VOOZH about

URL: https://willitrunai.com/can-run/pixtral-12b-on-m2-ultra-64gb

⇱ Pixtral 12B on Mac Studio M2 Ultra 64GB? YES

Can Pixtral 12B run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

A73Great
○Estimated from fit model

Pixtral 12B needs ~17.6 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~68 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Share:

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, 68.1 tok/s, Runs well
17.6 GB required46.1 GB available
38% VRAM used

Fit status

Runs well

Decode

68.1 tok/s

TTFT

2841 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 Mac Studio M2 Ultra 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: 68.1 tok/s decode · 2.8s TTFT (warm) · 170 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

WorkloadGradeFitDecodeTTFTContext
ChatARuns well68.1 tok/s1550 ms131K
CodingARuns well68.1 tok/s2841 ms131K
Agentic CodingARuns well68.1 tok/s4133 ms131K
ReasoningARuns well68.1 tok/s3358 ms131K
RAGARuns well68.1 tok/s5166 ms131K

Quantization options

How Pixtral 12B (12B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
4.7 GB
LowB65
Q3_K_S
3
5.9 GB
LowB66
NVFP4
4
6.7 GB
MediumB66
Q4_K_M
4
7.3 GB
MediumB66
Q5_K_M
5
8.6 GB
HighB66
Q6_K
6
9.8 GB
HighB67
Q8_0
8
12.8 GB
Very HighB67
F16Best for your GPU
16
24.6 GB
MaximumA72

Get started

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

Run

ollama run pixtral

Your hardware

More models your Mac Studio M2 Ultra 64GB can run

ModelParamsGradeDecodeCapabilities
👁 Alibaba
Qwen3-Coder 30B A3B Instruct		30.5BS70.2 tok/s
👁 Alibaba
Qwen 3.5 27B		27BS30.4 tok/s
👁 Alibaba
Qwen 3.6 27B		27BS23.1 tok/s
👁 Alibaba
Qwen 3.6 35B A3B		35BS59 tok/s
👁 Alibaba
Qwen3-VL 30B A3B Instruct		30BS72.6 tok/s

Frequently asked questions

See all results for Mac Studio M2 Ultra 64GBSee all hardware for Pixtral 12B