aefb8bdd99
* ggml: ggml_dequant_hadamard fused op for MLA -khad path
Adds a new ggml op that fuses (ggml_cast -> F32) + (ggml_hadamard) into a
single kernel. Reads a quantized (or F16/F32) source and produces a per-
Hadamard-block F32 chunk with the inverse transform applied, without
materializing a full-size F32 intermediate buffer.
Motivation: the MLA pp_opt path in build_deepseek2.cpp un-encodes the
H-applied cache_nope view at every PP call. Today that runs as a cast
(quant -> F32) followed by a separate ggml_hadamard kernel, costing two
full-size F32 passes per layer per rank per call. Fusing them halves
the bandwidth on the un-encode and removes one kernel launch.
CUDA kernels in dequant_hadamard.cu lift the Walsh-Hadamard butterfly
from hadamard.cu and dequant helpers from dequantize.cuh:
* qr=1 layout (q8_0): consecutive dequant pair, stage 1 fused with load
* qr=2 layout (q4_0 / q4_1 / q5_0 / q5_1 / q6_0 / iq4_nl): dequant pair
at stride qk/2, explicit stage 1 after sync
* F16 has a dedicated kernel
* F32 source falls back to the standalone Hadamard op
CPU impl in iqk_cpu_ops.cpp composes the existing type_traits.to_float
dequant with fast_ht for graph completeness. nh in {64, 128, 256, 512}.
* MLA-TP: Hadamard pretransform of wv_b/wk_b_pp for -khad
Fold the 64-block orthonormal Hadamard into wv_b and wk_b_pp once at
context init so the pp_opt mul_mats consume the K cache in its on-disk
encoded basis. The per-PP-call cache_nope un-Hadamard is then skipped
(rope half still un-applied — it goes to FA via concat, no wk_b multiply).
Math is identity by H^T H = I: mul_mat(H@wv_b, H@cache) = wv_b^T @ cache.
For mla=2/3 absorb, composes correctly with the existing post-FA
ggml_hadamard(kqv_compressed, 64).
All-or-nothing across layers under a castable type-allowlist (excludes
1-3 bpw IQ types whose requant blows up beyond PPL noise). Models with
ineligible weights fall back to the runtime un-Hadamard path unchanged.
Composes with the fused ggml_dequant_hadamard op (prior commit): with the
fold active only the rope half still runs the runtime transform, via the
fused kernel.
* MLA-TP: fix TG with -khad after wv_b/wk_b_pp fold
The absorb branch of build_deepseek2_tp_attention applies
ggml_hadamard to kqv_compressed after FA, then multiplies by
wv_b. Pre-fold this was needed because wv_b was un-encoded; with
the wv_b fold (prior commit) the mul_mat already expects
H-encoded kqv_compressed:
mul_mat(H @ wv_b, kqv_encoded) = wv_b^T @ H @ H @ kqv_unencoded
= wv_b^T @ kqv_unencoded (H @ H = I)
Skip the post-FA hadamard when model.khad_pretransformed is set
so the two H applications cancel instead of double-applying.
Affects the absorb branch: TG (n_tokens=1), short-context PP
(n_kv < 1024), and models without wk_b_pp. Long-context PP goes
through the pp_opt branch and is unrelated/unchanged.
Reported by @ikawrakow on PR 1852. Verified across mla={1,2,3} x
khad={on,off} x -ctk={q8_0,q4_0} on GLM-4.7-Flash IQ5_K and the
unsloth IQ4_XS variant ik used to reproduce.
* ggml_hadamard: accept F16 and quant sources; drop GGML_OP_DEQUANT_HADAMARD
Per @ikawrakow review on PR 1852: subsume the per-source-type dispatch
into the existing GGML_OP_HADAMARD instead of carrying a separate enum
entry, op constructor, and standalone files.
ggml_hadamard's API is unchanged from the call-site perspective. The
constructor's F32-only assertion is dropped; ggml_cuda_op_hadamard and
iqk_hadamard now dispatch internally:
- F32 source: existing F32 butterfly (unchanged)
- F16 source: dedicated kernel
- q8_0 / q4_0 / q4_1 / q5_0 / q5_1 / q6_0 / iq4_nl: fused dequant +
butterfly kernel (lifted from the deleted dequant_hadamard.cu)
- CPU side composes traits.to_float with fast_ht
Net diff: -80 lines. Removes dequant_hadamard.{cu,cuh}, the enum entry,
op table rows, ggml_dequant_hadamard constructor, dispatch cases, and
the DEQUANT_HADAMARD supports_op block.
Verified clean build + TG smoke (mla=3 +khad q8 on GLM-4.7-Flash-IQ4_XS,
same coherent output as prior commit on feat/dequant-hadamard).
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.
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
-rtroption 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 much 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 (
AVX2or better,ARM_NEONor better) and CUDA (Turing or newer). Please do not enter issues related to ROCm, Vulkan, Metal, old Nvidia GPUs,AVXCPUs, 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 inllama.cpp.
Important
Do not use quantized models from Unsloth that have
_XLin their name. These are likely to not work withik_llama.cpp.The above has caused some stir, so to clarify: the Unsloth
_XLmodels that are likely to not work are those that containf16tensors (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-moeor--n-cpu-moeor tensor overrides). If you are using/want to use split mode graph and observe gibberish/incoherent responses, try adding-cuda graphs=0to 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 start chatting.
Step by step guide for ik_llama.cpp in podman/docker container including llama-swap
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, Mimo-2.5 PR 1723
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
Hadamard transforms for V-cache
MXFP4 as used in gpt-oss models
Implemented for Zen4, AVX2, ARM_NEON, Metal, CUDA PR 682
Quantization improvements
IQ1_MPR 327,IQ2_XSPR 312,Q2_K, Q4_K, Q5_K, Q4_1, Q5_1PR 302,Q4_0, Q5_0, Q6_0, Q3_K, Q6_K, IQ4_XS, IQ4_NLPR 295- Low perplexity
Q4_0KV cache PR 1547 PR 1556
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_ksTG performance improvement on CUDA PR 468 - Faster
IQ3_KTandIQ4_KTPR 453 - Zen4: Faster PP for
IQ2_KS, IQ4_KS, IQ5_KSPR 428 - Fast GEMM/GEMV for
IQ1_SPR 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
- 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
- Self speculative decoding, ngram PR 1261, suffix PR 1646
- String ban function for all completions PR 1185 PR 1243
- Expiring Logit Bias PR 1731
- OpenAI
/v1/responsesAPI endpoint PR 1184 - Function call support PR 628
- jinja template support PR 677
- Webui: New Features for Conversations, Settings, and Chat Messages PR 618
- Dynamic control vector management endpoints PR 1223
- Legacy quants conversion schemes in
convert_hf_to_gguf.pyPR 449,Q6_0in PR 483 - Adaptive-P Sampler PR 1100 implemented as designed by it's author; supported on Webui
- Multi-modal Vision support in
llama-mtmd-cliPR 798 and inllama-serverPR 901 - mikupad as an alternative WebUI PR 558
- June 8 2025: Webui updated (legacy still available when
--path ./examples/server/public_legacyis 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.cppwhich 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.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. - 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_0KV 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_0quantized 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_KPR 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
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:
- GPT-3
- GPT-3.5 / InstructGPT / ChatGPT:
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