Can Pixtral 12B run on MacBook Pro M3 Max 64GB?

Q: Is unified memory on MacBook Pro M3 Max 64GB as fast as VRAM for Pixtral 12B?

Not always. MacBook Pro M3 Max 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

A71Great

Estimated from fit model

Pixtral 12B needs ~17.6 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~35 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) — 17.6 GB, 35.2 tok/s, Runs well

17.6 GB required46.1 GB available

38% VRAM used

Fit status

Runs well

Decode

35.2 tok/s

TTFT

5493 ms

Safe context

131K

Memory

17.6 GB / 46.1 GB

Memory breakdown

Weights7.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsPixtral 12B on MacBook Pro M3 Max 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: 35.2 tok/s decode · 5.5s TTFT (warm) · 88 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	35.2 tok/s	2996 ms	131K
Coding	A	Runs well	35.2 tok/s	5493 ms	131K
Agentic Coding	A	Runs well	35.2 tok/s	7990 ms	131K
Reasoning	A	Runs well	35.2 tok/s	6492 ms	131K
RAG	A	Runs well	35.2 tok/s	9987 ms	131K

Quantization options

How Pixtral 12B (12B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	B65
Q3_K_S	3	5.9 GB	Low	B66
NVFP4	4

Get started

Copy-paste commands to run Pixtral 12B on your machine.

Run

ollama run pixtral

Your hardware

More models your MacBook Pro M3 Max 64GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	36.3 tok/s
Qwen 3.5 27B	27B	S	15.7 tok/s

Frequently asked questions

Can MacBook Pro M3 Max 64GB run Pixtral 12B?

Yes, MacBook Pro M3 Max 64GB can run Pixtral 12B with a A grade (Runs well). Expected decode speed: 35.2 tok/s.

How much VRAM does Pixtral 12B need?

Pixtral 12B (12B parameters) requires approximately 17.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Pixtral 12B?

The recommended quantization for Pixtral 12B is Q4_K_M, which balances quality and memory efficiency.

What speed will Pixtral 12B run at on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, Pixtral 12B achieves approximately 35.2 tokens per second decode speed with a time-to-first-token of 5493ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 64GB run Pixtral 12B for coding?

For coding workloads, Pixtral 12B on MacBook Pro M3 Max 64GB receives a A grade with 35.2 tok/s and 131K context.

What context window can Pixtral 12B use on MacBook Pro M3 Max 64GB?

On MacBook Pro M3 Max 64GB, Pixtral 12B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Max 64GB as fast as VRAM for Pixtral 12B?

See all results for MacBook Pro M3 Max 64GB See all hardware for Pixtral 12B

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