get-beam
beta9
| get-beam | beta9 | |
|---|---|---|
| 9 | 4 | |
| 102 | 855 | |
| - | 8.0% | |
| 7.3 | 9.9 | |
| about 1 year ago | 5 days ago | |
| Shell | Go | |
| - | GNU Affero General Public License v3.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
get-beam
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Show HN: Beta9 – Open-source, serverless GPU container runtime
- A custom runc container runtime
You can run Beta9 locally, or using a managed service, Beam Cloud (https://beam.cloud).
See it in action: https://github.com/beam-cloud/beta9
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Ask HN: Where to find an env with GPU for model training?
You should checkout https://beam.cloud (I'm the founder), it'll give you access to plenty of cloud GPU resources for training or inference.
Right now it's pretty hard to get GPU quota on AWS/GCP, so hopefully this is useful for you.
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Cloudflare launches new AI tools to help customers deploy and run models
Cloudflare AI and Replicate are great for running off-the-shelf models, but anything custom is going to incur a 10+ minute cold start.
For running custom fine-tuned models on serverless, you could look into https://beam.cloud which is optimized for serving custom models with extremely fast cold start (I'm a little biased since I work there, but the numbers don't lie)
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Workers AI: serverless GPU-powered inference on Cloudflare’s global network
Serverless only works if the cold boot is fast. For context, my company runs a serverless cloud GPU product called https://beam.cloud, which we've optimized for fast cold start. We see Whisper in production cold start in under 10s (across model sizes). A lot of our users are running semi-real time STT, and this seems to be working well for them.
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Ultrafast serverless GPU runtime for custom SD models
I’m Eli, and my co-founder and I built Beam to run workloads on serverless cloud GPUs with hot reloading, autoscaling, and (of course) fast cold start. You don’t need Docker or AWS to use it, and everyone who signs up gets 10 hours of free GPU credit to try it out.
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[D] We built Beam: An ultrafast serverless GPU runtime
Github with example apps and tutorials: https://github.com/slai-labs/get-beam/tree/main/examples
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How to Finetune Llama 2: A Beginner's Guide
In this blog post, I want to make it as simple as possible to fine-tune the LLaMA 2 - 7B model, using as little code as possible. We will be using the Alpaca Lora Training script, which automates the process of fine-tuning the model and for GPU we will be using Beam.
- Run CodeLlama on a Serverless GPU
beta9
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The Architecture Behind Lovable and Bolt
The only Beam-specific part are the sandboxes, but those can easily be swapped out for the vendor of your choice. The architecture we described isn't exclusive to our product.
Beam is fully OSS BTW: https://github.com/beam-cloud/beta9
- Show HN: Run GPU Workloads Across Multiple Clouds
- Show HN: Beta9 – Open-source, serverless GPU container runtime
- Beta9: The open-source serverless GPU container runtime
What are some alternatives?
discourse-ai
distributed-llama - Connect home devices into a powerful cluster to accelerate LLM inference. More devices means faster inference.
whisper-turbo - Cross-Platform, GPU Accelerated Whisper 🏎️
gpu-operator - NVIDIA GPU Operator creates, configures, and manages GPUs in Kubernetes
finetune-llama2
JetStream - JetStream is a throughput and memory optimized engine for LLM inference on XLA devices, starting with TPUs (and GPUs in future -- PRs welcome).