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@@ -12,42 +12,42 @@ This repository is a fork of [llama.cpp](https://github.com/ggerganov/llama.cpp)
## Latest News
* May 4 2025: Significant token generation performance improvement on CUDA with Flash Attention for GQA models. For details and benchmarks see PR #370
* May 4 2025: 🚀 Significant token generation performance improvement on CUDA with Flash Attention for GQA models. For details and benchmarks see [PR #370](https://github.com/ikawrakow/ik_llama.cpp/pull/370)
* April 29 2025: Qwen3 support added
* April 26 2025: GLM-4 support added
* April 26 2025: Command-A support added
* April 22 2025: Support for the latest Microsoft Bitnet model added
* April 21 2025: ik_llama.cpp builds and runs successfully on Android (using termux)
* April 17 2025: Better CPU Flash Attention token generation performance
* April 17 2025: 🚀 Better CPU Flash Attention token generation performance
* April 13 2025: `IQ1_M` quantization improvements
* April 10 2025: LLaMA-4 support added
* April 7 2025: `IQ2_XS` quantization improvements
* April 3 2025: Much faster MoE implementation on Metal
* April 3 2025: 🚀 Much faster MoE implementation on Metal
* April 1 2025: Quantization improvements for `Q2_K, Q4_K, Q5_K, Q4_1, Q5_1`
* March 28 2025: Quantization imrovements for `Q4_0, Q5_0, Q6_0, Q3_K, Q6_K, IQ4_XS, IQ4_NL`
* March 25 2025: Better MoE performance on CUDA
* March 23 2025: Better batched processing speed for DeepSeek models
* 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 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_0` KV 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 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 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 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 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_0` quantized cache with MLA
* Feb 11 2025: Flash Attention support for DeepSeek models
* Feb 9 2025: MLA for DeepSeek models
* Feb 13 2025: Allow `Q8_0` quantized 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
## Resources