Radeon Pro W7900 48GBGünstige Wahl
864 GB/s (+591)
C
Raises estimated decode speed by about 62%.34.8 tok/s Dekodierung
Raises estimated decode speed by about 62%.
ca. $3,999 MSRP
Can mistral small 3.1 24b instruct 2503 hf run on MacBook Pro M4 Pro 64GB?
YES — Runs Great
C49Usable
Estimated — low-sample bucket· few comparable runs
mistral small 3.1 24b instruct 2503 hf needs ~25.3 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~22 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) — 25.3 GB, 21.5 tok/s, Runs well
25.3 GB required46.1 GB available
55% VRAM used
Fit status
Runs well
Decode
21.5 tok/s
TTFT
8989 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 M4 Pro 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: 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 | 21.5 tok/s | 4903 ms | 134K |
| Coding | C | Runs well | 21.5 tok/s | 8989 ms | 134K |
| Agentic Coding | C | Runs well | 21.5 tok/s | 13075 ms | 134K |
| Reasoning | C | Runs well | 21.5 tok/s | 10623 ms | 134K |
| RAG | C | Runs well | 21.5 tok/s | 16343 ms | 134K |
Inference speed
mistral small 3.1 24b instruct 2503 hf inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for mistral small 3.1 24b instruct 2503 hf at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~82 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 82.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 52.3 | Tight |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 47.2 | Tight |
| RTX 3090 24GB | 24 GB | Q4_K_M | 44.8 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 38.0 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 34.2 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 34.2 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 31.7 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 30.1 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.5 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 19.1 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 16.4 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 15.0 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 6.7 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 4.2 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How mistral small 3.1 24b instruct 2503 hf (24B params) fits at each quantization level on MacBook Pro M4 Pro 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 startUpgrade-Optionen
Hardware, die mistral small 3.1 24b instruct 2503 hf gut ausführt
Frequently asked questions
Can MacBook Pro M4 Pro 64GB run mistral small 3.1 24b instruct 2503 hf?
Yes, MacBook Pro M4 Pro 64GB can run mistral small 3.1 24b instruct 2503 hf with a C grade (Runs well). Expected decode speed: 21.5 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 M4 Pro 64GB?
On MacBook Pro M4 Pro 64GB, mistral small 3.1 24b instruct 2503 hf achieves approximately 21.5 tokens per second decode speed with a time-to-first-token of 8989ms using Q4_K_M quantization.
Can MacBook Pro M4 Pro 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 M4 Pro 64GB receives a C grade with 21.5 tok/s and 134K context.
What context window can mistral small 3.1 24b instruct 2503 hf use on MacBook Pro M4 Pro 64GB?
On MacBook Pro M4 Pro 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 M4 Pro 64GB as fast as VRAM for mistral small 3.1 24b instruct 2503 hf?
Not always. MacBook Pro M4 Pro 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.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--mistral-small-3-1-24b-instruct-2503-hf-gguf-on-m4-pro-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: