Is unified memory on MacBook Pro M4 Max 64GB as fast as VRAM for Mistral Small 3.2 24B Instruct 2506?

Not always. MacBook Pro M4 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.

Can Mistral Small 3.2 24B Instruct 2506 run on MacBook Pro M4 Max 64GB?

YES — Runs Great

C49Usable

Estimated from fit model

Mistral Small 3.2 24B Instruct 2506 needs ~25.3 GB VRAM. MacBook Pro M4 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~24 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) — 25.3 GB, 34.2 tok/s, Runs well

25.3 GB required46.1 GB available

55% VRAM used

Fit status

Runs well

Decode

34.2 tok/s

TTFT

5663 ms

Safe context

134K

Memory

25.3 GB / 46.1 GB

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsMistral Small 3.2 24B Instruct 2506 on MacBook Pro M4 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: 34.2 tok/s decode · 5.7s TTFT (warm) · 86 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	C	Runs well	34.2 tok/s	3089 ms	134K
Coding	C	Runs well	23.5 tok/s	8240 ms	134K
Agentic Coding	C	Runs well	34.2 tok/s	8237 ms	134K
Reasoning	C	Runs well	34.2 tok/s	6693 ms	134K
RAG	C	Runs well	34.2 tok/s	10296 ms	134K

Quantization options

How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on MacBook Pro M4 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C43
Q3_K_S	3	11.8 GB	Low	C44
NVFP4	4

Get started

Copy-paste commands to run Mistral Small 3.2 24B Instruct 2506 on your machine.

Run

lms load hf-unsloth--mistral-small-3-2-24b-instruct-2506-gguf && lms server start

Upgrade options

Hardware that runs Mistral Small 3.2 24B Instruct 2506 well

RTX PRO 5000 Blackwell 48GBBudget pick

1344 GB/s (+798)

Raises estimated decode speed by about 125%.77.1 tok/s decode

Raises estimated decode speed by about 125%.

~$4,999 MSRP

RTX 6000 Ada 48GBBest value

960 GB/s (+414)

Raises estimated decode speed by about 57%.53.8 tok/s decode

Raises estimated decode speed by about 57%.

~$6,800 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 64GB run Mistral Small 3.2 24B Instruct 2506?

Yes, MacBook Pro M4 Max 64GB can run Mistral Small 3.2 24B Instruct 2506 with a C grade (Runs well). Expected decode speed: 23.5 tok/s.

How much VRAM does Mistral Small 3.2 24B Instruct 2506 need?

Mistral Small 3.2 24B Instruct 2506 (24B parameters) requires approximately 25.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 3.2 24B Instruct 2506?

The recommended quantization for Mistral Small 3.2 24B Instruct 2506 is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 3.2 24B Instruct 2506 run at on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Mistral Small 3.2 24B Instruct 2506 achieves approximately 23.5 tokens per second decode speed with a time-to-first-token of 8240ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 64GB run Mistral Small 3.2 24B Instruct 2506 for coding?

For coding workloads, Mistral Small 3.2 24B Instruct 2506 on MacBook Pro M4 Max 64GB receives a C grade with 23.5 tok/s and 134K context.

What context window can Mistral Small 3.2 24B Instruct 2506 use on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Mistral Small 3.2 24B Instruct 2506 can safely use up to 134K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M4 Max 64GB See all hardware for Mistral Small 3.2 24B Instruct 2506

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