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llama.cpp discussion
llama.cpp reviews and mentions
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How to Setup a Local Coding Agent on macOS
> The benchmark prompt was:
> Write a compact Python function that parses a unified diff and returns the changed file paths. Then explain two edge cases.
> Each benchmark generated about 128 tokens.
Generating 128 tokens is probably not enough for good benchmark results. MTP speedup depends on how often the predicted tokens are accepted. In my experience, the very early output has a higher acceptance rate, so short testing can give false positive speedups.
Also llama.cpp includes a tool specifically for benchmarking:
https://github.com/ggml-org/llama.cpp/blob/master/tools/llam...
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Doubling Qwen3.6-27B on One RTX 3090: ollama llama.cpp + MTP, Lever by Lever (35.7 80.2 tok/s)
In my build, MTP came from mainline llama.cpp, not ik_llama. ik_llama got me to ~47 (engine + quant), but I couldn't get MTP running there — my build rejected the -mtp flags and ignored the model's nextn tensors. Mainline llama.cpp added MTP fairly recently (PR #22673, merged 2026-05-16), and that's where it worked for me. (There may well be an ik_llama path I missed — this is just what got it going on my box.)
- New `llama.cpp` Updates, AI Agents for Any LLM, and Quantized Vector Index for Local Inference
- Gemma 4 QAT models: Optimizing compression for mobile and laptop efficiency
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Introducing LlamaStash: a zero-overhead, terminal-native llama.cpp launcher
That script grew up. Today I'm releasing LlamaStash, the first public release of a fast, cross-platform, terminal-native launcher for llama.cpp with zero overhead.
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How fast is LlamaStash? Overhead, throughput, and a fair comparison with Ollama and LM Studio
LlamaStash spawns the unmodified upstream llama-server. So three different questions follow from that, and there is a benchmark suite for each.
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A 10 year old Xeon is all you need (for 26B-A4B MTP Drafters without GPU)
llama.cpp includes a benchmarking tool called llama-bench https://github.com/ggml-org/llama.cpp/blob/master/tools/llam...
ik_llama includes llama-sweep-bench https://github.com/ikawrakow/ik_llama.cpp/blob/main/examples...
When comparing hardware, the output of these tools is very helpful to let others put it into context. The post says the output is "reading speed" but knowing the prefill and token generation speeds would be a lot more helpful.
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Racket v9.2 is now available
lol the same way we implement all of the reduced precision fp8, fp4 types today: by storing them in the corresponding uint:
https://github.com/ggml-org/llama.cpp/discussions/15095
- Run Gemma-4 E2B-it with llama.cpp on Raspberry Pi4
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Gemma 4 dense by default: why your local agent doesn't want the MoE
# Build llama.cpp with Metal backend git clone https://github.com/ggml-org/llama.cpp cd llama.cpp && cmake -B build -DGGML_METAL=ON && cmake --build build -j # Community-quantized GGUFs (Google ships safetensors; unsloth ships GGUF) huggingface-cli download unsloth/gemma-4-31B-it-GGUF \ gemma-4-31B-it-Q4_K_M.gguf --local-dir . huggingface-cli download unsloth/gemma-4-26B-A4B-it-GGUF \ gemma-4-26B-A4B-it-Q4_K_M.gguf --local-dir . # Benchmark: 200 generations of 512 tokens, log per-call timing ./build/bin/llama-bench -m gemma-4-31B-it-Q4_K_M.gguf -n 512 -r 200 -o json > dense.json ./build/bin/llama-bench -m gemma-4-26B-A4B-it-Q4_K_M.gguf -n 512 -r 200 -o json > moe.json
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A note from our sponsor - SaaSHub
www.saashub.com | 14 Jun 2026
Stats
ggml-org/llama.cpp is an open source project licensed under MIT License which is an OSI approved license.
llama.cpp is marked as "self-hosted". This means that it can be used as a standalone application on its own.
The primary programming language of llama.cpp is C++.