text model · Command R · macOS
Can I run Command R 35B on Apple M5 (16GB)?
No. Command R 35B needs ~22.3 GB even at Q4_K_M, but Apple M5 (16GB) only has ~10.5 GB usable.
Needs ~22.3 GB even at Q4_K_M, but only ~10.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 11.8 GB: Command R 35B needs roughly 22.3 GB at Q4_K_M and Apple M5 (16GB) leaves only about 10.5 GB usable for a model. The lightest tracked hardware that runs Command R 35B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Command R 35B on Nvidia GeForce RTX 4090 (24GB).
- Q4_K_M needed
- ~22.3 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
Which quant fits
- Parameters
- 35B
- Q4_K_M size
- 20.05 GB
- Q8_0 size
- 34.63 GB
- Context
- 128k
- Ollama tag
- command-r:35b
- Memory
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
What you can run instead
Run Command R 35B on other hardware
FAQ
Can Apple M5 (16GB) run Command R 35B?
No. Command R 35B needs ~22.3 GB even at Q4_K_M, but Apple M5 (16GB) only has ~10.5 GB usable.
How much memory does Command R 35B need?
Apple M5 (16GB) does not have enough memory. At Q4_K_M the weights are ~20.05 GB; with KV cache and runtime overhead, budget ~22.3 GB at a 4k context.
What is the best tool to run Command R 35B on macOS?
LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/command-r-35b/apple-m5-16gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.