text model · Olmo · macOS
Can I run Olmo 3 7B Instruct on Apple M5 (16GB)?
Yes. Olmo 3 7B Instruct runs on Apple M5 (16GB) at Q4_K_M (~5.6 GB of ~10.5 GB usable).
Runs at Q4_K_M using ~5.6 GB of ~10.5 GB usable. You have room for Q8_0 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M5 (16GB) leaves ~4.9 GB of headroom, room to step up to Q8_0 for higher quality.
- Q4_K_M needed
- ~5.6 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run olmo-3:7b-instruct llama-cli -hf unsloth/Olmo-3-7B-Instruct-GGUF:Q4_K_M lms get unsloth/Olmo-3-7B-Instruct-GGUF - Parameters
- 7B
- Q4_K_M size
- 4.16 GB
- Q8_0 size
- 7.23 GB
- Context
- 64k
- Ollama tag
- olmo-3:7b-instruct
- Memory
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Olmo 3 7B Instruct on other hardware
FAQ
Can Apple M5 (16GB) run Olmo 3 7B Instruct?
Yes. Olmo 3 7B Instruct runs on Apple M5 (16GB) at Q4_K_M (~5.6 GB of ~10.5 GB usable).
How much memory does Olmo 3 7B Instruct need?
Apple M5 (16GB) has room to spare. At Q4_K_M the weights are ~4.16 GB; with KV cache and runtime overhead, budget ~5.6 GB at a 4k context.
What is the best tool to run Olmo 3 7B Instruct 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/olmo-3-7b-instruct/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.