Is unified memory on MacBook Pro M3 Max 128GB as fast as VRAM for Mistral Small 24B Instruct 2501?

Not always. MacBook Pro M3 Max 128GB 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 24B Instruct 2501 run on MacBook Pro M3 Max 128GB?

YES — Runs Great

C44Usable

Estimated from fit model

Mistral Small 24B Instruct 2501 needs ~32.2 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~16 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) — 32.2 GB, 16.4 tok/s, Runs well

32.2 GB required92.2 GB available

35% VRAM used

Fit status

Runs well

Decode

16.4 tok/s

TTFT

11810 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 24B Instruct 2501 on MacBook Pro M3 Max 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: 16.4 tok/s decode · 11.8s TTFT (warm) · 41 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	16.4 tok/s	6442 ms	357K
Coding	C	Runs well	16.4 tok/s	11810 ms	357K
Agentic Coding	C	Runs well	16.4 tok/s	17178 ms	357K
Reasoning	C	Runs well	16.4 tok/s	13957 ms	357K
RAG	C	Runs well	16.4 tok/s	21472 ms	357K

Quantization options

How Mistral Small 24B Instruct 2501 (24B params) fits at each quantization level on MacBook Pro M3 Max 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

Get started

Copy-paste commands to run Mistral Small 24B Instruct 2501 on your machine.

Run

lms load hf-maziyarpanahi--mistral-small-24b-instruct-2501-gguf && lms server start

Upgrade options

Hardware that runs Mistral Small 24B Instruct 2501 well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+128)819 GB/s (+419)

Raises estimated decode speed by about 132%.38 tok/s decode

Raises estimated decode speed by about 132%.

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

~$6,999 MSRP

AMD Instinct MI350X 288GBBest value

288 GB VRAM (+160)8000 GB/s (+7600)

Raises estimated decode speed by about 1949%.336 tok/s decode

Raises estimated decode speed by about 1949%.

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

~$8,000 MSRP

Frequently asked questions

Can MacBook Pro M3 Max 128GB run Mistral Small 24B Instruct 2501?

Yes, MacBook Pro M3 Max 128GB can run Mistral Small 24B Instruct 2501 with a C grade (Runs well). Expected decode speed: 16.4 tok/s.

How much VRAM does Mistral Small 24B Instruct 2501 need?

Mistral Small 24B Instruct 2501 (24B parameters) requires approximately 32.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Mistral Small 24B Instruct 2501?

The recommended quantization for Mistral Small 24B Instruct 2501 is Q4_K_M, which balances quality and memory efficiency.

What speed will Mistral Small 24B Instruct 2501 run at on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, Mistral Small 24B Instruct 2501 achieves approximately 16.4 tokens per second decode speed with a time-to-first-token of 11810ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 128GB run Mistral Small 24B Instruct 2501 for coding?

For coding workloads, Mistral Small 24B Instruct 2501 on MacBook Pro M3 Max 128GB receives a C grade with 16.4 tok/s and 357K context.

What context window can Mistral Small 24B Instruct 2501 use on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, Mistral Small 24B Instruct 2501 can safely use up to 357K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M3 Max 128GB See all hardware for Mistral Small 24B Instruct 2501

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