ktransformers

Why does it have to be a dual CPU design? 8 channels of DDR5 4800 will still get you something like 300 GB per second bandwidth. Not amazing, but OK. Granite Rapids-W will likely be something like 50% better (cores and bandwidth). And the original message you were responding to was using a CPU with AMX and mixing it with a GPU like Nvidia 4900/5900. That way the large part of the model sits in the larger slower memory, and the active part in the GPU with the faster memory. Very cost effective and fast. (Something like generating 16 Tokens/s of 671B Deepseek R1 with a total hardware cost of $10-$20k.) They tried both single and dual CPU, with the latter about 30% faster....not necessarily worth it. https://github.com/kvcache-ai/ktransformers/blob/main/doc/en...

A better approach is to split the model with MOEs running on CPUs and MLAs running on GPU. See the ktransformers project: https://github.com/kvcache-ai/ktransformers/blob/main/doc/en... This takes advantage of the sparsity of MOE and the efficient KV-cache of MLA.

> I was wondering if anyone here has experimented with running a cluster of SBC for LLM inference? Ex. the Radxa ROCK 5C has 32GB of memory and also a NPU and only costs about 300 euros. Look into RPC. Llama.cpp supports it. * https://www.reddit.com/r/LocalLLaMA/comments/1cyzi9e/llamacp... > Last time I experimented it was possible to run some layers on the GPU and some on the CPU, but to me it seems more efficient to run everything on the GPU initially (but a few layers at a time so they fit in VRAM) and then switch to the CPU when doing the memory bound token generation. Moving layers over the PCIe bus to do this is going to be slow, which seems to be the issue with that strategy. I think it the key is to use MoE and be smart about which layers go where. This project seems to be doing that with great results: * https://github.com/kvcach

Hey Hacker News! We are excited to share the new version of KTransformers, a flexible framework designed for cutting-edge LLM inference optimizations! Leveraging state-of-the-art kernels from llamafile and marlin, KTransformers seamlessly enhances the performance of HuggingFace Transformers, making it possible to operate large 671B MoE models or extremely long 1M context locally with promising speed. KTransformers is a Python-centric framework designed with extensibility at its core. By implementing and injecting an optimized module with a single line of code, users gain access to a Transformers-compatible interface, RESTful APIs compliant with OpenAI and Ollama, and even a simplified ChatGPT-like web UI. For example, it allows you to integrate with all your familiar frontends, such as the VS Code plugin backed by Tabby. To demonstrate it