How to Setup a Local Coding Agent on macOS
Source: Hacker News
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:
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was fast enough to actually use on my Mac
-
worked through an OpenAI compatible API (so I could use it in other tools)
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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:
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llama.cpp built with Metal on macOS
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Gemma 4 26B-A4B in GGUF format
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A Q8 MTP draft model for speculative decoding
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The Gemma 4 multimodal projector
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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 Model
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
-m models/unsloth-gemma-4-26B-A4B-it-GGUF/gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf
-ngl 999
-fa on
-c 4096
-n 128
Result:
Setup Prompt tok/s Generation tok/s
Gemma 4 26B-A4B Q4, llama.cpp Metal 298.0 58.2
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
-m models/unsloth-gemma-4-26B-A4B-it-GGUF/gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf
—model-draft models/unsloth-gemma-4-26B-A4B-it-GGUF/MTP/gemma-4-26B-A4B-it-Q8_0-MTP.gguf
—spec-type draft-mtp
—spec-draft-n-max 3
-ngl 999
-fa on
-c 4096
-n 128
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 Prompt tok/s Generation tok/s Speedup
Main model only 298.0 58.2 1.00x
Main model + Q8 MTP draft 295.6 72.2 1.24x
The useful part is that prompt processing stayed basically the same, while generation improved by about 24%.
Tuning MTP
I tested --spec-draft-n-max values from 1 to 6.
--spec-draft-n-max
Prompt tok/s
Generation tok/s
1 295.5 68.4
2 299.1 72.0
3 295.6 72.2
4 297.3 70.7
5 297.9 63.7
6 296.3 61.2
On my M1 Max machine, 3 was the fastest, with 2 close enough that either would be fine. Values above that got slower.
MLX Comparison
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 Model Generation tok/s
llama.cpp Metal + MTP Unsloth GGUF Q4 + Q8 MTP 72.2
llama.cpp Metal Unsloth GGUF Q4 58.2
MLX-LM Unsloth UD MLX 4-bit 45.8
MLX-LM mlx-community 4-bit 43.9
MLX-LM mlx-community OptiQ 4-bit 38.1
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:
“input”: [“text”]
That meant Pi did not send image tool output through to the model properly.
The llama.cpp server also needs the Gemma 4 multimodal projector in order for the multi-modal part to work (only the 12B is natively multi-modal):
mmproj-BF16.gguf
When loaded with --mmproj, llama.cpp advertises multimodal support, and Pi can send images.
I re-ran the text benchmark with the projector loaded, just to check it didn’t change the speed:
Setup Projector Prompt tok/s Generation tok/s
llama.cpp Metal + MTP none 120.3 71.4
llama.cpp Metal + MTP
mmproj-BF16.gguf
297.4
72.2
The final run with the projector did not show a text-generation slowdown.
Now for setup instructions:
Install llama.cpp
Install dependencies:
brew install cmake git tmux python@3.11
Clone and build llama.cpp:
mkdir -p ~/Developer/ML-Models/Gemma4/repos cd ~/Developer/ML-Models/Gemma4
git clone https://github.com/ggml-org/llama.cpp repos/llama.cpp
cd repos/llama.cpp
cmake -B build
-DCMAKE_BUILD_TYPE=Release
-DGGML_METAL=ON
-DGGML_ACCELERATE=ON
cmake —build build —config Release -j
The build I tested had:
GGML_METAL=ON GGML_ACCELERATE=ON GGML_BLAS=ON GGML_BLAS_VENDOR=Apple
Download the Model Files
Create a Python environment:
cd ~/Developer/ML-Models/Gemma4 python3.11 -m venv .venv source .venv/bin/activate pip install -U huggingface_hub hf_xet
Download the files:
mkdir -p models/unsloth-gemma-4-26B-A4B-it-GGUF
huggingface-cli download unsloth/gemma-4-26B-A4B-it-GGUF
gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf
mmproj-BF16.gguf
MTP/gemma-4-26B-A4B-it-Q8_0-MTP.gguf
—local-dir models/unsloth-gemma-4-26B-A4B-it-GGUF
You should end up with:
models/unsloth-gemma-4-26B-A4B-it-GGUF/ gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf mmproj-BF16.gguf MTP/gemma-4-26B-A4B-it-Q8_0-MTP.gguf
Start the Local Server
This is the final server command:
repos/llama.cpp/build/bin/llama-server
-m models/unsloth-gemma-4-26B-A4B-it-GGUF/gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf
—model-draft models/unsloth-gemma-4-26B-A4B-it-GGUF/MTP/gemma-4-26B-A4B-it-Q8_0-MTP.gguf
—mmproj models/unsloth-gemma-4-26B-A4B-it-GGUF/mmproj-BF16.gguf
—spec-type draft-mtp
—spec-draft-n-max 3
-ngl 999
-fa on
-c 65536
—parallel 1
—host 127.0.0.1
—port 8080
The OpenAI-compatible endpoint is:
I used a small start_server.sh wrapper so it runs inside tmux:
#!/usr/bin/env bash set -euo pipefail
ROOT_DIR=”$(cd ”$(dirname ”${BASH_SOURCE[0]}”)” && pwd)” SESSION_NAME=”${SESSION_NAME:-gemma4-server}” HOST=”${HOST:-127.0.0.1}” PORT=”${PORT:-8080}” CTX_SIZE=”${CTX_SIZE:-65536}” PARALLEL=”${PARALLEL:-1}”
LLAMA_SERVER=“$ROOT_DIR/repos/llama.cpp/build/bin/llama-server” MODEL=“$ROOT_DIR/models/unsloth-gemma-4-26B-A4B-it-GGUF/gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf” DRAFT_MODEL=“$ROOT_DIR/models/unsloth-gemma-4-26B-A4B-it-GGUF/MTP/gemma-4-26B-A4B-it-Q8_0-MTP.gguf” MMPROJ=“$ROOT_DIR/models/unsloth-gemma-4-26B-A4B-it-GGUF/mmproj-BF16.gguf” LOG_FILE=“$ROOT_DIR/logs/llama-server-mtp.log”
mkdir -p “$ROOT_DIR/logs”
tmux new-session -d -s “$SESSION_NAME” -c “$ROOT_DIR”
“$LLAMA_SERVER
-m ‘$MODEL’
—model-draft ‘$DRAFT_MODEL’
—mmproj ‘$MMPROJ’
—spec-type draft-mtp
—spec-draft-n-max 3
-ngl 999
-fa on
-c ‘$CTX_SIZE’
—parallel ‘$PARALLEL’
—host ‘$HOST’
—port ‘$PORT’
2>&1 | tee -a ‘$LOG_FILE’”
Start it:
chmod +x start_server.sh ./start_server.sh
Check that the server is running:
curl http://127.0.0.1:8080/v1/models
Configure Pi
Pi reads model providers from:
~/.pi/agent/models.json
Add a local provider:
{ “providers”: { “gemma4-local”: { “name”: “Gemma 4 Local”, “baseUrl”: “http://127.0.0.1:8080/v1”, “api”: “openai-completions”, “apiKey”: “local”, “authHeader”: false, “compat”: { “supportsDeveloperRole”: false, “supportsReasoningEffort”: false }, “models”: [ { “id”: “gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf”, “name”: “Gemma 4 26B-A4B Q4 + MTP”, “reasoning”: false, “input”: [“text”, “image”], “contextWindow”: 65536, “maxTokens”: 8192, “cost”: { “input”: 0, “output”: 0, “cacheRead”: 0, “cacheWrite”: 0 } } ] } } }
The important pieces are:
-
baseUrlpoints to the llama.cpp OpenAI-compatible server. -
apiisopenai-completions. -
authHeaderisfalse, because this is a local server. -
inputincludes bothtextandimage, otherwise Pi treats it as text-only.
Optionally make it the default in:
~/.pi/agent/settings.json
{ “defaultProvider”: “gemma4-local”, “defaultModel”: “gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf”, “defaultThinkingLevel”: “minimal” }
Then check Pi can see it:
pi —offline —list-models gemma
Expected:
provider model context max-out thinking images gemma4-local gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf 65.5K 8.2K no yes
Run Pi using the local model:
pi —provider gemma4-local —model gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf
Or use non-interactive mode:
pi -p —provider gemma4-local —model gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf
“Explain what this repository does”
For screenshots:
pi -p @“/path/to/screenshot.png” “Describe this image and point out anything relevant to the UI”
Final Setup
The final local coding-agent stack was:
Layer Choice
Inference runtime llama.cpp
macOS acceleration Metal + Accelerate
Main model
gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf
Draft model
gemma-4-26B-A4B-it-Q8_0-MTP.gguf
MTP setting
--spec-draft-n-max 3
Multimodal projector
mmproj-BF16.gguf
Server
llama-server on 127.0.0.1:8080
API
OpenAI-compatible /v1
Coding agent Pi
Pi model input
["text", "image"]
The main conclusion was that the MTP draft model is worth using. On this machine it took Gemma 4 from 58.2 tokens/second to 72.2 tokens/second, while keeping the setup simple enough to run as a local OpenAI-compatible server.
P.S: Some suggested using Qwen3.6 35B-A3B instead of Gemma 4 26B-A4B. According to the benchmarks I can find, Qwen is a much better coding agent than Gemma 4.
However, it is also slower. Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf + unsloth-Qwen3.6-35B-A3B-MTP-GGUF + mmproj-BF16.gguf results in 55 tk/s, instead of 72 tk/s. Which is quite significant when you are sitting waiting for it.
Download the models:
mkdir -p models/unsloth-Qwen3.6-35B-A3B-MTP-GGUF
huggingface-cli download unsloth/Qwen3.6-35B-A3B-MTP-GGUF
Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf
mmproj-BF16.gguf
—local-dir models/unsloth-Qwen3.6-35B-A3B-MTP-GGUF
Start the server:
LLAMA_SERVER=/Users/kylehowells/Developer/ML-Models/Gemma4/repos/llama.cpp/build/bin/llama-server
$LLAMA_SERVER
-m models/unsloth-Qwen3.6-35B-A3B-MTP-GGUF/Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf
—mmproj models/unsloth-Qwen3.6-35B-A3B-MTP-GGUF/mmproj-BF16.gguf
—spec-type draft-mtp
—spec-draft-n-max 3
-ngl 999
-fa on
-c 65536
—parallel 1
—host 127.0.0.1
—port 8081
Pi Config:
{ “providers”: { “qwen36-local”: { “name”: “Qwen3.6 Local”, “baseUrl”: “http://127.0.0.1:8081/v1”, “api”: “openai-completions”, “apiKey”: “local”, “authHeader”: false, “compat”: { “supportsDeveloperRole”: false, “supportsReasoningEffort”: false }, “models”: [ { “id”: “Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf”, “name”: “Qwen3.6 35B-A3B Q4 + MTP”, “reasoning”: true, “input”: [“text”, “image”], “contextWindow”: 65536, “maxTokens”: 8192, “cost”: { “input”: 0, “output”: 0, “cacheRead”: 0, “cacheWrite”: 0 } } ] } } }