Can Mistral Small 3.2 24B Instruct 2506 run on Mac Studio M1 Ultra 128GB?

YES — Runs Great

C46Usable

○Estimated from fit model

Mistral Small 3.2 24B Instruct 2506 needs ~32.2 GB VRAM. Mac Studio M1 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 32.2 GB, 30.1 tok/s, Runs well

32.2 GB required92.2 GB available

35% VRAM used

Fit status

Runs well

Decode

30.1 tok/s

TTFT

6442 ms

Safe context

357K

Memory

32.2 GB / 92.2 GB

Memory breakdown

Weights14.6 GB

KV Cache2.8 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsMistral Small 3.2 24B Instruct 2506 on Mac Studio M1 Ultra 128GB
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: 30.1 tok/s decode · 6.4s TTFT (warm) · 75 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	30.1 tok/s	3514 ms	357K
Coding	C	Runs well	30.1 tok/s	6442 ms	357K
Agentic Coding	C	Runs well	30.1 tok/s	9370 ms	357K
Reasoning	C	Runs well	30.1 tok/s	7613 ms	357K
RAG	C	Runs well	30.1 tok/s	11712 ms	357K

Quantization options

How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on Mac Studio M1 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	D40
Q3_K_S	3	11.8 GB	Low	D40
NVFP4	4	13.4 GB	Medium	C40
Q4_K_M	4	14.6 GB	Medium	C40
Q5_K_M	5	17.3 GB	High	C41
Q6_K	6	19.7 GB	High	C41
Q8_0	8	25.7 GB	Very High	C42
F16Best for your GPU	16	49.2 GB	Maximum	C47

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

👁 NVIDIA
RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

1792 GB/s (+992)

Raises estimated decode speed by about 242%.102.8 tok/s decode

Raises estimated decode speed by about 242%.

~$9,999 MSRP

👁 NVIDIA
RTX PRO 6000 Blackwell Server Edition 96GBBest value

1597 GB/s (+797)

Raises estimated decode speed by about 204%.91.6 tok/s decode

Raises estimated decode speed by about 204%.