text model · Olmo · macOS
Can I run Olmo 3.1 32B Instruct on Apple M3 Ultra (256GB)?
Yes. Olmo 3.1 32B Instruct runs on Apple M3 Ultra (256GB) at Q4_K_M (~20.3 GB of ~192 GB usable).
Runs at Q4_K_M using ~20.3 GB of ~192 GB usable. You have room for FP16 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M3 Ultra (256GB) leaves ~171.7 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~20.3 GB
- Usable on device
- ~192 GB
- Device memory
- 256 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~270 W
- Electricity / 1M tokens
- ~$0.31
- Pays for itself after
- ~21,047M tok
At ~$0.15/kWh and the estimated ~36 tok/s, a million generated tokens costs about $0.31 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$3,999 Apple M3 Ultra (256GB) pays for itself after roughly 21,047 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run olmo-3.1:32b-instruct llama-cli -hf bartowski/allenai_Olmo-3.1-32B-Instruct-GGUF:Q4_K_M lms get bartowski/allenai_Olmo-3.1-32B-Instruct-GGUF - Parameters
- 32B
- Q4_K_M size
- 18.14 GB
- Q8_0 size
- 31.9 GB
- Context
- 64k
- Ollama tag
- olmo-3.1:32b-instruct
- Memory
- 256 GB unified
- Usable for weights
- ~192 GB
- Power draw
- ~270 W
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run Olmo 3.1 32B Instruct on other hardware
FAQ
Can Apple M3 Ultra (256GB) run Olmo 3.1 32B Instruct?
Yes. Olmo 3.1 32B Instruct runs on Apple M3 Ultra (256GB) at Q4_K_M (~20.3 GB of ~192 GB usable).
How much memory does Olmo 3.1 32B Instruct need?
Apple M3 Ultra (256GB) has room to spare. At Q4_K_M the weights are ~18.14 GB; with KV cache and runtime overhead, budget ~20.3 GB at a 4k context.
What is the best tool to run Olmo 3.1 32B 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/olmo-3.1-32b-instruct/apple-m3-ultra-256gb) 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.