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

TL;DR

This repository started as a fork of llama.cpp in June of 2024 and was last synced with upstream in August of 2024. Compared to mainline llama.cpp, it offers additional SOTA quantization types and, in many cases, better performance. Various features related to LLM inference appeared here first before becoming available in llama.cpp. MLA, quant repacking, fused delta-net (known in `llama.cpp as "Gated Delta Net" - GDN), tensor parallel, MTP, DFlash, to just name a few.

[!IMPORTANT] If you are running hybrid CPU/GPU inference for MoE models with all or some experts left on the CPU, do not use -rtr unless you know what you are doing. The -rtr option causes all tensors left in RAM to be repacked to row-interleaved format while loading the model. As not all quantization types have a CUDA implementation, this will result in matrix multiplications with these tensors to be always done on the CPU, even when it would have been much better to offload the computation to the GPU, typically resulting in lower prompt processing speed. Most notably, k-quants (K2_K, Q3_K, Q4_K, Q5_K, Q6_K) do not have CUDA row-interleaved implementation.

[!NOTE] The only fully functional and performant compute backends are CPU (AVX2 or better, ARM_NEON or better) and CUDA (Turing or newer). Please do not enter issues related to ROCm, Vulkan, Metal, old Nvidia GPUs, AVX CPUs, etc. They will not get resolved unless you roll up your sleeves and help bring your favorite backend up to speed. With the current regular contributors this project simply does not have the bandwidth to work on all backends available in llama.cpp.

[!IMPORTANT] Do not use quantized models from Unsloth that have _XL in their name. These are likely to not work with ik_llama.cpp.

The above has caused some stir, so to clarify: the Unsloth _XL models that are likely to not work are those that contain f16 tensors (which is never a good idea in the first place). All others are fine.

[!NOTE] Some users have reported issues with graph parallel (a.k.a. split mode graph) and partial GPU offload (using --cpu-moe or --n-cpu-moe or tensor overrides). If you are using/want to use split mode graph and observe gibberish/incoherent responses, try adding -cuda graphs=0 to your command line.

Quickstart

Prerequisites

git clone https://github.com/ikawrakow/ik_llama.cpp

cd ik_llama.cpp

On Debian/Ubuntu Linux, install the required packages (if using another Linux distro, you need to find the corresponding packages and adapt):

apt-get update && apt-get install build-essential git libcurl4-openssl-dev curl libgomp1 cmake

Build for CPU

cmake -B build -DGGML_NATIVE=ON

cmake --build build --config Release -j$(nproc)

For AVX-512-capable CPUs (AMD Zen4 / Intel Sapphire Rapids+), see docs/build.md section "CPU build flags for AVX-512" for the additional flags that activate the IQK quantized GEMM kernels (the HAVE_FANCY_SIMD path). Without those flags, a vanilla Release build silently falls back to the AVX2 path on this hardware.

Build for GPU

Install Nvidia Drivers and CUDA Toolkit.

cmake -B build -DGGML_NATIVE=ON -DGGML_CUDA=ON

cmake --build build --config Release -j$(nproc)

Step-by-step instructions for a case of a successful Windows build

https://github.com/ikawrakow/ik_llama.cpp/blob/main/docs/build.md

Run

Download .gguf model files (e.g. bartowski/Qwen_Qwen3-0.6B-IQ4_NL.gguf) to your favorite directory (e.g. /my_local_files/gguf).

Start the server with one of the commands (CPU or GPU):

./build/bin/llama-server --model /my_local_files/gguf/Qwen_Qwen3-0.6B-IQ4_NL.gguf --ctx-size 4096

./build/bin/llama-server --model /my_local_files/gguf/Qwen_Qwen3-0.6B-IQ4_NL.gguf --ctx-size 4096 -ngl 999

That's all! Open http://127.0.0.1:8080 in Browser and start chatting, or use the available API endpoins in your program/harness.

Run in Docker or Podman

Pull one of the available images from ghcr.io. View all tags

docker pull ghcr.io/ikawrakow/ik-llama-cpp:cpu-swap
docker pull ghcr.io/ikawrakow/ik-llama-cpp:cpu-server
docker pull ghcr.io/ikawrakow/ik-llama-cpp:cpu-full

docker pull ghcr.io/ikawrakow/ik-llama-cpp:cu12-swap
docker pull ghcr.io/ikawrakow/ik-llama-cpp:cu12-server
docker pull ghcr.io/ikawrakow/ik-llama-cpp:cu12-full

Check Step by step guide for image customization and other details.

Common parameters and options

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, Kimi-2 PR 609, dots.llm1 PR 573, Hunyuan PR 565, GLM-4.5 PR 668 (4.5/4.6/4.7/AIR), Ernie 4.5 MOE and 0.3B PR 759, grok-2 PR 782, Ling/Ring (Bailing-MoE2) PR 833, Qwen3-VL PR 883, SmolLM3 PR 934, GigaChat3 PR 995, ministral3 PR 1030, Mimo-V2-Flash PR 1096, GLM-4.7-Flash PR 1168, Seed-OSS PR 1218, Step-3.5-Flash PR 1231, GLM-5 PR 1268, Qwen3-Next PR 1266, Qwen3.5-MoE PR 1288 and dense Qwen-3.5 1326, Mistral 4 PR 1450, Bonsai 1-bit PR 1570, Gemma4 PR 1581 including assistant, Mimo-2.5 PR 1723, JetBrains Mellum2 PR 1919, Poolside Laguna XS.2 PR 1911, Cohere2-MoE North Mini Code PR 1945, MiniMax-M3 PR 1963, Laguna M.1 PR 2003, OpenPangu #2065, DeepSeek-V4 PR 2165, Muse-Glimmer PR 2293, DSpark PR 2304, Qwen-3.8-Flash-Next PR 2365

Quantization

Quantization additions

Trellis quants (IQ1_KT, 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. IQ1_KT was added more recently in PR 616. Note: these are base on a novel, integer-base trellis, which allows to achieve reasonable CPU performance, see PR 529 and PRs quoted there for details.

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

IQ2_KL is a more recent addition in PR 602

Hadamard transforms for K-cache

CPU PR 1033 and CUDA PR 1034

Hadamard transforms for V-cache

PR 1527

MXFP4 as used in gpt-oss models

Implemented for Zen4, AVX2, ARM_NEON, Metal, CUDA PR 682

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
• Low perplexity Q4_0 KV cache PR 1547 PR 1556
• MTP: option to use re-quantized output tensor --mtp-requantize-output-tensor new_type PR 1809

Quantization performance improvements

• Much faster CPU prompt processing for all non-interleaved quants. Initial idea in PR 515 and PR 531, with many follow up PRs to apply to all quantization types for the 3 supported CPU platforms.
• All quantization types now have quantized matrix multiplication CUDA kernels, see PR 557 and several others
• 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
• AVX-VNNI optimizations PR 1446 PR 1455 PR 1467 PR 1474 PR 1482

Features

• New split mode "graph" for multi GPU setups PR 1022
• Fused delta-net for Qwen3-Next and Qwen3.5-MoE PR 1315 PR 1333 PR 1362 PR 1373
• Hadamard transforms for K-cache and V-cache PR 1033 PR 1034 PR 1527
• Auto-fit offloaded tensors to available VRAM (MoE and dense models) PR 1501 PR 1504, allows per GPU fit margin PR 1872
• Checkpoints for recurrent models PR 1310 PR 1398
• MTP decoding support for popular models like GLM-4.x MoE 1270, Qwen 3.5/3.6 1698 1745, Gemma 4 1744, GLM 5 1890, Step 3.7 2250
• Self speculative decoding, ngram PR 1261, suffix PR 1646
• DFlash initial support PR 1970
• DSpark initial support PR 2280
• GLM-DSA architecture indexer cache
• GLM-5.2 vision hack PR 2283
• String ban function for all completions PR 1185 PR 1243
• Expiring Logit Bias PR 1731
• OpenAI /v1/responses API endpoint PR 1184
• Function call support PR 628
• jinja template support PR 677
• Webui: New Features for Conversations, Settings, and Chat Messages PR 618, MCP PR 1904
• On-demand tensor reload PR 1989
• Dynamic control vector management endpoints PR 1223
• Legacy quants conversion schemes in convert_hf_to_gguf.py PR 449, Q6_0 in PR 483
• Adaptive-P Sampler PR 1100 implemented as designed by it's author; supported on Webui
• Multi-modal Vision support in llama-mtmd-cli PR 798 and in llama-server PR 901
• mikupad as an alternative WebUI PR 558
• 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

• Better GPU offload strategy for MoE models when using hybrid HPU/CPU inference, see PR 520
• Much faster rng sampling PR 1187
• 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

Testing

Function Calls Tests

To run the function calls test suite:

cd build
cmake --build . --target test-function-calls
./bin/test-function-calls

The test suite covers parser functionality, streaming, error handling, content cleaning, and server integration. All tests should pass to ensure production readiness.

Contributing

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

License

• subprocess.h - Single-header process launching solution for C and C++ - Public domain
• server
• GBNF grammars

Development documentation

• How to build
• Running on Docker
• Performance troubleshooting
• GGML tips & tricks

Seminal papers and background on the models

If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:

• LLaMA:
  • Introducing LLaMA: A foundational, 65-billion-parameter large language model
  • LLaMA: Open and Efficient Foundation Language Models
• GPT-3
  • Language Models are Few-Shot Learners
• GPT-3.5 / InstructGPT / ChatGPT:
  • Aligning language models to follow instructions
  • Training language models to follow instructions with human feedback

Completions

Command-line completion is available for some environments.

Bash Completion

$ build/bin/llama-cli --completion-bash > ~/.llama-completion.bash
$ source ~/.llama-completion.bash

Optionally this can be added to your .bashrc or .bash_profile to load it automatically. For example:

$ echo "source ~/.llama-completion.bash" >> ~/.bashrc

Dependencies

• yhirose/cpp-httplib - Single-header HTTP server, used by llama-server - MIT license
• stb-image - Single-header image format decoder, used by multimodal subsystem - Public domain
• nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
• miniaudio.h - Single-header audio format decoder, used by multimodal subsystem - Public domain
• subprocess.h - Single-header process launching solution for C and C++ - Public domain