What causes "MLX / Metal: command buffer execution failed"?

Apple's Metal Performance Shaders compiler hit an internal error or hardware fault during a kernel launch. Most often caused by: - Running out of unified memory mid-inference (no clear OOM error like CUDA gives) - macOS version too old for the MLX features the model uses - A specific quantization format the Metal backend doesn't support yet

How do you fix "MLX / Metal: command buffer execution failed"?

**Free unified memory.** Macs have no separate VRAM — system memory IS GPU memory. Close apps: ```bash # Show top memory consumers top -o MEM ``` Common culprits: Chrome (1-3 GB), Slack (500 MB), Spotify (300 MB), other ML scripts. **Update macOS.** MLX requires macOS 13.5+ for full features; some kernels need 14+. `Apple → About This Mac` shows version. Update via System Settings. **Update MLX:** ```bash pip install --upgrade mlx mlx-lm ``` **Try a different quantization.** MLX has its own format (`-mlx-4bit`, `-mlx-8bit`). GGUF Q4_K_M run via llama.cpp on Metal is also a fallback path: ```bash # llama.cpp on Metal — different code path, sometimes works when MLX doesn't ./main -m model.gguf --n-gpu-layers 999 ``` **Reduce context size** if the error only happens at long contexts — KV cache is the most common pressure point.

Metal / Apple Silicon

MLX / Metal: command buffer execution failed

[MLX][ERROR] Metal command buffer execution failed

By Fredoline Eruo · Last verified Jun 12, 2026

Cause

Apple's Metal Performance Shaders compiler hit an internal error or hardware fault during a kernel launch. Most often caused by:

Running out of unified memory mid-inference (no clear OOM error like CUDA gives)
macOS version too old for the MLX features the model uses
A specific quantization format the Metal backend doesn't support yet

Solution

Free unified memory. Macs have no separate VRAM — system memory IS GPU memory. Close apps:

# Show top memory consumers
top -o MEM

Common culprits: Chrome (1-3 GB), Slack (500 MB), Spotify (300 MB), other ML scripts.

Update macOS. MLX requires macOS 13.5+ for full features; some kernels need 14+. Apple → About This Mac shows version. Update via System Settings.

Update MLX:

pip install --upgrade mlx mlx-lm

Try a different quantization. MLX has its own format (-mlx-4bit, -mlx-8bit). GGUF Q4_K_M run via llama.cpp on Metal is also a fallback path:

# llama.cpp on Metal — different code path, sometimes works when MLX doesn't
./main -m model.gguf --n-gpu-layers 999

Reduce context size if the error only happens at long contexts — KV cache is the most common pressure point.

Related errors

Did this fix it?

If your case was different, email Contact support with what you saw and we'll update the page. If it worked but took different commands on your platform, we want to know that too.