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buun-llama-cpp

buunslamma

This is a highly experimental fork of llama.cpp. Use at your own discretion.

A research and development fork of llama.cpp, providing unique KV cache codecs, inference techniques, and bleeding edge features.

VBR — Variable Bit-Rate KV Cache

Why pay 3-bit or 4-bit quality for a context length you only sometimes reach? VBR quantizes the KV cache dynamically as your session grows — giving you the highest-quality cache possible at any given depth. It quantizes the cache layer by layer, using our best KV codecs, from the least-sensitive layers to the most-sensitive, and only as far down the ladder as your VRAM and context actually require.

The cache starts at FP16 and stays there until there is real budget pressure; then it degrades one (layer, side) tensor at a time — first to an aggregate 15.75 bits/value, then 15.51, and so on down the ladder (f16 → turbo8 → turbo4 → turbo3_tcq → turbo2_tcq → turbo1_tcq), following a per-model price order measured on KLD panels. For Qwen35 (16 attention layers) that is 160 distinct steps from FP16 down to turbo1_tcq; for Gemma4-31B (60 layers), 600 — the finest-grained quality control we can give you at every moment of a session.

And because VBR always draws from the best codecs on the ladder, you never have to track KV formats again: new codecs and research roll straight into VBR, so -ct vbr will always hand you the best possible cache.

Recommended usage

On a dedicated GPU, just run:

llama-server -m model.gguf -ct vbr

That single flag is the whole product: it derives a KV VRAM budget from whatever is left after weights and compute, advertises the largest context that fits at the floor tier (capped at the model's training length), and degrades tiers on the fly as context fills. No context length to guess, no codec to pick. Run with -v to watch the VBR degrade #… steps fire.

Settings

flag meaning
-ct vbr (or -ctk vbr / -ctv vbr) Enable VBR. A one-sided selection implies VBR on the other side too; explicitly pinning a side (-ctv q8_0) holds it at fixed bits and never degrades it.
-c <N> Cap the context at N tokens; VBR then spends your whole VRAM budget running that window at the highest quality it can, instead of advertising the max floor-tier capacity. E.g. -c 30000 = the best-quality cache that fits a 30k window.
--vbr-vram <SIZE> Explicit KV VRAM budget (e.g. 8G). Default auto = whatever VRAM is left after weights and compute.
--vbr-floor <bits|tier> Literal aggregate bits/value floor for dynamic mode (default t1 = 1.25). Degrades stop at the last step still ≥ the floor.
--vbr-budget <tier|number> Default dynamic (runtime controller). A tier (t8/t4/t3/t2/t1) or a number instead selects a fixed static tier — no runtime degrades.

Requirements: a CUDA or ROCm backend (turbo-typed KV needs the TurboQuant interface; layers whose KV lands on the CPU fall back to q8_0). Flash attention is required and force-enabled. Dynamic mode uses unified KV (forced automatically with -np > 1). Context-shift / self-extend and slot/session save-restore are disabled in dynamic mode (they would snapshot tier-typed KV that can't restore across a degrade — tier-aware save-restore is planned); context checkpoints stay enabled on hybrid models. Generation stops cleanly when the context fills. Models without a baked price order use a generic cross-model order.

TCQ (trellis-coded KV cache)

Standard KV cache quantization treats each value independently. TCQ constrains the quantization indices to follow a 512-state trellis, giving a much larger effective codebook at the same bit rate (with FWHT rotation and context-adaptive norm scaling on top). At the 3-bit setting, the trellis cuts median KL-divergence by ~40% versus scalar quantization while using slightly fewer bits (3.25 vs 3.50 bpv), and lands perplexity on par with an f16 KV cache.

Paper: Closing the Gap: Trellis-Coded Quantization for KV Cache at 2-3 Bits

Quality (Qwen3.6-27B Q6_K, 16K, RTX 3090)

turbo3 is scalar 3-bit, turbo3_tcq the trellis-coded version at fewer bits — the gap is exactly what the trellis buys. pp = prefill t/s on a 16K prompt, tg = decode t/s at 16K depth.

Codec bpv PPL median KLD pp (t/s) tg (t/s)
turbo3 (scalar) 3.5 5.730 0.00279 1015 28.1
turbo3_tcq 3.25 5.668 0.00163 818 26.6

The trellis cuts median KLD ~40% (and edges PPL below f16) at fewer bits. Its cost lands in prefill — the Viterbi re-encode drops prompt processing to ~818 t/s vs ~1015 for scalar turbo3; decode is barely affected on a dedicated GPU.

Speed

The trellis cost is compute, so it depends on hardware. On dedicated GPUs (e.g. RTX 3090) the fused tensor-core decode path keeps generation at essentially vanilla / f16 speed (the tg numbers above). On weaker-compute hardware (e.g. the Strix Halo iGPU) the cost is exposed and the TCQ types can be up to ~40% slower than their scalar counterparts — there, prefer the scalar or higher-bit codecs.

How it works

  1. FWHT rotation with random sign flips converts correlated KV vectors into i.i.d. Gaussian entries
  2. Viterbi encoding on a 512-state (3-bit) or 256-state (2-bit) right-shift trellis finds the globally optimal codeword assignment
  3. O(1) sliding-window decode -- each value decodes via a bit window lookup, no trellis traversal at inference
  4. Context-adaptive alpha -- logarithmic norm scaling formula automatically adjusts dequantization scale per context length

Custom codebooks

Trained codebooks are included in codebooks/. The defaults are compiled into the CUDA kernels, but you can override them:

TURBO_TCQ_CB=codebooks/3bit/product_aware_iter080.bin \
TURBO_TCQ_CB2=codebooks/2bit/product_aware_iter090.bin \
./build/bin/llama-server -m model.gguf -ngl 99 -fa \
  -ctk turbo3_tcq -ctv turbo3_tcq

Codebook training scripts are in scripts/tcq_train_*.py.

Codecs

These are the individual KV cache codecs the fork ships. In practice you rarely pick one by hand — dynamic VBR mixes them per layer automatically and is the recommended default; the codecs below are mainly for testing and comparison.

Measured on Qwen3.6-27B Q6_K at 16K context (18 chunks of the wikitext-2 test set) on an RTX 3090. Median KL-divergence is versus an f16 KV cache (0 = identical logits); pp is prefill throughput on a 16K prompt and tg is decode throughput at 16K depth. Ordered by KL-divergence (quality) — lower is better.

Codec bpv PPL median KLD pp (t/s) tg (t/s)
f16 16.0 5.683 0 (ref) 1104 30.7
q8_0 8.5 5.698 0.00020 1050 27.4
turbo8 8.125 5.693 0.00020 1013 28.6
turbo4 4.125 5.723 0.00090 1017 28.4
q4_0 4.5 5.705 0.00115 1033 26.9
turbo3_tcq 3.25 5.668 0.00163 818 26.6
turbo3 3.5 5.730 0.00279 1015 28.1
turbo2_tcq 2.25 5.711 0.00561 985 28.5
turbo2 2.5 5.964 0.01083 1013 28.2
turbo1_tcq 1.25 6.012 0.02633 991 28.5

MTP + Vision (--mmproj-gpu-swap)

On VRAM-constrained GPUs, MTP speculative decoding and the vision encoder (mmproj) may not fit in VRAM simultaneously. For example, Qwen3.6-27B Q6_K + MTP uses ~22.6 GiB on a 24 GiB RTX 3090, leaving no room for mmproj's ~1.1 GiB GPU footprint.

--mmproj-gpu-swap solves this by keeping mmproj on CPU at startup, then temporarily swapping MTP out of VRAM when an image arrives, loading mmproj to GPU for fast encoding (~1-2s instead of 30-60s on CPU), and swapping back afterward. MTP has no persistent state, so the swap is lossless.

./build/bin/llama-server -m Qwen3.6-27B-Q6_K.gguf \
  --mmproj mmproj.gguf --spec-type draft-mtp \
  --mmproj-gpu-swap -ngl 99

When combined with auto-fit (no -c flag), the server automatically sizes context to leave room for the swap. With a single slot, it also auto-enables --kv-unified to avoid splitting the KV cache into separate streams, which doubles usable per-slot context.

Build

NVIDIA (CUDA)

cmake -B build \
  -DGGML_CUDA=ON \
  -DGGML_NATIVE=ON \
  -DGGML_CUDA_FA=ON \
  -DGGML_CUDA_FA_ALL_QUANTS=ON \
  -DCMAKE_BUILD_TYPE=Release

cmake --build build -j$(nproc)

AMD (ROCm / HIP)

Tested on ROCm 7.2 + RDNA3 (gfx1100, RX 7900 XTX). Other RDNA3/RDNA4 targets should work by swapping AMDGPU_TARGETS.

cmake -B build \
  -DGGML_HIP=ON \
  -DAMDGPU_TARGETS=gfx1100 \
  -DCMAKE_BUILD_TYPE=Release \
  -DLLAMA_BUILD_SERVER=ON \
  -DCMAKE_C_COMPILER=/opt/rocm/bin/amdclang \
  -DCMAKE_CXX_COMPILER=/opt/rocm/bin/amdclang++

cmake --build build -j$(nproc)

For Windows ROCM

Test on ROCm 7.13 + AMD Radeon AI PRO R9700

Recommended configurations

For most use, just use VBR — it picks the best codec per layer automatically and spends all your spare VRAM on quality:

./build/bin/llama-server -m model.gguf -ngl 99 -ct vbr

The fixed-tier codecs below are for pinning a specific tier — benchmarking, a fixed budget, or a backend without VBR support.

turbo4 (4.125 bpv) -- low divergence, great compression

The safe fixed-tier default. 4-bit KV cache at ~3.8x compression with no speed penalty — higher fidelity (lower KL-divergence from FP16) than the 2–3 bit codecs below.

./build/bin/llama-server -m model.gguf -ngl 99 -fa \
  -ctk turbo4 -ctv turbo4

3-bit TCQ (3.25 bpv) -- best quality at 3-bit

The best-quality 3-bit option — about 40% lower KL-divergence than scalar turbo3 at the same tier. ~5x KV cache compression.

./build/bin/llama-server -m model.gguf -ngl 99 -fa \
  -ctk turbo3_tcq -ctv turbo3_tcq

2-bit TCQ (2.25 bpv) -- maximum compression

~7x KV cache compression. Best for fitting very long contexts in limited VRAM.

./build/bin/llama-server -m model.gguf -ngl 99 -fa \
  -ctk turbo2_tcq -ctv turbo2_tcq

Asymmetric 2.75 bpv -- best 2-bit quality

3-bit keys + 2-bit values. 15-17% lower KLD than the reverse, because adaptive alpha already compensates V quantization error.

./build/bin/llama-server -m model.gguf -ngl 99 -fa \
  -ctk turbo3_tcq -ctv turbo2_tcq

Scalar turbo3 / turbo2 (3.5 / 2.5 bpv) -- no trellis

Scalar quantization without TCQ. Faster encode, worse quality than TCQ equivalents.

# 3-bit scalar
./build/bin/llama-server -m model.gguf -ngl 99 -fa \
  -ctk turbo3 -ctv turbo3

# 2-bit scalar
./build/bin/llama-server -m model.gguf -ngl 99 -fa \
  -ctk turbo2 -ctv turbo2

Quick start

Getting started with llama.cpp is straightforward. Here are several ways to install it on your machine:

Once installed, you'll need a model to work with. Head to the Obtaining and quantizing models section to learn more.

Example command:

# Use a local model file
llama-cli -m my_model.gguf

# Or download and run a model directly from Hugging Face
llama-cli -hf ggml-org/gemma-3-1b-it-GGUF

# Launch OpenAI-compatible API server
llama-server -hf ggml-org/gemma-3-1b-it-GGUF

Description

The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud.

  • Plain C/C++ implementation without any dependencies
  • Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
  • AVX, AVX2, AVX512 and AMX support for x86 architectures
  • RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE support for RISC-V architectures
  • 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
  • Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA)
  • Vulkan and SYCL backend support
  • CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity

The llama.cpp project is the main playground for developing new features for the ggml library.

Models

Typically finetunes of the base models below are supported as well.

Instructions for adding support for new models: HOWTO-add-model.md

Text-only

Multimodal

Bindings
UIs

(to have a project listed here, it should clearly state that it depends on llama.cpp)

Tools
  • akx/ggify – download PyTorch models from Hugging Face Hub and convert them to GGML
  • akx/ollama-dl – download models from the Ollama library to be used directly with llama.cpp
  • crashr/gppm – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption
  • gpustack/gguf-parser - review/check the GGUF file and estimate the memory usage
  • Styled Lines (proprietary licensed, async wrapper of inference part for game development in Unity3d with pre-built Mobile and Web platform wrappers and a model example)
  • unslothai/unsloth – 🦥 exports/saves fine-tuned and trained models to GGUF (Apache-2.0)
Infrastructure
  • Paddler - Open-source LLMOps platform for hosting and scaling AI in your own infrastructure
  • GPUStack - Manage GPU clusters for running LLMs
  • llama_cpp_canister - llama.cpp as a smart contract on the Internet Computer, using WebAssembly
  • llama-swap - transparent proxy that adds automatic model switching with llama-server
  • Kalavai - Crowdsource end to end LLM deployment at any scale
  • llmaz - ☸️ Easy, advanced inference platform for large language models on Kubernetes.
  • LLMKube - Kubernetes operator for llama.cpp with multi-GPU and Apple Silicon Metal support"
Games
  • Lucy's Labyrinth - A simple maze game where agents controlled by an AI model will try to trick you.

Supported backends

Backend Target devices
Metal Apple Silicon
BLAS All
BLIS All
SYCL Intel GPU
OpenVINO [In Progress] Intel CPUs, GPUs, and NPUs
MUSA Moore Threads GPU
CUDA Nvidia GPU
HIP AMD GPU
ZenDNN AMD CPU
Vulkan GPU
CANN Ascend NPU
OpenCL Adreno GPU
IBM zDNN IBM Z & LinuxONE
WebGPU All
RPC All
Hexagon [In Progress] Snapdragon
VirtGPU VirtGPU APIR

Obtaining and quantizing models

The Hugging Face platform hosts a number of LLMs compatible with llama.cpp:

You can either manually download the GGUF file or directly use any llama.cpp-compatible models from Hugging Face or other model hosting sites, by using this CLI argument: -hf <user>/<model>[:quant]. For example:

llama-cli -hf ggml-org/gemma-3-1b-it-GGUF

By default, the CLI would download from Hugging Face, you can switch to other options with the environment variable MODEL_ENDPOINT. The MODEL_ENDPOINT must point to a Hugging Face compatible API endpoint.

After downloading a model, use the CLI tools to run it locally - see below.

llama.cpp requires the model to be stored in the GGUF file format. Models in other data formats can be converted to GGUF using the convert_*.py Python scripts in this repo.

The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with llama.cpp:

To learn more about model quantization, read this documentation

llama-cli

A CLI tool for accessing and experimenting with most of llama.cpp's functionality.

  • Run in conversation mode

    Models with a built-in chat template will automatically activate conversation mode. If this doesn't occur, you can manually enable it by adding -cnv and specifying a suitable chat template with --chat-template NAME

    llama-cli -m model.gguf

# > hi, who are you?
# Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today?
#
# > what is 1+1?
# Easy peasy! The answer to 1+1 is... 2!
  • Run in conversation mode with custom chat template 
    # use the "chatml" template (use -h to see the list of supported templates)
llama-cli -m model.gguf -cnv --chat-template chatml

# use a custom template
llama-cli -m model.gguf -cnv --in-prefix 'User: ' --reverse-prompt 'User:'
  • Constrain the output with a custom grammar 
    llama-cli -m model.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:'

# {"appointmentTime": "8pm", "appointmentDetails": "schedule a a call"}

    The grammars/ folder contains a handful of sample grammars. To write your own, check out the GBNF Guide.

    For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/

llama-server

A lightweight, OpenAI API compatible, HTTP server for serving LLMs.

  • Start a local HTTP server with default configuration on port 8080 
    llama-server -m model.gguf --port 8080

# Basic web UI can be accessed via browser: http://localhost:8080
# Chat completion endpoint: http://localhost:8080/v1/chat/completions
  • Support multiple-users and parallel decoding 
    # up to 4 concurrent requests, each with 4096 max context
llama-server -m model.gguf -c 16384 -np 4
  • Enable speculative decoding 
    # the draft.gguf model should be a small variant of the target model.gguf
llama-server -m model.gguf -md draft.gguf
  • Serve an embedding model 
    # use the /embedding endpoint
llama-server -m model.gguf --embedding --pooling cls -ub 8192
  • Serve a reranking model 
    # use the /reranking endpoint
llama-server -m model.gguf --reranking
  • Constrain all outputs with a grammar 
    # custom grammar
llama-server -m model.gguf --grammar-file grammar.gbnf

# JSON
llama-server -m model.gguf --grammar-file grammars/json.gbnf

llama-perplexity

A tool for measuring the perplexity 1 (and other quality metrics) of a model over a given text.

  • Measure the perplexity over a text file 
    llama-perplexity -m model.gguf -f file.txt

# [1]15.2701,[2]5.4007,[3]5.3073,[4]6.2965,[5]5.8940,[6]5.6096,[7]5.7942,[8]4.9297, ...
# Final estimate: PPL = 5.4007 +/- 0.67339
  • Measure KL divergence 
    # TODO

llama-bench

Benchmark the performance of the inference for various parameters.

  • Run default benchmark 
    llama-bench -m model.gguf

# Output:
# | model               |       size |     params | backend    | threads |          test |                  t/s |
# | ------------------- | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |
# | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         pp512 |      5765.41 ± 20.55 |
# | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         tg128 |        197.71 ± 0.81 |
#
# build: 3e0ba0e60 (4229)

llama-simple

A minimal example for implementing apps with llama.cpp. Useful for developers.

  • Basic text completion 
    llama-simple -m model.gguf

# Hello my name is Kaitlyn and I am a 16 year old girl. I am a junior in high school and I am currently taking a class called "The Art of

Contributing

  • Contributors can open PRs
  • Collaborators will be invited based on contributions
  • Maintainers can push to branches in the llama.cpp repo and merge PRs into the master branch
  • Any help with managing issues, PRs and projects is very appreciated!
  • See good first issues for tasks suitable for first contributions
  • Read the CONTRIBUTING.md for more information
  • Make sure to read this: Inference at the edge
  • A bit of backstory for those who are interested: Changelog podcast

Other documentation

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:

XCFramework

The XCFramework is a precompiled version of the library for iOS, visionOS, tvOS, and macOS. It can be used in Swift projects without the need to compile the library from source. For example:

// swift-tools-version: 5.10
// The swift-tools-version declares the minimum version of Swift required to build this package.

import PackageDescription

let package = Package(
    name: "MyLlamaPackage",
    targets: [
        .executableTarget(
            name: "MyLlamaPackage",
            dependencies: [
                "LlamaFramework"
            ]),
        .binaryTarget(
            name: "LlamaFramework",
            url: "https://github.com/ggml-org/llama.cpp/releases/download/b5046/llama-b5046-xcframework.zip",
            checksum: "c19be78b5f00d8d29a25da41042cb7afa094cbf6280a225abe614b03b20029ab"
        )
    ]
)

The above example is using an intermediate build b5046 of the library. This can be modified to use a different version by changing the URL and checksum.

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

Footnotes

  1. https://huggingface.co/docs/transformers/perplexity ↩

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LLAMA Turboquant implementation with CUDA support

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