How to Setup a Local Coding Agent on macOS - Kyle Howells How to Setup a Local Coding Agent on macOS - Kyle Howells Running Gemma 4 26B-A4B and Qwen3.6 35B-A3B locally with llama.cpp, MTP speculative decoding, multimodal support, and PI as a coding agent. I'd had my internet fail a few times recently leaving me stranded without a coding agent, and so when I saw the "Gemma 4 now runs 2x faster with MTP" Multi-Token Prediction update for Gemma 4 I decided to have a go at getting it running. I wanted a local coding agent setup that: was fast enough to actually use on my Mac worked through an OpenAI compatible API (so I could use it in other tools) and preferably could handle screenshots/images when needed, so I can feed it screenshots of what it has made. And I did! This video is realtime. And shows the agent responding at a perfectly usable speed. After a bit of testing the final setup I ended up with is: llama.cpp built with Metal on macOS Gemma 4 26B-A4B in GGUF format A Q8 MTP draft model for speculative decoding The Gemma 4 multimodal projector Pi as the terminal coding agent This was tested on an Apple M1 Max with 64 GB unified memory, running macOS 15.7.7. The main model is: gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf. Link on Huggingface: models/unsloth-gemma-4-26B-A4B-it-GGUF/gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf That file is about 16 GB. With the MTP draft head and multimodal projector the model folder is about 17 GB. 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. Baseline: llama.cpp + Metal First I ran the main model directly through llama.cpp with Metal acceleration: repos/llama.cpp/build/bin/llama-cli \ Setup Gemma 4 26B-A4B Q4, llama.cpp Metal 58 tokens/second is not fast, but is usable, but for coding-agent work you want it to be as fast as possible, especially when the agent is making many tool calls. Adding the MTP Draft Model Gemma 4 now has the MTP draft model available: MTP/gemma-4-26B-A4B-it-Q8_0-MTP.gguf This can be loaded by llama.cpp as a speculative draft model: repos/llama.cpp/build/bin/llama-cli \ The first run with MTP came in at 69.2 tokens/second using 4 draft tokens. However, Unsloth's guide on How to Run MTP Models includes this note: "We found --spec-draft-n-max 2 is the best starting point however, do not assume 2 is optimal, as performance is hardware-dependent. Try any value from 1 through 6 and use whichever is fastest for your system." After sweeping --spec-draft-n-max, the best result was 72.2 tokens/second with 3 draft tokens. Setup Main model only Main model + Q8 MTP draft The useful part is that prompt processing stayed basically the same, while generation improved by about 24%. I tested --spec-draft-n-max values from 1 to 6. --spec-draft-n-max On my M1 Max machine, 3 was the fastest, with 2 close enough that either would be fine. Values above that got slower. I also tested MLX models through mlx-lm, to find out which is the faster way to run the model on a Mac, llama.cpp or mlx. Runtime llama.cpp Metal + MTP llama.cpp Metal MLX-LM MLX-LM MLX-LM I thought MLX (being optimised for the Mac) would be fastest. However, for this specific setup, llama.cpp was faster than MLX, and llama.cpp with MTP was clearly the best option. I guess all the effort and tweaking which has gone into llama.cpp over time means it quite well optimised fr macOS despite being cross platform. I also tried Gemma 4 MTP through gemma-4-swift-mlx, but the tested 26B 4-bit MLX checkpoints did not match the loader's expected weight keys, and I already had the previous MLX tests, so moved on rather than redownload new models and try to tweak things to match. Adding Image Support For Pi, I also wanted to be able to attach screenshots. The local model entry I setup for it originally declared the model as text-only: Links
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