@@ -23,7 +23,7 @@ Our vision for KTransformers is to serve as a flexible platform for experimentin
<h2 id="Updates">🔥 Updates</h2>
***Fed 10, 2025**: Support Deepseek-R1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~64x speedup. The detailed tutorial is [here](./doc/en/DeepseekR1_V3_tutorial.md)
***Fed 10, 2025**: Support Deepseek-R1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~28x speedup. The detailed tutorial is [here](./doc/en/DeepseekR1_V3_tutorial.md)
***Aug 28, 2024**: Support 1M context under the InternLM2.5-7B-Chat-1M model, utilizing 24GB of VRAM and 150GB of DRAM. The detailed tutorial is [here](./doc/en/long_context_tutorial.md).
***Aug 28, 2024**: Decrease DeepseekV2's required VRAM from 21G to 11G.
***Aug 15, 2024**: Update detailed [TUTORIAL](doc/en/injection_tutorial.md) for injection and multi-GPU.
- Compared to 4.51 tokens/s in llama.cpp with 2×32 cores, achieving up to **3.03× speedup**.
- Upcoming Open Source Release:
- AMX optimizations and selective expert activation will be open-sourced in V0.3.
- Currently available only in preview binary distribution, which can be downloaded [here](https://github.com/kvcache-ai/ktransformers/releases/download/v0.1.4/ktransformers-0.3.0rc0+cu126torch26fancy-cp311-cp311-linux_x86_64.whl).
- Currently available only in preview binary distribution, which can be downloaded [here](./doc/en/DeepseekR1_V3_tutorial.md).
-**Local 236B DeepSeek-Coder-V2:** Running its Q4_K_M version using only 21GB VRAM and 136GB DRAM, attainable on a local desktop machine, which scores even better than GPT4-0613 in [BigCodeBench](https://huggingface.co/blog/leaderboard-bigcodebench).
- Compared to 4.51 tokens/s in llama.cpp with 2×32 cores, achieving up to **3.03× speedup**.
We also give our upcoming optimizations previews, including an Intel AMX-accelerated kernel and a selective expert activation method, which will significantly enhance performance. With V0.3-preview, we achieve up to 286 tokens/s for prefill, making it up to **64× faster than llama.cpp** for local inference.
We also give our upcoming optimizations previews, including an Intel AMX-accelerated kernel and a selective expert activation method, which will significantly enhance performance. With V0.3-preview, we achieve up to 286 tokens/s for prefill, making it up to **28× faster than llama.cpp** for local inference.
The binary distribution is available now and the source code will come ASAP! Check out the wheel package [here](https://github.com/kvcache-ai/ktransformers/releases/download/v0.1.4/ktransformers-0.3.0rc0+cu126torch26fancy-cp311-cp311-linux_x86_64.whl)
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@@ -31,6 +31,7 @@ The binary distribution is available now and the source code will come ASAP! Che
We run our best performance tests (V0.2) on <br>
CPU: Intel (R) Xeon (R) Gold 6454S 1T DRAM (2 NUMA nodes) <br>
