MacBook Pro M4 Pro 64GBコスパ重視
64 GB Unified (+32)
C
Raises estimated decode speed by about 36%.21.5 tok/sデコード
Raises estimated decode speed by about 36%.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
Can Mistral Small 24B Instruct 2501 run on MacBook Pro M2 Max 32GB?
YES — Tight Fit
C48Usable
Estimated from fit model
Mistral Small 24B Instruct 2501 needs ~21.8 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~16 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 21.8 GB, 15.8 tok/s, Tight fit
21.8 GB required23.0 GB available
95% VRAM used
Fit status
Tight fit
Decode
15.8 tok/s
TTFT
12217 ms
Safe context
23K
Memory
21.8 GB / 23.0 GB
Memory breakdown
Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom3.5 GB
See how fast it feels
See how fast it feelsMistral Small 24B Instruct 2501 on MacBook Pro M2 Max 32GB
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: 15.8 tok/s decode · 12.2s TTFT (warm) · 40 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 15.8 tok/s | 6664 ms | 23K |
| Coding | C | Tight fit | 15.8 tok/s | 12217 ms | 23K |
| Agentic Coding | D | Runs with offload (needs ~0.9 GB host RAM) | 14.1 tok/s | 19993 ms | 23K |
| Reasoning | C | Tight fit | 15.8 tok/s | 14438 ms | 23K |
| RAG | D | Runs with offload (needs ~0.9 GB host RAM) | 14.1 tok/s | 24991 ms | 23K |
Quantization options
How Mistral Small 24B Instruct 2501 (24B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C49 |
Q3_K_S | 3 | 11.8 GB | Low | C50 |
NVFP4 | 4 | 13.4 GB | Medium | C50 |
Q4_K_M | 4 | 14.6 GB | Medium | C50 |
Q5_K_MBest for your GPU | 5 | 17.3 GB | High | C50 |
Q6_K | 6 | 19.7 GB | High | F0 |
Q8_0 | 8 | 25.7 GB | Very High | F0 |
F16 | 16 | 49.2 GB | Maximum | F0 |
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アップグレードオプション
Mistral Small 24B Instruct 2501を快適に動かすハードウェア
MacBook Pro M4 Max 48GBコスパ最良
48 GB Unified (+16)546 GB/s (+146)
C
Raises estimated decode speed by about 116%.34.2 tok/sデコード
Raises estimated decode speed by about 116%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
MacBook Pro M3 Max 48GBAppleアップグレード
48 GB Unified (+16)
C
Adds memory headroom for longer context windows and future model growth.16.4 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
Frequently asked questions
Can MacBook Pro M2 Max 32GB run Mistral Small 24B Instruct 2501?
Yes, MacBook Pro M2 Max 32GB can run Mistral Small 24B Instruct 2501 with a C grade (Tight fit). Expected decode speed: 15.8 tok/s.
How much VRAM does Mistral Small 24B Instruct 2501 need?
Mistral Small 24B Instruct 2501 (24B parameters) requires approximately 21.8 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 M2 Max 32GB?
On MacBook Pro M2 Max 32GB, Mistral Small 24B Instruct 2501 achieves approximately 15.8 tokens per second decode speed with a time-to-first-token of 12217ms using Q4_K_M quantization.
Can MacBook Pro M2 Max 32GB run Mistral Small 24B Instruct 2501 for coding?
For coding workloads, Mistral Small 24B Instruct 2501 on MacBook Pro M2 Max 32GB receives a C grade with 15.8 tok/s and 23K context.
What context window can Mistral Small 24B Instruct 2501 use on MacBook Pro M2 Max 32GB?
On MacBook Pro M2 Max 32GB, Mistral Small 24B Instruct 2501 can safely use up to 23K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if Mistral Small 24B Instruct 2501 feels slow on MacBook Pro M2 Max 32GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Is unified memory on MacBook Pro M2 Max 32GB as fast as VRAM for Mistral Small 24B Instruct 2501?
Not always. MacBook Pro M2 Max 32GB 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.
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