ik_llama.cpp: llama.cpp fork with better CPU performance

TL;DR

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.

Latest News

Model Support

LlaMA-3-Nemotron PR 377, Qwen3 PR 355, GLM-4 PR 344, Command-A PR 341, bitnet-b1.58-2B-4T PR 337, LLaMA-4 PR 321, Gemma3 PR 276, DeepSeek-V3 PR 176

Quantization

Quantization additions

Trellis quants (IQ2_KT, IQ3_KT, IQ4_KT)

Information and the original CUDA implementation in PR 113. Additional implementations: Metal PR 475, Neon PR 471, CPU PR 441

IQK quants

Information can be found in Discussion 8.

Initial implementations (Zen4, AVX2, NEON): IQ5_KS_R4 PR 426, IQ5_KS PR 422, IQ4_KS_R4 PR 150, IQ5_K_R4 PR 149, IQ2_K_R4 PR 146, IQ3_K_R4 PR 145, IQ4_K_R4 PR 138, IQ4_KSS PR 89, IQ2_KS PR 85, IQ4_KS PR 83, IQ6_K PR 14, IQ2_K, IQ3_K and IQ5_K PR 7, IQ4_K PR 6

Cuda implementations: IQ4_KS_R4 and IQ5_KS_R4 PR 493, IQ1_S_R4 PR 492, IQ1_M_R4 PR 494. IQ4_KS_R4 and IQ5_KS_R4 PR 462, IQ2_K_R4, IQ3_K_R4, IQ4_K_R4, IQ5_K_R4 PR 461, IQ4_K, IQ5_K, IQ6_K PR 417, IQ2_KS, IQ2_K, IQ3_K PR 418

Quantization improvements

IQ1_M PR 327, IQ2_XS PR 312, Q2_K, Q4_K, Q5_K, Q4_1, Q5_1 PR 302, Q4_0, Q5_0, Q6_0, Q3_K, Q6_K, IQ4_XS, IQ4_NL PR 295

Quantization performance improvements

• Faster CPU prompt processing for Trellis quants and MoE models. PR 488
• Trellis quants: faster CPU prompt processing PR 482.
• Minor (~2%) iq2_ks TG performance improvement on CUDA PR 468
• Faster IQ3_KT and IQ4_KT PR 453
• Zen4: Faster PP for IQ2_KS, IQ4_KS, IQ5_KS PR 428
• Fast GEMM/GEMV for IQ1_S PR 212

Features

• Legacy quants conversion schemes in convert_hf_to_gguf.py PR 449, Q6_0 in PR 483
• June 8 2025: Webui updated (legacy still available when --path ./examples/server/public_legacy is passed) PR 481
• June 8 2025: RPC improvements PR 480
• June 7 2025: Add an endpoint that lists all the saved prompt caches to server PR 502
• June 6 2025: Make prompt cache saving and restoring MLA aware PR 497
• June 3 2025: Added samplers, XTC PR 486, top-n σ PR 489.
• May 22 2025: Refactor iqk_mul_mat.cpp which speeds up compilation time significantly. PR 435
• May 17 2025: Option to enable or disable the CPU FA kernels PR 429.
• 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.cpp GGUFs for DeepSeek models with MLA enabled were resolved in PR 394. The lower prompt processing performance resulting from using llama.cpp-style MLA GGUFs was recovered in PR 409.
• April 21 2025: ik_llama.cpp builds and runs successfully on Android (using termux), see PR 336
• March 1 2025: Smart Expert Reduction for faster DeepSeek inference PR 239
• Feb 25 2025: Tensor overrides for better control where model weights are stored (GPU or CPU) PR 232
• Feb 23 2025: sweep-bench - better performance benchmarking PR 225
• Feb 19 2025: Q8_KV - new type for 8-bit KV-cache quantization PR 208
• March 7 2025: Custom quantization mixes using regular expressions PR 244

Performance improvements

• May 13 2025: Better CPU FA performance for DeepSeek-Lite. PR 410
• May 11 2025: Slightly faster flash attention for DeepSeek models on CUDA, along with extending compatibility to Touring or newer GPUs. PR 408
• May 4 2025: Significant token generation performance improvement on CUDA with Flash Attention for GQA models. For details and benchmarks. PR 370
• April 17 2025: Better CPU Flash Attention token generation performance. PR 332
• April 3 2025: Much faster MoE implementation on Metal. PR 307
• March 25 2025: Better MoE performance on CUDA PR 283
• March 23 2025: Better batched processing speed for DeepSeek models PR 282
• March 18 2025: Reduce compute buffer size PR 237
• March 10 2025: Better TG performance for MoE models on CUDA PR 248
• Feb 23 2025: Fused FFN ops for faster MoE inference PR 229

Flash-MLA

• May 7 2025: 🚀 FlashMLA-3 for DeepSeek models on CUDA. PR 386. Caveat: Ampere or newer Nvidia GPU required
• March 21 2025: 🚀 FlashMLA-3: fastest CPU-only inference for DeepSeek models PR 273
• March 17 2025: 🚀 FlashMLA-2 performance improvements PR 253
• March 12 2025: Allow Q8_0 KV cache with FlashMLA-2 on CUDA PR 265
• March 9 2025: 🚀 FlashMLA on CUDA PR 247
• March 8 2025: 🚀 Faster FlashMLA CPU implementation PR 243
• March 3 2025: 🚀 Introducing FlashMLA - MLA with Flash Attention PR 240
• Feb 27 2025: MLA without transposed cache PR 235
• Feb 13 2025: Allow Q8_0 quantized cache with MLA PR 206
• Feb 11 2025: 🚀 Flash Attention support for DeepSeek models PR 200
• Feb 9 2025: 🚀 MLA for DeepSeek models PR 188

Fixes

• Fix bug in MMVQ kernel PR 446
• Fix AVX2 implementation of IQ4_K, IQ4_KS, IQ5_K, IQ6_K PR 427
• Fix standard attention on the CPU PR 421
• Fix imatrix calculation for MLA models PR 411
• Fix new CUDA FA on Touring PR 413
• Fix SER. CPU: PR 415 CUDA: PR 416

Resources

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

Contributing

Contributions in form of pull requests, issue submissions (bug reports, feature requests), or general discussions, are welcome.

License

MIT