This repository is a fork of 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.
- May 12 2025: User can now control if/which operations with tensors held in RAM are offloaded to the GPU. See PR 405
- May 12 2025: Compatibility issues with mainline
llama.cppGGUFs for DeepSeek models with MLA enabled were resolved in PR 394. The lower prompt processing performance resulting from usingllama.cpp-style MLA GGUFs was recovered in PR 409. - May 11 2025: 🚀 Slightly faster flash attention for DeepSeek models on CUDA, along with extending compatibility to Touring or newer GPUs. See PR 408
- May 9 2025: Support for LlaMA-3-Nemotron models added, see PR 377
- May 7 2025: 🚀 Faster TG for DeepSeek models with GPU or hybrid GPU/CPU inference. See PR 386 for details. Caveat: Ampere or newer Nvidia GPU required
- May 4 2025: 🚀 Significant token generation performance improvement on CUDA with Flash Attention for GQA models. For details and benchmarks see PR #370
- April 29 2025: Qwen3 support added, see PR 355
- April 26 2025: GLM-4 support added, see PR 344
- April 26 2025: Command-A support added, see PR 341
- April 22 2025: Support for the latest Microsoft Bitnet model added, see PR 337
- April 21 2025: ik_llama.cpp builds and runs successfully on Android (using termux), see PR 336
- April 17 2025: 🚀 Better CPU Flash Attention token generation performance, see PR 332
- April 13 2025:
IQ1_Mquantization improvements, see PR 327 - April 10 2025: LLaMA-4 support added, see PR 321. In the PR there are also some custom quantization recipes for L4-Scout provided.
- April 7 2025:
IQ2_XSquantization improvements, see PR 312 - April 3 2025: 🚀 Much faster MoE implementation on Metal, see PR 307
- April 1 2025: Quantization improvements for
Q2_K, Q4_K, Q5_K, Q4_1, Q5_1, see PR 302 - March 28 2025: Quantization imrovements for
Q4_0, Q5_0, Q6_0, Q3_K, Q6_K, IQ4_XS, IQ4_NL, see PR 295 - March 25 2025: 🚀 Better MoE performance on CUDA
- March 23 2025: 🚀 Better batched processing speed for DeepSeek models
- March 22 2025: Gemma3 support added
- March 21 2025: 🚀 FlashMLA-3: fastest CPU-only inference for DeepSeek models
- March 18 2025: Reduce compute buffer size
- March 17 2025: 🚀 FlashMLA-2 performance improvements
- March 12 2025: Allow
Q8_0KV cache with FlashMLA-2 on CUDA - March 10 2025: 🚀 Better TG performance for MoE models on CUDA
- March 9 2025: 🚀 FlashMLA on CUDA
- March 8 2025: 🚀 Faster FlashMLA CPU implementation
- March 7 2025: Custom quantization mixes using regular expressions
- March 5 2025: 🚀 FlashMLA on CUDA
- March 3 2025: 🚀 Introducing FlashMLA - MLA with Flash Attention
- March 1 2025: Smart Expert Reduction for faster DeepSeek inference
- Feb 27 2025: MLA without transposed cache
- Feb 25 2025: Tensor overrides for better control where model weights are stored (GPU or CPU)
- Feb 23 2025: 🚀 Fused FFN ops for faster MoE inference
- Feb 23 2025:
sweep-bench- better performance benchmarking - Feb 20 2025: 🚀 Fast GEMM/GEMV for
IQ1_S - Feb 19 2025:
Q8_KV- new type for 8-bit KV-cache quantization - Feb 13 2025: Allow
Q8_0quantized cache with MLA - Feb 11 2025: 🚀 Flash Attention support for DeepSeek models
- Feb 9 2025: 🚀 MLA for DeepSeek models
- Jan 23 2025: DeepSeek-V3 support added
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
- The Wiki page has performance comparisons to mainline
llama.cpp - This guide is a good place to start if you came here because of DeepSeek models
- This discussion is about running DeepSeek-V3/R1 on a 16 x 3090 setup
- This discussion describes the new quantization types available in
ik_llama.cpp
Contributions in form of pull requests, issue submissions (bug reports, feature requests), or general discussions, are welcome.
MIT
