RTX PRO 5000 Blackwell 48GBコスパ重視1344 GB/s (+544)
C
Raises estimated decode speed by about 143%.77.1 tok/sデコード
Raises estimated decode speed by about 143%.
〜$4,999 MSRP
Can mistral small 3.1 24b instruct 2503 hf run on Mac Studio M2 Ultra 64GB?
YES — Runs Great
C50Usable
Estimated from fit model
mistral small 3.1 24b instruct 2503 hf needs ~25.3 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~32 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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, 31.7 tok/s, Runs well
25.3 GB required46.1 GB available
55% VRAM used
Fit status
Runs well
Decode
31.7 tok/s
TTFT
6108 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 Mac Studio M2 Ultra 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: 31.7 tok/s decode · 6.1s TTFT (warm) · 79 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 | 31.7 tok/s | 3332 ms | 134K |
| Coding | C | Runs well | 31.7 tok/s | 6108 ms | 134K |
| Agentic Coding | C | Runs well | 31.7 tok/s | 8885 ms | 134K |
| Reasoning | C | Runs well | 31.7 tok/s | 7219 ms | 134K |
| RAG | C | Runs well | 31.7 tok/s | 11106 ms | 134K |
Quantization options
How mistral small 3.1 24b instruct 2503 hf (24B params) fits at each quantization level on Mac Studio M2 Ultra 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アップグレードオプション
mistral small 3.1 24b instruct 2503 hfを快適に動かすハードウェア
Frequently asked questions
Can Mac Studio M2 Ultra 64GB run mistral small 3.1 24b instruct 2503 hf?
Yes, Mac Studio M2 Ultra 64GB can run mistral small 3.1 24b instruct 2503 hf with a C grade (Runs well). Expected decode speed: 31.7 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 Mac Studio M2 Ultra 64GB?
On Mac Studio M2 Ultra 64GB, mistral small 3.1 24b instruct 2503 hf achieves approximately 31.7 tokens per second decode speed with a time-to-first-token of 6108ms using Q4_K_M quantization.
Can Mac Studio M2 Ultra 64GB run mistral small 3.1 24b instruct 2503 hf for coding?
For coding workloads, mistral small 3.1 24b instruct 2503 hf on Mac Studio M2 Ultra 64GB receives a C grade with 31.7 tok/s and 134K context.
What context window can mistral small 3.1 24b instruct 2503 hf use on Mac Studio M2 Ultra 64GB?
On Mac Studio M2 Ultra 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 Mac Studio M2 Ultra 64GB as fast as VRAM for mistral small 3.1 24b instruct 2503 hf?
Not always. Mac Studio M2 Ultra 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-m2-ultra-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: