ik_llama.cpp: when regular llama.cpp just isn't enough

Familiar situation: you run a language model on your computer, and it barely works? Especially if you don't have a top-tier GPU, just a regular processor. That's exactly why ik_llama.cpp was created — a fork of the popular llama.cpp focused on maximum performance for CPU and hybrid CPU/GPU configurations.

What it is and why you need it

ik_llama.cpp is a fork of the llama.cpp project, created by Ivan Kavrakov (ikawrakow). The main goal is to provide a more performant solution for running language models on regular hardware. If the original llama.cpp is already good, this fork makes it even better, especially in scenarios:

Running on CPU (including mobile devices)
Using hybrid CPU/GPU configurations
Applying modern quantization methods

The project is actively developed: at the time of writing, it has 10,098 stars and 125 forks on GitHub.

Key features

1. Advanced quantization methods

The project implements several new quantization types that significantly reduce model size with minimal quality loss:

Trellis quants (IQ1_KT, IQ2_KT, IQ3_KT, IQ4_KT) — based on a new integer trellis, providing reasonable CPU performance
IQK quants — a whole family of quantization methods, including IQ5_KS, IQ4_KS, IQ6_K and others
Q8_KV — a new type for 8-bit KV-cache quantization

These methods allow running models that previously required GPU on regular processors.

2. Flash-MLA for DeepSeek models

Particularly interesting is the FlashMLA implementation (MLA — Multi-Layer Attention) for DeepSeek models:

FlashMLA-3 — the fastest implementation for CPU
CUDA support for Nvidia GPUs (Ampere or newer)
Ability to use Q8_0 quantized cache with MLA

As the author notes, FlashMLA-3 delivers record-breaking performance for DeepSeek models on CPU.

3. Hybrid CPU/GPU processing

The project offers fine-grained control over where operations are executed:

Tensor overrides for managing weight placement (GPU or CPU)
Improved offload strategy for MoE (Mixture of Experts) models
Ability to disable CPU FA (Flash Attention) kernels when needed

This is especially useful for systems with discrete GPUs, where you can distribute the load between the processor and graphics card.

Technical details

The project is written in C++ and supports:

Various CPU architectures: AVX2, NEON, Zen4
CUDA for GPU computing
Metal for Apple Silicon
Even runs on Android via Termux

Interesting technical solutions:

Fused MoE operations — accelerated inference for models with Mixture of Experts architecture
Row-interleaved quant packing — efficient packing of quantized data
Smart Expert Reduction — intelligent expert reduction for faster DeepSeek inference

Practical applications

Where ik_llama.cpp is particularly useful:

Local running of large models — when you don't have access to powerful GPUs but need to work with modern LLMs
Mobile devices — ability to run on Android via Termux
Hybrid systems — optimal use of both CPU and GPU in one system
Experiments with quantization — many new quantization methods for researchers

For example, as noted in one discussion, the project allows efficient operation with DeepSeek-V3 even on a configuration with 16 x Nvidia RTX 3090.

Getting started

Clone the repository:

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

Build the project (example for Linux):

mkdir build
cd build
cmake ..
make -j

For testing function calling functionality:

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

Conclusion: is it worth trying?

ik_llama.cpp is an excellent choice if:

You need maximum performance on CPU
You work with modern models like DeepSeek, LLaMA-3, Qwen3
You want to experiment with advanced quantization methods
You have a hybrid system with CPU and GPU

The project is actively developed, has an MIT license, and is open to contributors. If you already use llama.cpp, switching to this fork can give you a noticeable performance boost without additional costs.

For a more detailed overview of the project's capabilities, I recommend exploring:

Project Wiki with performance comparisons
Discussion of new quantization types
DeepSeek models guide