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61 lines
3.3 KiB
Markdown
61 lines
3.3 KiB
Markdown
# ik_llama.cpp: llama.cpp fork with better CPU performance
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[](https://opensource.org/licenses/MIT)
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## TL;DR
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This repository is a fork of [llama.cpp](https://github.com/ggerganov/llama.cpp) with better CPU and hybrid GPU/CPU performance, new SOTA quantization types, first-class Bitnet support, better DeepSeek performance via MLA, FlashMLA, fused MoE operations and tensor overrides for hybrid GPU/CPU inference, row-interleaved quant packing, etc.
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## Latest News
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* April 26 2025: GLM-4 support added
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* April 26 2025: Command-A support added
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* April 22 2025: Support for the latest Microsoft Bitnet model added
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* April 17 2025: Better CPU Flash Attention token generation performance
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* April 13 2025: `IQ1_M` quantization improvements
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* April 10 2025: LLaMA-4 support added
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* April 7 2025: `IQ2_XS` quantization improvements
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* April 3 2025: Much faster MoE implementation on Metal
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* April 1 2025: Quantization improvements for `Q2_K, Q4_K, Q5_K, Q4_1, Q5_1`
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* March 28 2025: Quantization imrovements for `Q4_0, Q5_0, Q6_0, Q3_K, Q6_K, IQ4_XS, IQ4_NL`
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* March 25 2025: Better MoE performance on CUDA
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* March 23 2025: Better batched processing speed for DeepSeek models
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* March 22 2025: Gemma3 support added
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* March 21 2025: FlashMLA-3: fastest CPU-only inference for DeepSeek models
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* March 18 2025: reduce compute buffer size
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* March 17 2025: FlashMLA-2 performance improvements
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* March 12 2025: Allow `Q8_0` KV cache with FlashMLA-2 on CUDA
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* March 10 2025: Better TG performance for MoE models on CUDA
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* March 9 2025: FlashMLA on CUDA
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* March 8 2025: Faster FlashMLA CPU implementation
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* March 7 2025: Custom quantization mixes using regular expressions
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* March 5 2025: FlashMLA on CUDA
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* March 3 2025: Introducing FlashMLA - MLA with Flash Attention
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* March 1 2025: Smart Expert Reduction for faster DeepSeek inference
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* Feb 27 2025: MLA without transposed cache
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* Feb 25 2025: tensor overrides for better control where model weights are stored (GPU or CPU)
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* Feb 23 2025: fused FFN ops for faster MoE inference
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* Feb 23 2025: `sweep-bench` - better performance benchmarking
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* Feb 20 2025: fast GEMM/GEMV for `IQ1_S`
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* Feb 19 2025: `Q8_KV` - new type for 8-bit KV-cache quantization
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* Feb 13 2025: allow `Q8_0` quantized cache with MLA
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* Feb 11 2025: Flash Attention support for DeepSeek models
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* Feb 9 2025: MLA for DeepSeek models
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* Jan 23 2025: DeepSeek-V3 support added
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## Resources
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There is no single point of reference describing all new `ik_llama.cpp` features. Pull requests often contain detailed information, so browsing the PRs is often the best way to learn about new features and how to use them. In addition
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* [The Wiki page](https://github.com/ikawrakow/ik_llama.cpp/wiki) has performance comparisons to mainline `llama.cpp`
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* [This guide](https://github.com/ikawrakow/ik_llama.cpp/discussions/258) is a good place to start if you came here because of DeepSeek models
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* [This discussion](https://github.com/ikawrakow/ik_llama.cpp/discussions/266) is about running DeepSeek-V3/R1 on a 16 x 3090 setup
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* [This discussion](https://github.com/ikawrakow/ik_llama.cpp/discussions/8) describes the new quantization types available in `ik_llama.cpp`
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## Contributing
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Contributions in form of pull requests, issue submissions (bug reports, feature requests), or general discussions, are welcome.
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## License
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MIT
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