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Run Local LLMs on Mac
This guide walks you through running a local LLM server on macOS with an OpenAI-compatible API. You get full privacy, zero API costs, and surprisingly good performance on Apple Silicon.
We cover two backends:
| Backend | Install | Best at | Format |
|---|---|---|---|
| llama.cpp | brew install llama.cpp | Fastest time-to-first-token, quantized KV cache for low memory | GGUF |
| omlx | omlx.ai | Fastest token generation, native Metal optimization | MLX (safetensors) |
Both expose an OpenAI-compatible /v1/chat/completions endpoint. Hermes works with either one — just point it at http://localhost:8080 or http://localhost:8000.
This guide targets Macs with Apple Silicon (M1 and later). Intel Macs will work with llama.cpp but without GPU acceleration — expect significantly slower performance.
Choosing a model
For getting started, we recommend Qwen3.5-9B — it's a strong reasoning model that fits comfortably in 8GB+ of unified memory with quantization.
| Variant | Size on disk | RAM needed (128K context) | Backend |
|---|---|---|---|
| Qwen3.5-9B-Q4_K_M (GGUF) | 5.3 GB | ~10 GB with quantized KV cache | llama.cpp |
| Qwen3.5-9B-mlx-lm-mxfp4 (MLX) | ~5 GB | ~12 GB | omlx |
Memory rule of thumb: model size + KV cache. A 9B Q4 model is ~5 GB. The KV cache at 128K context with Q4 quantization adds ~4-5 GB. With default (f16) KV cache, that balloons to ~16 GB. The quantized KV cache flags in llama.cpp are the key trick for memory-constrained systems.
For larger models (27B, 35B), you'll need 32 GB+ of unified memory. The 9B is the sweet spot for 8-16 GB machines.
Option A: llama.cpp
llama.cpp is the most portable local LLM runtime. On macOS it uses Metal for GPU acceleration out of the box.
Install
brew install llama.cpp
This gives you the llama-server command globally.
Download the model
You need a GGUF-format model. The easiest source is Hugging Face via the huggingface-cli:
brew install huggingface-cli
Then download:
huggingface-cli download unsloth/Qwen3.5-9B-GGUF Qwen3.5-9B-Q4_K_M.gguf --local-dir ~/models
Some models on Hugging Face require authentication. Run huggingface-cli login first if you get a 401 or 404 error.
Start the server
llama-server -m ~/models/Qwen3.5-9B-Q4_K_M.gguf \
-ngl 99 \
-c 131072 \
-np 1 \
-fa on \
--cache-type-k q4_0 \
--cache-type-v q4_0 \
--host 0.0.0.0
Here's what each flag does:
| Flag | Purpose |
|---|---|
-ngl 99 | Offload all layers to GPU (Metal). Use a high number to ensure nothing stays on CPU. |
-c 131072 | Context window size (128K tokens). Reduce this if you're low on memory. |
-np 1 | Number of parallel slots. Keep at 1 for single-user use — more slots split your memory budget. |
-fa on | Flash attention. Reduces memory usage and speeds up long-context inference. |
--cache-type-k q4_0 | Quantize the key cache to 4-bit. This is the big memory saver. |
--cache-type-v q4_0 | Quantize the value cache to 4-bit. Together with the above, this cuts KV cache memory by ~75% vs f16. |
--host 0.0.0.0 | Listen on all interfaces. Use 127.0.0.1 if you don't need network access. |
The server is ready when you see:
main: server is listening on http://0.0.0.0:8080
srv update_slots: all slots are idle
Memory optimization for constrained systems
The --cache-type-k q4_0 --cache-type-v q4_0 flags are the most important optimization for systems with limited memory. Here's the impact at 128K context:
| KV cache type | KV cache memory (128K ctx, 9B model) |
|---|---|
| f16 (default) | ~16 GB |
| q8_0 | ~8 GB |
| q4_0 | ~4 GB |
On an 8 GB Mac, use q4_0 KV cache and reduce context to -c 32768 (32K). On 16 GB, you can comfortably do 128K context. On 32 GB+, you can run larger models or multiple parallel slots.
If you're still running out of memory, reduce context size first (-c), then try a smaller quantization (Q3_K_M instead of Q4_K_M).
Test it
curl -s http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen3.5-9B-Q4_K_M.gguf",
"messages": [{"role": "user", "content": "Hello!"}],
"max_tokens": 50
}' | jq .choices[0].message.content
Get the model name
If you forget the model name, query the models endpoint:
curl -s http://localhost:8080/v1/models | jq '.data[].id'
Option B: MLX via omlx
omlx is a macOS-native app that manages and serves MLX models. MLX is Apple's own machine learning framework, optimized specifically for Apple Silicon's unified memory architecture.
Install
Download and install from omlx.ai. It provides a GUI for model management and a built-in server.
Download the model
Use the omlx app to browse and download models. Search for Qwen3.5-9B-mlx-lm-mxfp4 and download it. Models are stored locally (typically in ~/.omlx/models/).
Start the server
omlx serves models on http://127.0.0.1:8000 by default. Start serving from the app UI, or use the CLI if available.
Test it
curl -s http://127.0.0.1:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen3.5-9B-mlx-lm-mxfp4",
"messages": [{"role": "user", "content": "Hello!"}],
"max_tokens": 50
}' | jq .choices[0].message.content
List available models
omlx can serve multiple models simultaneously:
curl -s http://127.0.0.1:8000/v1/models | jq '.data[].id'
Benchmarks: llama.cpp vs MLX
Both backends tested on the same machine (Apple M5 Max, 128 GB unified memory) running the same model (Qwen3.5-9B) at comparable quantization levels (Q4_K_M for GGUF, mxfp4 for MLX). Five diverse prompts, three runs each, backends tested sequentially to avoid resource contention.
Results
| Metric | llama.cpp (Q4_K_M) | MLX (mxfp4) | Winner |
|---|---|---|---|
| TTFT (avg) | 67 ms | 289 ms | llama.cpp (4.3x faster) |
| TTFT (p50) | 66 ms | 286 ms | llama.cpp (4.3x faster) |
| Generation (avg) | 70 tok/s | 96 tok/s | MLX (37% faster) |
| Generation (p50) | 70 tok/s | 96 tok/s | MLX (37% faster) |
| Total time (512 tokens) | 7.3s | 5.5s | MLX (25% faster) |
What this means
-
llama.cpp excels at prompt processing — its flash attention + quantized KV cache pipeline gets you the first token in ~66ms. If you're building interactive applications where perceived responsiveness matters (chatbots, autocomplete), this is a meaningful advantage.
-
MLX generates tokens ~37% faster once it gets going. For batch workloads, long-form generation, or any task where total completion time matters more than initial latency, MLX finishes sooner.
-
Both backends are extremely consistent — variance across runs was negligible. You can rely on these numbers.
Which one should you pick?
| Use case | Recommendation |
|---|---|
| Interactive chat, low-latency tools | llama.cpp |
| Long-form generation, bulk processing | MLX (omlx) |
| Memory-constrained (8-16 GB) | llama.cpp (quantized KV cache is unmatched) |
| Serving multiple models simultaneously | omlx (built-in multi-model support) |
| Maximum compatibility (Linux too) | llama.cpp |
Connect to Hermes
Once your local server is running:
hermes model
Select Custom endpoint and follow the prompts. It will ask for the base URL and model name — use the values from whichever backend you set up above.