Can Mistral Small 3.2 24B Instruct 2506 run on MacBook Pro M3 Pro 36GB?

YES — Tight Fit

C46Usable

○Estimated from fit model

Mistral Small 3.2 24B Instruct 2506 needs ~22.2 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~8 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 22.2 GB, 7.5 tok/s, Tight fit

22.2 GB required25.9 GB available

86% VRAM used

Fit status

Tight fit

Decode

7.5 tok/s

TTFT

25884 ms

Safe context

37K

Memory

22.2 GB / 25.9 GB

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime0.9 GB

Headroom3.9 GB

See how fast it feels

See how fast it feelsMistral Small 3.2 24B Instruct 2506 on MacBook Pro M3 Pro 36GB
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: 7.5 tok/s decode · 25.9s TTFT (warm) · 19 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	7.5 tok/s	14119 ms	37K
Coding	C	Tight fit	7.5 tok/s	25884 ms	37K
Agentic Coding	C	Runs with offload	7.5 tok/s	37650 ms	37K
Reasoning	C	Tight fit	7.5 tok/s	30590 ms	37K
RAG	C	Runs with offload	7.5 tok/s	47062 ms	37K

Quantization options

How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	C48
Q3_K_S	3	11.8 GB	Low	C49
NVFP4	4	13.4 GB	Medium	C50
Q4_K_M	4	14.6 GB	Medium	C50
Q5_K_M	5	17.3 GB	High	C50
Q6_KBest for your GPU	6	19.7 GB	High	C49
Q8_0	8	25.7 GB	Very High	F0
F16	16	49.2 GB	Maximum	F0