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erikkaum 
posted an update 6 months ago
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Releasing my first kernel 🔥 MaxSim

Late-interaction retrieval (ColBERT / PyLate) bottlenecks on materializing the full similarity matrix. This kernel avoids it by using tiled scoring with simdgroup_matrix (Metal) and WMMA.

The result is 3–5× speedup compared to naive PyTorch baseline 🔥

Benchmarks:
- SmallRerank (B=32, C=10): up to 3.2× (M3 Pro) / 2.8× (A100)
- HeavyRerank (B=32, C=100): up to 3.8× (M3 Pro) / 5.3× (A100)
- LongDocStress (Ld=1024): up to 6.2× (L4)

Try it out 👇
https://huggingface.co/kernels/erikkaum/maxsim
erikkaum 
posted an update over 1 year ago
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ZML just released a technical preview of their new Inference Engine: LLMD.

- Just 2.4GB container, which means fast startup times and efficient autoscaling
- Cross-Platform GPU Support: works on both NVIDIA and AMD GPUs.
- written in Zig

I just tried it out and deployed it on Hugging Face Inference Endpoints and wrote a quick guide 👇 You can try it in like 5 minutes!

https://huggingface.co/blog/erikkaum/test-driving-llmd-inference-engine
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erikkaum 
posted an update over 1 year ago
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We just released native support for @SGLang and @vllm-project in Inference Endpoints 🔥

Inference Endpoints is becoming the central place where you deploy high performance Inference Engines.

And that provides the managed infra for it. Instead of spending weeks configuring infrastructure, managing servers, and debugging deployment issues, you can focus on what matters most: your AI model and your users 🙌
erikkaum 
posted an update almost 2 years ago
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A while ago I started experimenting with compiling the Python interpreter to WASM.

To build a secure, fast, and lightweight sandbox for code execution — ideal for running LLM-generated Python code.

- Send code simply as a POST request
- 1-2ms startup times

Hack away:
https://github.com/ErikKaum/runner