Can Pixtral 12B run on MacBook Pro M4 Max 96GB?

YES — Runs Great

B70Good

Estimated from fit model

Pixtral 12B needs ~21.0 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~45 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 21.0 GB, 44.5 tok/s, Runs well

21.0 GB required69.1 GB available

30% VRAM used

Fit status

Runs well

Decode

44.5 tok/s

TTFT

4355 ms

Safe context

131K

Memory

21.0 GB / 69.1 GB

Memory breakdown

Weights7.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsPixtral 12B on MacBook Pro M4 Max 96GB
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: 44.5 tok/s decode · 4.4s TTFT (warm) · 111 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	B	Runs well	44.5 tok/s	2375 ms	131K
Coding	B	Runs well	44.5 tok/s	4355 ms	131K
Agentic Coding	A	Runs well	44.5 tok/s	6335 ms	131K
Reasoning	B	Runs well	44.5 tok/s	5147 ms	131K
RAG	A	Runs well	44.5 tok/s	7918 ms	131K

Quantization options

How Pixtral 12B (12B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	B64
Q3_K_S	3	5.9 GB	Low	B64
NVFP4	4	6.7 GB	Medium	B64
Q4_K_M	4	7.3 GB	Medium	B64
Q5_K_M	5	8.6 GB	High	B64
Q6_K	6	9.8 GB	High	B64
Q8_0	8	12.8 GB	Very High	B65
F16Best for your GPU	16	24.6 GB	Maximum	B67

Get started

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

Run

ollama run pixtral

Opciones de mejora

Hardware que ejecuta bien Pixtral 12B

Mac Studio M2 Ultra 128GBOpción económica

128 GB Unified (+32)800 GB/s (+254)

Sube la velocidad estimada de decodificación alrededor de un 53%.68.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 53%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Mac Studio M1 Ultra 128GBMejor relación calidad-precio

128 GB Unified (+32)800 GB/s (+254)

Sube la velocidad estimada de decodificación alrededor de un 45%.64.6 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 45%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 96GB run Pixtral 12B?

Yes, MacBook Pro M4 Max 96GB can run Pixtral 12B with a B grade (Runs well). Expected decode speed: 44.5 tok/s.

How much VRAM does Pixtral 12B need?

Pixtral 12B (12B parameters) requires approximately 21.0 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 M4 Max 96GB?

On MacBook Pro M4 Max 96GB, Pixtral 12B achieves approximately 44.5 tokens per second decode speed with a time-to-first-token of 4355ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 96GB run Pixtral 12B for coding?

For coding workloads, Pixtral 12B on MacBook Pro M4 Max 96GB receives a B grade with 44.5 tok/s and 131K context.

What context window can Pixtral 12B use on MacBook Pro M4 Max 96GB?

On MacBook Pro M4 Max 96GB, 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 M4 Max 96GB as fast as VRAM for Pixtral 12B?

Not always. MacBook Pro M4 Max 96GB 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.

See all results for MacBook Pro M4 Max 96GB See all hardware for Pixtral 12B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/pixtral-12b-on-m4-max-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: