text model · OLMo · macOS

Can I run OLMo 2 32B Instruct on Apple M3 Ultra (256GB)?

Compatibility verdict VRAM threshold engine

Yes, it runs GPU accelerated ~34 tok/s est.

Yes. OLMo 2 32B Instruct runs on Apple M3 Ultra (256GB) at Q4_K_M (~21.7 GB of ~192 GB usable).

Needs ~21.7 GB Device usable ~192 GB

Runs at Q4_K_M using ~21.7 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 ~170.3 GB of headroom, room to step up to FP16 for higher quality.

Q4_K_M needed: ~21.7 GB
Usable on device: ~192 GB
Device memory: 256 GB
Best quant: Q4_K_M

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Which quant fits

Quant ladder vs ~192 GB usable

Q2_K

~15.6 GB

Q3_K_M

~17.8 GB

Q4_K_M

~21.7 GB

Q5_K_M

~25 GB

Q6_K

~28.4 GB

Q8_0

~36.5 GB

FP16

~66.7 GB

The line marks Apple M3 Ultra (256GB)'s ~192 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.

Power draw: ~270 W
Electricity / 1M tokens: ~$0.33
Pays for itself after: ~23,524M tok

At ~$0.15/kWh and the estimated ~34 tok/s, a million generated tokens costs about $0.33 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$3,999 Apple M3 Ultra (256GB) pays for itself after roughly 23,524 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

Install commands macOS

Pick your tool. All 2 load the same Q4_K_M weights.

llama.cpp

$ llama-cli -hf unsloth/OLMo-2-0325-32B-Instruct-GGUF:Q4_K_M

LM Studio

$ lms get unsloth/OLMo-2-0325-32B-Instruct-GGUF

How to run it

On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).

Model OLMo

Parameters: 32B
Q4_K_M size: 19.5 GB
Q8_0 size: 34.3 GB
Context: 4k

Full OLMo 2 32B Instruct requirements →

Device macOS

Memory: 256 GB unified
Usable for weights: ~192 GB
Power draw: ~270 W
Best runtime: MLX direct / Ollama (MLX backend)

Best models for Apple M3 Ultra (256GB) →

You could also run

Run OLMo 2 32B Instruct on other hardware

Nvidia GeForce RTX 4090 (24GB)Nvidia GeForce RTX 3090 (24GB)AMD Radeon RX 7900 XTX (24GB)Nvidia GeForce RTX 5090 (32GB)32GB RAM Laptop (CPU/iGPU only)Apple M4 Pro (48GB)

FAQ

Can Apple M3 Ultra (256GB) run OLMo 2 32B Instruct?

Yes. OLMo 2 32B Instruct runs on Apple M3 Ultra (256GB) at Q4_K_M (~21.7 GB of ~192 GB usable).

How much memory does OLMo 2 32B Instruct need?

Apple M3 Ultra (256GB) has room to spare. At Q4_K_M the weights are ~19.5 GB; with KV cache and runtime overhead, budget ~21.7 GB at a 4k context.

What is the best tool to run OLMo 2 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.

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Markdown

[![OLMo 2 32B Instruct on Apple M3 Ultra (256GB)](https://localmodel.run/badge/olmo-2-32b/apple-m3-ultra-256gb.svg)](https://localmodel.run/can-i-run/olmo-2-32b/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.