NVIDIA A100 40GBOpção econômica1555 GB/s (+1282)
C
Raises estimated decode speed by about 315%.89.2 tok/s decodificação
Raises estimated decode speed by about 315%.
~$10,000 MSRP
Can mistral small 3.1 24b instruct 2503 hf run on MacBook Pro M4 Pro 48GB?
YES — Runs Great
C50Usable
Estimated — low-sample bucket· few comparable runs
mistral small 3.1 24b instruct 2503 hf needs ~23.5 GB VRAM. MacBook Pro M4 Pro 48GB has 34.6 GB. With Q4_K_M quantization, expect ~14 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.
Select quantization to explore
Q4_K_M (Medium quality) — 23.5 GB, 21.5 tok/s, Runs well
23.5 GB required34.6 GB available
68% VRAM used
Fit status
Runs well
Decode
21.5 tok/s
TTFT
8989 ms
Safe context
79K
Memory
23.5 GB / 34.6 GB
Memory breakdown
Weights14.6 GB
KV Cache2.8 GB
Runtime0.9 GB
Headroom5.2 GB
See how fast it feels
See how fast it feelsmistral small 3.1 24b instruct 2503 hf on MacBook Pro M4 Pro 48GB
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: 21.5 tok/s decode · 9.0s TTFT (warm) · 54 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 | 14.4 tok/s | 7354 ms | 79K |
| Coding | C | Runs well | 14.4 tok/s | 13483 ms | 79K |
| Agentic Coding | C | Runs well | 14.4 tok/s | 19612 ms | 79K |
| Reasoning | C | Runs well | 14.4 tok/s | 15935 ms | 79K |
| RAG | C | Runs well | 14.4 tok/s | 24515 ms | 79K |
Quantization options
How mistral small 3.1 24b instruct 2503 hf (24B params) fits at each quantization level on MacBook Pro M4 Pro 48GB (34.6 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C45 |
Q3_K_S | 3 | 11.8 GB | Low | C46 |
NVFP4 | 4 | 13.4 GB | Medium | C47 |
Q4_K_M | 4 | 14.6 GB | Medium | C47 |
Q5_K_M | 5 | 17.3 GB | High | C49 |
Q6_K | 6 | 19.7 GB | High | C49 |
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 startOpções de upgrade
Hardware que roda bem mistral small 3.1 24b instruct 2503 hf
Frequently asked questions
Can MacBook Pro M4 Pro 48GB run mistral small 3.1 24b instruct 2503 hf?
Yes, MacBook Pro M4 Pro 48GB can run mistral small 3.1 24b instruct 2503 hf with a C grade (Runs well). Expected decode speed: 14.4 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 23.5 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 M4 Pro 48GB?
On MacBook Pro M4 Pro 48GB, mistral small 3.1 24b instruct 2503 hf achieves approximately 14.4 tokens per second decode speed with a time-to-first-token of 13483ms using Q4_K_M quantization.
Can MacBook Pro M4 Pro 48GB 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 M4 Pro 48GB receives a C grade with 14.4 tok/s and 79K context.
What context window can mistral small 3.1 24b instruct 2503 hf use on MacBook Pro M4 Pro 48GB?
On MacBook Pro M4 Pro 48GB, mistral small 3.1 24b instruct 2503 hf can safely use up to 79K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M4 Pro 48GB as fast as VRAM for mistral small 3.1 24b instruct 2503 hf?
Not always. MacBook Pro M4 Pro 48GB 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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