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a719349982
* Remove most of split mode row
* WIP
* WIP: also allocate the KV cache using tensor split
* WIP: it runs with wrong result
But it also looks like the backend scheduler is not going to help:
* It copies mask and input positions to GPU 0
* => RoPE ops must run on GPU 0
* => To proceed attn evaluation, GPU 1 must wait for GPU 0 to finish its
entire attn calculation
* Same with FFN. The rms_norm gets scheduled on GPU 0. Hence, GPU 1 must
wait for GPU 0 to finish its entore FFN calculation before it can
start (as it needs to copy the result of rms_norm from GPU 0)
* => Seems useless without writing a bespoke TP scheduling
* WIP
* This works, but it is slow
* This is slightly better
the graph is still not being computed in parallel.
Why? Because the scheduler creates graph splits where the
result of the computation on one GPU becomes an input for the
other split. Hence, to trigger the computation on the second GPU
one needs to wait for the computation on the first GPU to finish,
even thiough the two can be done in parallel up to the sunchronization
point. So, all that is left to do is to trick the scheduler to create
to splits that can be done in parallel, and then have a graph split
where the results get combined.
* Playing games with the scheduler
This change tricks it into doing the right thing^TM.
Still quite a bit slower than split mode layer for the 8B LlaMA model.
But for the 70B LlaMA it now beats split mode layer for TG:
28 t/s vs 24.4 t/s. PP is 627 t/s vs 744 t/s.
In comparison, split mode "row" in mainline gets
484 t/s PP and 19.3 t/s TG.
* Fix attn split
Granularity for Wq, Wo is not just head size, but
head size * gqa_ratio.
Else the Wk, Wv tensors end up not being a multiple of the
head size when we divide the split determined by Wo with
the gqa_ratio.
* Show memory used per device
* Make it work with partial offload
but no tensor overrides yet, just ngl < num_layers.
* Allow for f16 source in fused_rms_norm
* This results in faster PP.
Now PP is faster than split mode layer for L3-70B.
* Rename split mode "row" to split mode "graph"
* Leave FFN partial results as f16
* WIP GLM4.5 - runs with wrong results
* WIP GLM4.5 - this works
PP is already better than split mode layer, but TG for zero context
is kind of low - 60 vs 92 t/s. TG becomes better than split mode layer
at around 20k tokens. PP at 26k tokens is 1.55X of sm layer.
* Work around compiler bug
It issues a warning that there is an extra semicolon outside of a function,
but there isn't. If I remove the anonymous namespace and turn the
functions inside into static, the warning disapears, so clearly
a compiler bug.
* Make graph reuse work with split mode graph
* Remove more split mode row remnants
* WIP tensor overrides
Runs with wrong results, don't see where the issue could be.
* This works but is slow
Still does not work for row-interleaved quants
* Slightly better
* Slightly better
* Row-interleaved quants work
* Better
* Minor
* Guarad against using split mode "graph" for unsupported models
* Guards against using merge_qkv with split mode "graph"
* WIP split mode attn
Works for LlaMA models, but not for GLM-4.5.
Doesn't seem to improve performance, so I guess no point in trying to
fix it.
* Split mode graph for qwen3moe
* Try to better distribute the splits
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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, Kimi-2 PR 609, dots.llm1 PR 573, Hunyuan PR 565
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
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
- 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
Features
- Function call support PR 628
- Webui: New Features for Conversations, Settings, and Chat Messages PR 618
- Legacy quants conversion schemes in
convert_hf_to_gguf.pyPR 449,Q6_0in PR 483 - 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
- 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
MIT