Can mistral small 3.1 24b instruct 2503 hf run on MacBook Pro M1 Max 64GB?

YES — Runs Great

C48Usable

Estimated from fit model

mistral small 3.1 24b instruct 2503 hf needs ~25.3 GB VRAM. MacBook Pro M1 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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, 15.0 tok/s, Runs well

25.3 GB required46.1 GB available

55% VRAM used

Fit status

Runs well

Decode

15.0 tok/s

TTFT

12883 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 3.1 24b instruct 2503 hf on MacBook Pro M1 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: 15.0 tok/s decode · 12.9s TTFT (warm) · 38 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	15.0 tok/s	7027 ms	134K
Coding	C	Runs well	15.0 tok/s	12883 ms	134K
Agentic Coding	C	Runs well	15.0 tok/s	18739 ms	134K
Reasoning	C	Runs well	15.0 tok/s	15226 ms	134K
RAG	C	Runs well	15.0 tok/s	23424 ms	134K

Quantization options

How mistral small 3.1 24b instruct 2503 hf (24B params) fits at each quantization level on MacBook Pro M1 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	13.4 GB	Medium	C44
Q4_K_M	4	14.6 GB	Medium	C45
Q5_K_M	5	17.3 GB	High	C46
Q6_K	6	19.7 GB	High	C46
Q8_0Best for your GPU	8	25.7 GB	Very High	C48
F16	16	49.2 GB	Maximum	F0

Get started

Copy-paste commands to run mistral small 3.1 24b instruct 2503 hf on your machine.

Run

lms load hf-maziyarpanahi--mistral-small-3-1-24b-instruct-2503-hf-gguf && lms server start

Upgrade-Optionen

Hardware, die mistral small 3.1 24b instruct 2503 hf gut ausführt

Mac Studio M3 Ultra 96GBGünstige Wahl

96 GB Unified (+32)819 GB/s (+419)

Raises estimated decode speed by about 153%.38 tok/s Dekodierung

Raises estimated decode speed by about 153%.

Adds memory headroom for longer context windows and future model growth.

ca. $3,999 MSRP

Radeon Pro W7900 48GBBestes Preis-Leistungs-Verhältnis

864 GB/s (+464)

Raises estimated decode speed by about 132%.34.8 tok/s Dekodierung

Raises estimated decode speed by about 132%.

ca. $3,999 MSRP

Frequently asked questions

Can MacBook Pro M1 Max 64GB run mistral small 3.1 24b instruct 2503 hf?

Yes, MacBook Pro M1 Max 64GB can run mistral small 3.1 24b instruct 2503 hf with a C grade (Runs well). Expected decode speed: 15.0 tok/s.

How much VRAM does mistral small 3.1 24b instruct 2503 hf need?

mistral small 3.1 24b instruct 2503 hf (24B parameters) requires approximately 25.3 GB of memory with Q4_K_M quantization.

What is the best quantization for mistral small 3.1 24b instruct 2503 hf?

The recommended quantization for mistral small 3.1 24b instruct 2503 hf is Q4_K_M, which balances quality and memory efficiency.

What speed will mistral small 3.1 24b instruct 2503 hf run at on MacBook Pro M1 Max 64GB?

On MacBook Pro M1 Max 64GB, mistral small 3.1 24b instruct 2503 hf achieves approximately 15.0 tokens per second decode speed with a time-to-first-token of 12883ms using Q4_K_M quantization.

Can MacBook Pro M1 Max 64GB run mistral small 3.1 24b instruct 2503 hf for coding?

For coding workloads, mistral small 3.1 24b instruct 2503 hf on MacBook Pro M1 Max 64GB receives a C grade with 15.0 tok/s and 134K context.

What context window can mistral small 3.1 24b instruct 2503 hf use on MacBook Pro M1 Max 64GB?

On MacBook Pro M1 Max 64GB, mistral small 3.1 24b instruct 2503 hf can safely use up to 134K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M1 Max 64GB as fast as VRAM for mistral small 3.1 24b instruct 2503 hf?

Not always. MacBook Pro M1 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.

See all results for MacBook Pro M1 Max 64GB See all hardware for mistral small 3.1 24b instruct 2503 hf

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