Can OLMo 2 32B run on MacBook Pro M4 Pro 64GB?

Q: Is unified memory on MacBook Pro M4 Pro 64GB as fast as VRAM for OLMo 2 32B?

Not always. MacBook Pro M4 Pro 64GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

YES — Runs Great

A82Great

Estimated — low-sample bucket· few comparable runs

OLMo 2 32B needs ~31.2 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 31.2 GB, 20.9 tok/s, Runs well

31.2 GB required46.1 GB available

68% VRAM used

Fit status

Runs well

Decode

20.9 tok/s

TTFT

9248 ms

Safe context

Memory

31.2 GB / 46.1 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsOLMo 2 32B on MacBook Pro M4 Pro 64GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 20.9 tok/s decode · 9.2s TTFT (warm) · 52 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	20.9 tok/s	5044 ms	4K
Coding	A	Runs well	10.8 tok/s	17978 ms	4K
Agentic Coding	A	Runs well	20.9 tok/s	13451 ms	4K
Reasoning	A	Runs well	20.9 tok/s	10929 ms	4K
RAG	A	Runs well	20.9 tok/s	16814 ms	4K

Quantization options

How OLMo 2 32B (32B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A76
Q3_K_S	3	15.7 GB	Low	A78
NVFP4	4

Get started

Copy-paste commands to run OLMo 2 32B on your machine.

Run

lms load OLMo-2-0325-32B-Instruct && lms server start

Your hardware

More models your MacBook Pro M4 Pro 64GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.6 35B A3B	35B	S	29.4 tok/s

Frequently asked questions

Can MacBook Pro M4 Pro 64GB run OLMo 2 32B?

Yes, MacBook Pro M4 Pro 64GB can run OLMo 2 32B with a A grade (Runs well). Expected decode speed: 10.8 tok/s.

How much VRAM does OLMo 2 32B need?

OLMo 2 32B (32B parameters) requires approximately 31.2 GB of memory with Q4_K_M quantization.

What is the best quantization for OLMo 2 32B?

The recommended quantization for OLMo 2 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will OLMo 2 32B run at on MacBook Pro M4 Pro 64GB?

On MacBook Pro M4 Pro 64GB, OLMo 2 32B achieves approximately 10.8 tokens per second decode speed with a time-to-first-token of 17978ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 64GB run OLMo 2 32B for coding?

For coding workloads, OLMo 2 32B on MacBook Pro M4 Pro 64GB receives a A grade with 10.8 tok/s and 4K context.

What context window can OLMo 2 32B use on MacBook Pro M4 Pro 64GB?

On MacBook Pro M4 Pro 64GB, OLMo 2 32B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Pro 64GB as fast as VRAM for OLMo 2 32B?

See all results for MacBook Pro M4 Pro 64GB See all hardware for OLMo 2 32B

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