the OpenFit might be the best option for those who live in cities or anyone who constantly runs outside amongst traffic, pedestrians and other hazards. There’s no question that you’ll hear what’s going on around you and that can be crucial to keeping yourself safe on those streets.

Pros

  • Comfortable open-ear design
  • Design allows for more situational awareness
  • Good sound quality and volume
Cons

  • No ANC
  • Not as secure when compared to in-ear or hook-toting buds

$180 at Amazon

Others headphones for running we tested

Apple AirPods Pro

The Apple AirPods Pro have an IP54 rating, which protects them from brief encounters with dust and splashes. While that’s more dust protection than many other earbuds we tested, it’s the same level of water-resistance that most exercise-specific competitors have. We generally like the AirPods Pro, but the Beats Fit Pro offer many of the same features and conveniences (namely good transparency mode and the H1 chip), with a design that’s more appropriate for working out.

Beats Powerbeats Pro

The Powerbeats Pro are a good alternative to the Beats Fit Pro if you’re a stickler for a hook design. However, they cost $50 more than the Fit Pro (although they’re often hovering around $180) and don’t offer any significant upgrades or additional features aside from their design. They’re also quite old at this point (having launched in 2019) and it appears Beats is putting more effort into updating its newer models instead.

Anker Soundcore AeroFit Pro

The Soundcore AeroFit Pro is Anker’s version of the Shokz OpenFit, but I found the fit to be less secure and not as comfortable. The actual earbuds on the AeroFit Pro are noticeably bulkier than those on the OpenFit and that caused them to shift and move much more during exercise. They never fell off of my ears completely, but I spent more time adjusting them than I did enjoying them.

JBL Endurance Peak 3

The most noteworthy thing about the Endurance Peak 3 is that they have the same IP68 rating as the Jabra Elite 8 Active, except they only cost $100. But, while you get the same protection here, you’ll have to sacrifice in other areas. The Endurance Peak 3 didn’t blow me away when it came to sound quality or comfort (its hook is more rigid than those on my favorite similarly designed buds) and their charging case is massive compared to most competitors.

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The Garmin Forerunner 55 GPS running watch drops to a record low of $150

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Spring is so close now that it’s almost in our grasp. Say goodbye to wearing a huge coat to go to the gym and hello to outdoor activities. With that in mind, there many great GPS running watches out there to track your time in the sun, including the on sale Garmin’s Forerunner 55. The smartwatch is down to $150 from $200 — a 25 percent discount that brings the device to its record-low price.

Garmin

The Garmin Forerunner 55 is a slightly upgraded version of the company’s 45S (which we rave about here). It comes with features such as a heart rate monitor, respiration rate, menstrual tracking, pacing strategies, and more. It also has a GPS that helps track distance, speed, and location and creates pacing strategies for a selected course.

While it’s billed as a running watch, the Garmin Forerunner 55 also works for activities such as pilates, cycling, breathwork and swimming. As a smartwatch, the battery can last 20 days, while being in GPS mode gives the watch 20 hours — way more time than it takes to go for a run, stop for a snack and run back.

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Keychain charger keeps iPhone, Apple Watch running for just $16

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At just $16, this versatile keychain charger is a crazy-good deal. If you want to ensure your iPhone, Apple Watch and AirPods remain juiced up and ready no matter where you go, this portable charging solution is a must. There’s nothing more portable than this 2-in-1 Wireless Keychain Charger, which can efficiently charge both an iPhone, Apple Watch and AirPods no matter where you are.

It’s small enough to fit in a pocket. And for a limited time, it’s available at a pint-size price, too. Through March 10, it’s on sale for only $15.19 with code ENJOY20.

Keychain charger for iPhone and Apple Watch

Measuring just 3.54 inches by 1.95 inches by 0.78 inches, and weighing only 2.45 ounces, this high-tech magnetic keychain charger for iPhone, Apple Watch and AirPods is ultraportable and just the thing for on-the-go use. Simply attach it to your keys, backpack or purse, and take it wherever you go.

This dual-purpose keychain charger packs a 2,500mAh battery that can charge your Apple Watch between three and five times, and it is fully compatible with all Apple Watch series. Simply place your device on the magnetic charging pad and let the magic of wireless charging handle the rest. It’ll also work with AirPods with a wireless charging case.

The LED display even tells you exactly what’s going on with your charge in real time.

To charge up your iPhone, simply connect via the Type-C input/output port. (You can charge other compatible devices, too.) Whether you’ve depleted your battery snapping vacation photos, playing games during your commute or FaceTiming a friend, you can enjoy a quick, easy charge wherever you are.

Save on a handy accessory for any Apple Watch or iPhone owner

Say goodbye to low batteries in your Apple Watch, AirPods and iPhone with this trusty, ultra-portable charging solution.

You can get this 2-in-1 Wireless Keychain Charger for your iPhone and Apple Watch at an extra 20% off its regular price of $25.99 when you use code ENJOY20. That drops the price to just $15.19.

Note: The price drop on this combo keychain charger is only available through March 10 at 11:59 p.m. Pacific.

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H1 Humanoid Robot sets new world record for running

H1 Humanoid Robot sets new world record for running

The H1 humanoid robot has achieved a new world record by reaching a maximum speed of 3.3 meters per second, which is comparable to human running speeds. The robot, which is also capable of performing complex dance moves, demonstrates advanced dynamic coordination and power landing capabilities. The H1 robot, standing at approximately 180 cm tall and weighing 47 kg, is equipped with a depth camera and a 3D LiDAR for navigation.

A Chinese startup has created the H1 humanoid robot, a machine that can run at a speed of 3.3 meters per second. That’s a new record! This robot isn’t just quick; it moves with a grace and power that’s almost human. It’s getting closer to running just like we do. Xingxing Wang, the Founder of Unitree Robotics, independently developed his first robot, a simple bipedal robot, in the winter break of the first year of university in early 2010.

360 3D lidar depth perception robot

The H1 is quite the sight. It stands tall at 180 cm and weighs 47 kg. It’s built in a way that lets you add arms and other parts to it. This makes the H1 perfect for many jobs. Think about factories, emergencies, or even keeping watch. It’s expected to reach speeds of up to 5 meters per second soon. That’s going to make a big difference in work that covers large areas or in dangerous places.

This robot knows where it’s going, thanks to its top-notch sensors. It has a depth camera and 3D LiDAR. These help it move through tough terrain without trouble. High precision and knowing what’s happening around it are key for the H1. Plus, you can control it with your smartphone, which makes it easy to use.

H1 Humanoid Robot runs at 3.3 meters per second

Here are some other articles you may find of interest on the subject of developing humanoid robots and current technologies in the field :

The H1 costs about as much as a fancy car, $90,000. But it could save money and help businesses do more. Speed isn’t its only skill. The H1 can climb stairs, jump, and even dance. It’s got moves that show off its amazing coordination.

The company doesn’t just make humanoid robots. They have robotic dogs too. Each one is made for a different job. The strong B2 model can run fast, jump high, and carry loads. The Go2 model is for everyday people and schools. This shows that the startup wants to reach all kinds of customers.

With the H1 and its robotic dog friends, we’re at a turning point in robotics. They’re ready to do all sorts of things. They have new and exciting features, and they’re priced to sell. These robots are setting the stage for a time when they’ll be a big part of work life. They’ll make things faster and safer in many industries. For full specifications or details on the amazing H1 humanoid robot developed by Unitree jump over to the official website.

Filed Under: Technology News, Top News





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AutoGen Studio running purely on local LLMs

AutoGen Studio running purely on local LLMs

If you are interested in running artificial intelligence and AI models locally the ability  to integrate local large language models (LLMs) into your own systems for personal or business use. AutoGen Studio, a cutting-edge AI platform, has made this possible, allowing users to harness the power of LLMs directly within their workspace. This integration is a significant step forward for those who wish to maintain control over their data while benefiting from the advanced capabilities of language models.

AutoGen Studio has introduced a new feature that allows users to replace the default GPT-4 model with an open-source alternative. This gives users the freedom to customize their AI tools and retain data sovereignty, a critical concern for many businesses and individuals who are wary of storing sensitive information on external servers.

“With AutoGen Studio, users can rapidly create, manage, and interact with agents that can learn, adapt, and collaborate. As we release this interface into the open-source community, our ambition is not only to enhance productivity but to inspire a level of personalized interaction between humans and agents”  explains the Microsoft team over on the official GitHub blog.

To begin using this feature, users must first download and install LM Studio, a versatile platform that supports various operating systems including macOS, Windows, and Linux. The installation process is straightforward, with a user-friendly guide to help get LM Studio up and running on your device.

AutoGen Studio running local large language models (LLMs)

Once installed, the next step is to set up a local server. This server will act as the central hub for your chosen LLM, providing an API endpoint that connects AutoGen Studio with the language model. This connection is vital for the seamless operation of the AI tools within your workspace. LM Studio offers a selection of LLMs to choose from, each with its own strengths and suited for different project requirements.

For example, the Hermes 2.5 mral 7B model is a versatile option that can be downloaded and used as the driving force behind your linguistic tasks. Once again thanks to Prompt Engineering  for creating a fantastic overview and demonstration of how AutoGen Studio can be run purely on local large language models opening up a wide variety of possibilities and applications for both personal and business use.

Here are some other articles you may find of interest on the subject of large language models and AutoGen :

After selecting and setting up your LLM, you’ll need to configure AutoGen Studio. This involves creating new agents and workflows that will utilize the capabilities of your local LLM. These agents and workflows are at the heart of AutoGen Studio’s functionality, enabling users to automate a wide range of tasks with the intelligence of the LLM.

Before deploying your agents, it’s wise to test them in AutoGen Studio’s playground. This simulated environment allows you to refine your workflows and ensure that your agents perform as expected. It’s an essential step in the development process, helping to iron out any issues before going live.

It’s important to be aware of the limitations that come with open-source LLMs. Some may not have the capability to generate visuals or perform function calls. Understanding these limitations is key to successfully integrating LLMs into your projects. For tasks that require these advanced features, you may need to look into more sophisticated open-source LLMs.

For those with projects that demand more complex functionalities, the open-source LLM ecosystem offers a range of models that may fit the bill. Exploring this ecosystem can lead to the discovery of a model that is capable of handling the intricate tasks required by your project.

The integration of local LLMs with AutoGen Studio through LM Studio provides users with powerful language modeling tools that can be customized to meet specific needs while maintaining privacy and control over data. By following the steps outlined above, users can create a tailored AI solution that aligns with their unique requirements. This integration is a testament to the flexibility and adaptability of AI technology, offering a new level of customization for those looking to incorporate AI into their workflows.

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Running Mixtral 8x7B Mixture-of-Experts (MoE) on Google Colab’s free tier

Running Mixtral 8x7B MoE in Google Colab

if you are interested in running your very own AI models locally  on your home network or hardware you might be interested that it is possible to run Mixtral 8x7B on Google Colab.  Mixtral 8x7B is a high-quality sparse mixture of experts model (SMoE) with open weights. Licensed under Apache 2.0, Mixtral outperforms Llama 2 70B on most benchmarks with 6x faster inference

The ability to run complex models on accessible platforms is a significant advantage for researchers and developers. The Mixtral 8x7B Mixture of Experts (MoE) model is one such complex AI tool that has been making waves due to its advanced capabilities. However, the challenge of running the new AI model arises when users attempt to run this model on Google Colab’s free tier, which offers only 16GB of Video Random Access Memory (VRAM), while Mixtral 8x7B typically requires a hefty 45GB to run smoothly. This difference in available memory has led to the development of innovative techniques that enable the model to function effectively, even with limited resources.

A recent paper has introduced a method that allows for fast inference by offloading parts of the model to the system’s RAM. This approach is a lifeline for those who do not have access to high-end hardware with extensive VRAM. The Mixtral 8x7B MoE model, designed by MRAI AI, is inherently sparse, meaning it activates only the necessary layers when required. This design significantly reduces the memory footprint, making it possible to run the model on platforms with less VRAM.

The offloading technique is a game-changer when VRAM is maxed out. It transfers parts of the model that cannot be accommodated by the VRAM to the system RAM. This strategy allows users to leverage the power of the Mixtral 8x7B MoE model on standard consumer-grade hardware, bypassing the need for a VRAM upgrade.

Google Colab runing Mixtral 8x7B MoE AI model

Check out the tutorial below kindly created by Prompt Engineering which provides more information on the research paper and how you can run Mixtral 8x7B MoE in Google Colab utilising less memory than normally required.

Here are some other articles you may find of interest on the subject of Mixtral :

Another critical aspect of managing VRAM usage is the quantization of the model. This process involves reducing the precision of the model’s computations, which decreases its size and, consequently, the VRAM it occupies. The performance impact is minimal, making it a smart trade-off. Mixed quantization techniques are employed to ensure that the balance between efficiency and memory usage is just right.

To take advantage of these methods and run the Mixtral 8x7B MoE model successfully, your hardware should have at least 12 GB of VRAM and sufficient system RAM to accommodate the offloaded data. The process begins with setting up your Google Colab environment, which involves cloning the necessary repository and installing the required packages. After this, you’ll need to fine-tune the model parameters, offloading, and quantization settings to suit your hardware’s specifications.

An integral part of the setup is the tokenizer, which processes text for the model. Once your environment is ready, you can feed data into the tokenizer and prompt the model to generate responses. This interaction with the Mixtral 8x7B MoE model allows you to achieve the desired outputs for your projects. However, it’s important to be aware of potential hiccups, such as the time it takes to download the model and the possibility of Google Colab timeouts, which can interrupt your work. To ensure a seamless experience, it’s crucial to plan ahead and adjust your settings to prevent these issues.

Through the strategic application of offloading and quantization, running the Mixtral 8x7B MoE model on Google Colab with limited VRAM is not only possible but also practical. By following the guidance provided, users can harness the power of large AI models on commonly available hardware, opening up new possibilities in the realm of artificial intelligence. This approach democratizes access to cutting-edge AI technology, allowing a broader range of individuals and organizations to explore and innovate in this exciting field.

Image Credit : Prompt Engineering

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Running Llama 2 on Apple M3 Silicon Macs locally

Running Llama 2 on Apple M3 Silicon hardware

Apple launched its new M3 Silicon back in October and has now made it available in a number of different systems allowing users to benefit from the next generation processing provided by the family of chips. If you are interested in learning more about running large language models on the latest Apple M3 silicon you’ll be pleased to know that Techno Premium as been testing out and demonstrating what you can expect from the processing power when running Meta’s Llama 2 large language model on the Apple silicon hardware. Check out the video below.

If you’re intrigued by the capabilities of large language models like Llama 2 and how they perform on cutting-edge hardware, the M3 chip’s introduction offers a fantastic opportunity to run large language models locally. Benefits include :

  • Enhanced GPU Performance: A New Era in Computing The M3 chip boasts a next-generation GPU, marking a significant advancement in Apple’s silicon graphics architecture. Its performance is not just about speed; it’s about efficiency and introducing groundbreaking technologies like Dynamic Caching. This feature ensures optimal memory usage for each task, a first in the industry. The benefits? Up to 2.5 times faster rendering speeds compared to the M1 chip series. This means, for large language models like Llama 2, the processing of complex algorithms and data-heavy tasks becomes smoother and more efficient.
  • Unparalleled CPU and Neural Engine Speeds The M3 chip’s CPU has performance cores that are 30% faster and efficiency cores that are 50% faster than those in the M1. The Neural Engine, crucial for tasks like natural language processing, is 60% faster. These enhancements ensure that large language models, which require intensive computational power, can operate more effectively, leading to quicker and more accurate responses.

Running LLMs on Apple M3 Silicon hardware

Here are some other articles you may find of interest on the subject of Apple’s latest M3 Silicon chips :

  • New Apple M3 iMac gets reviewed
  • New Apple M3, M3 Pro, and M3 Max silicon chips with next gen
  • Apple M3 MacBook Pro gets reviewed
  • Apple M3 iMac rumored to launch in October
  • New Apple MacBook Pro M3 Pro 14 and 16-inch laptops
  • Apple M3 Max Macbook Pro, 14 and 16 Core CPUs compared
  • New Apple MacBook Pro M3 14-inch laptop from $1,599
  • Advanced Media Processing Capabilities A noteworthy addition to the M3 chip is its new media engine, including support for AV1 decode. This means improved and efficient video experiences, which is essential for developers and users working with multimedia content in conjunction with language models.
  • Redefined Mac Experience Johny Srouji, Apple’s senior vice president of Hardware Technologies, highlights the M3 chip as a paradigm shift in personal computing. Its 3-nanometer technology, enhanced GPU and CPU, faster Neural Engine, and extended memory support collectively make the M3, M3 Pro, and M3 Max chips a powerhouse for high-performance computing tasks, like running advanced language models.
  • Dynamic Caching: A Revolutionary Approach Dynamic Caching is central to the M3’s new GPU architecture. It dynamically allocates local memory in hardware in real-time, ensuring only the necessary memory is used for each task. This efficiency is key for running complex language models, as it optimizes resource usage and boosts overall performance.
  •  Introduction of Ray Tracing and Mesh Shading The M3 chips bring hardware-accelerated ray tracing to Mac for the first time. This technology, crucial for realistic and accurate image rendering, also benefits language models when they are used in conjunction with graphics-intensive applications. Mesh shading, another new feature, enhances the processing of complex geometries, important for graphical representations in AI applications.
  • Legendary Power Efficiency Despite these advancements, the M3 chips maintain Apple silicon’s hallmark power efficiency. The M3 GPU delivers performance comparable to the M1 while using nearly half the power. This means running large language models like Llama 2 becomes more sustainable and cost-effective.

If you are considering large language models like Llama 2 locally, the latest Apple M3 range of chips offers an unprecedented level of performance and efficiency. You will be pleased to know that whether it’s faster processing speeds, enhanced graphics capabilities, or more efficient power usage, the Apple M3 chips cater to the demanding needs of advanced AI applications.

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Improve your running with real-time analytics using CLOMP

CLOMP provides runners with real-time performance analytics

Runners looking to improve their running performance might be interested in a new piece of technology called CLOMP. A unique data-driven workout tool that leverages medical-grade sensors to measure muscle oxygen levels, thereby optimizing athletic performance.

This innovative tool is a game-changer for both casual runners and professional athletes, offering a comprehensive suite of features that take workouts to the next level. Featuring a companion application that is available for both iOS and Android devices users can easily analyse data and record their training history for analysis and improvement.

CLOMP running trainer

CLOMP

One of the key attributes of CLOMP is its use of medical-grade sensors to measure muscle oxygen levels. These sensors are based on Functional Near-Infrared Spectroscopy (fNIRS) technology, a cutting-edge technology that is currently in the patent process due to its high level of accuracy. This technology allows CLOMP to precisely calculate the lactate threshold, a critical metric that determines the point at which fatigue sets in. By understanding this threshold, athletes can optimize their training to maximize performance and prevent overexertion, which is a common cause of injuries in sports.

Value early bird pledges are now available for the ingenious project from roughly $259 or £212 (depending on current exchange rates), offering a considerable discount of approximately 38% off the suggested retail price, while the Indiegogo crowd funding is under way.

CLOMP real-time data

In addition to muscle oxygen levels and lactate threshold, CLOMP provides real-time analytics on various other performance metrics. These include heart rate, Fat Burn Ratio, Optimum Fat Burn Speed, and fat burn in grams during a workout. These metrics are crucial in determining an athlete’s capabilities at peak performance, rest, and average exertion. Moreover, CLOMP also offers a Diet Mode, which calculates the Optimum Fat Burn Speed based on the chosen run duration. This feature helps athletes optimize their fat burn efficiency, a vital aspect of achieving fitness goals.

Improve your running

CLOMP stands out for its ability to provide customized workout plans based on individual data. The tool uses the data gathered from its sensors and analytics to determine the optimal workout for individual fitness goals. This personalized approach ensures that athletes can achieve their fitness goals effectively and efficiently, whether they are beginners or professionals.

Beyond its high-tech features, CLOMP is also designed with user convenience and data privacy in mind. The tool is compatible with both iOS and Android devices and can work with other sports devices like Android and Apple Watch. This compatibility ensures that athletes can easily access their workout data and plans from their preferred devices. Moreover, all user data is encrypted to ensure privacy, reflecting CLOMP’s commitment to protecting user information.

If the CLOMP campaign successfully raises its required pledge goal and the project progresses smoothly, worldwide shipping is expected to take place sometime around December 2023. To learn more about the CLOMP real-time running analytics to improve your performance project play the promotional video below.

Real-time running analytics

What sets CLOMP apart even further is its provision of expert coaching. The tool is backed by Gerardo Barrios, a certified triathlon coach, and offers 16 different workout goals and training plans. This feature makes CLOMP suitable not only for athletes but also for coaches who want to measure the performance of their athletes and provide them with effective training plans.

CLOMP IOS and Android application

In summary, CLOMP is a revolutionary fitness tool that combines medical-grade sensor technology, real-time analytics, personalized workout plans, and expert coaching to help athletes optimize their performance. Whether you are a casual runner, a professional athlete, or a coach, CLOMP offers a comprehensive solution to improve your running and achieve your fitness goals. With its commitment to accuracy, data privacy, and user convenience, CLOMP is poised to redefine the fitness technology landscape.

For a complete list of all available pledge options, stretch goals, extra media and functionality overview for the real-time running analytics to improve your performance, jump over to the official CLOMP crowd funding campaign page by investigating the link below.

Source : Indiegogo

Disclaimer: Participating in Kickstarter campaigns involves inherent risks. While many projects successfully meet their goals, others may fail to deliver due to numerous challenges. Always conduct thorough research and exercise caution when pledging your hard-earned money.

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Build a personal AI assistant running on your laptop with LM Studio

build a custom personal AI assistant on your laptop

If you are interested in learning more about how you can easily create your very own personal AI assistant running it locally from your laptop or desktop PC. You might be interested in a new program and framework called LM Studio. LM Studio is a lightweight program designed to make it easy to install and use of local language models on personal computers rather than third-party servers. One of the key features of LM Studio is its user-friendly interface making it easy to manage a variety of different AI models depending on your needs all from one interface

Thanks to its minimalist UI and chatbot interface LM Studio has been specifically designed to provide users with an efficient and easy-to-use platform for running language models. This feature is particularly beneficial for users who are new to the world of large language models, as it simplifies the process of running these models locally. Which until a few months ago was quite a tricky undertaking to do but has now been simplified thanks to the likes of LM Studio and other framework such as Ollama and others.

How to run personal AI assistance locally on your laptop

One of the standout features of LM Studio is the ability for users to start their own inference server with just a few clicks. This feature offers users the ability to play around with their inferences, providing them with a deeper understanding of how these models work. Additionally, LM Studio provides a guide for choosing the right model based on the user’s RAM, further enhancing the user experience.

Other articles we have written that you may find of interest on the subject of large language models :

Benefits of running LLM is locally

The benefits of running large language models on your laptop or desktop PC locally :

  • Hands-On Experience: Working directly with the model code allows you to understand the architecture, data preprocessing, and other technical aspects in detail.
  • Customization: You have the freedom to tweak parameters, modify the architecture, or even integrate the model with other systems to see how it performs under different conditions.
  • Debugging and Profiling: Running models locally makes it easier to debug issues, profile computational performance, and optimize code. You can get a clear picture of how resources like memory and CPU are utilized.
  • Data Privacy: You can experiment with sensitive or proprietary datasets without sending the data over the network, thus maintaining data privacy.
  • Cost-Efficiency: There’s no need to pay for cloud-based machine time for experimentation, although the upfront hardware cost and electricity can be significant.
  • Offline Availability: Once downloaded and set up, the model can be run without an internet connection, allowing you to work on AI projects anywhere.
  • End-to-End Understanding: Managing the entire pipeline, from data ingestion to model inference, provides a holistic view of AI systems.
  • Skill Development: The experience of setting up, running, and maintaining a large-scale model can be a valuable skill set for both academic and industrial applications.

Another significant feature of LM Studio is its compatibility with any ggml Llama, MPT, and StarCoder model on Hugging Face. This includes models such as Llama 2, Orca, Vicuna, Nous Hermes, WizardCoder, MPT, among others. This wide range of compatibility allows users to explore different models, expanding their knowledge and experience in the field of large language models.

LM Studio also allows users to discover, download, and run local LMS within the application. This feature simplifies the process of finding and using different models, eliminating the need for multiple platforms or programs. Users can search for and download models that are best suited for their computer, enhancing the efficiency and effectiveness of their work.

Ensuring privacy and security is a key focus of LM Studio. The program is 100% private, using an encryption method and providing a clear statement that explains how it uses HTTP requests. This feature provides users with the assurance that their data and information are secure.

User feedback and continuous improvement are key components of LM Studio’s approach. The program has a feedback tab where users can provide constructive feedback and request features. This feature ensures that LM Studio continues to evolve and improve based on user needs and preferences. Furthermore, LM Studio has a Discord where users can get more information, provide feedback, and request features.

LM Studio is a comprehensive platform for experimenting with local and open-source Large Language Models. Its user-friendly interface, wide range of compatibility, and focus on privacy and security make it an ideal choice for users looking to explore the world of large language models. Whether you’re a seasoned professional or a beginner in the field, LM Studio offers a platform that caters to your needs.

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Running Raspberry Pi OS Bullseye on an Orange Pi Zero 3

Running Raspberry Pi OS Bullseye on an Orange Pi Zero 3The last week or so the official Raspberry Pi development team rolled out a new version of its Raspberry Pi OS operating system now featuring Debian Bookworm. The update is an incremental update from the previous Debian Bullseye release but marks a significant milestone in the evolution of the Raspberry Pi ecosystem, introducing major architectural changes that promise to enhance performance, security, and user experience. However if you have a Orange Pi Zero 3 mini PC you might be interested to know that you can run the older Raspberry Pi OS Bullseye operating system on it with success.

Check out the video below to learn more about how to do it. The Orange Pi Zero 3 is a versatile and powerful single-board computer that offers a range of features and capabilities. This open-source development board is not just a consumer product, but a tool designed for anyone who wants to use technology to create and innovate. It’s a simple, fun, and useful tool that can be used to shape the world around you.

The Orange Pi Zero 3 is powered by an Allwinner H618 quad-core 64-bit 1.5GHz high-performance Cortex-A53 processor. This CPU is complemented by a Mali G31 MP2 GPU, which supports OpenGL ES 1.0/2.0/3.2 and OpenCL 2.0. This combination of CPU and GPU provides the Orange Pi Zero 3 with the power and performance to handle a range of tasks, from running complex applications to playing high-definition video.

Orange Pi Zero 3 mini PC

The Orange Pi Zero 3 comes with a choice of 1GB, 1.5GB, 2GB, or 4GB LPDDR4 memory, which is shared with the GPU. This provides the board with the flexibility to handle a range of tasks, from running multiple applications simultaneously to processing large amounts of data.

In terms of storage, the Orange Pi Zero 3 features a micro SD card slot and 16MB SPI Flash. This provides ample space for storing applications, data, and operating systems. The board supports a range of operating systems, including Android TV 12, Ubuntu, and Debian.

 Previous articles we have written that you might be interested in on the subject Raspberry Pi :

The Orange Pi Zero 3 also features a range of connectivity options. It supports 10/100M/1000M Ethernet, and comes with an AW859A chip that supports IEEE 802.11 a/b/g/n/ac and Bluetooth 5.0. This allows the board to connect to a range of devices and networks, making it a versatile tool for a range of applications.

For video output, the Orange Pi Zero 3 features a Micro HDMI 2.0a port and supports TV CVBS output, which supports PAL/NTSC via a 13pin expansion board. Audio output is provided through the Micro HDMI output and a 3.5mm audio port, which is also available via the 13pin expansion board.

The Orange Pi Zero 3 mini PC is powered through a USB Type C interface and features three USB 2.0 ports for connecting peripherals. The board also features a 26pin connector with I2C, SPI, UART, and multiple GPIO ports, and a 13pin connector with USB 2.0, TV-OUT, LINE OUT, IR-RX, and 3 GPIO ports.

The board also features a debug serial port with UART-TX, UART-RX, and GND, and an LED light for power and status indication. An infrared receiver is also included, which supports infrared remote control via the 13pin expansion board. In terms of physical specifications, the Orange Pi Zero 3 measures 85mm by 56mm and weighs just 30g. This compact size and light weight make it a portable and convenient tool for a range of applications.

The Orange Pi Zero 3 is a powerful and versatile single-board computer that offers a range of features and capabilities. Whether you’re a hobbyist looking to experiment with different operating systems and applications, or a professional looking for a powerful and flexible development tool, the Orange Pi Zero 3 offers a compelling option.

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