mistral small 3.1 24b instruct 2503 hf needs ~24.8 GB VRAM. AMD Instinct MI210 64GB has 64.0 GB. With Q4_K_M quantization, expect ~76 tok/s.
Operating mode
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
Fit status
Runs well
Decode
76.1 tok/s
TTFT
2545 ms
Safe context
239K
Memory
24.8 GB / 64.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 76.1 tok/s | 1388 ms | 239K |
| Coding | C | Runs well | 76.1 tok/s | 2545 ms | 239K |
| Agentic Coding | C | Runs well | 76.1 tok/s | 3701 ms | 239K |
| Reasoning | C | Runs well | 76.1 tok/s | 3007 ms | 239K |
| RAG | C | Runs well | 76.1 tok/s | 4627 ms | 239K |
Inference speed
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.
How mistral small 3.1 24b instruct 2503 hf (24B params) fits at each quantization level on AMD Instinct MI210 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | C41 |
Q3_K_S | 3 | 11.8 GB | Low | C42 |
NVFP4 | 4 | 13.4 GB | Medium | C42 |
Q4_K_M | 4 | 14.6 GB | Medium | C42 |
Q5_K_M | 5 | 17.3 GB | High | C43 |
Q6_K | 6 | 19.7 GB | High | C43 |
Q8_0 | 8 | 25.7 GB | Very High | C45 |
F16Best for your GPU | 16 | 49.2 GB | Maximum | C48 |
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 startYes, AMD Instinct MI210 64GB can run mistral small 3.1 24b instruct 2503 hf with a C grade (Runs well). Expected decode speed: 76.1 tok/s.
mistral small 3.1 24b instruct 2503 hf (24B parameters) requires approximately 24.8 GB of memory with Q4_K_M quantization.
The recommended quantization for mistral small 3.1 24b instruct 2503 hf is Q4_K_M, which balances quality and memory efficiency.
On AMD Instinct MI210 64GB, mistral small 3.1 24b instruct 2503 hf achieves approximately 76.1 tokens per second decode speed with a time-to-first-token of 2545ms using Q4_K_M quantization.
For coding workloads, mistral small 3.1 24b instruct 2503 hf on AMD Instinct MI210 64GB receives a C grade with 76.1 tok/s and 239K context.
On AMD Instinct MI210 64GB, mistral small 3.1 24b instruct 2503 hf can safely use up to 239K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--mistral-small-3-1-24b-instruct-2503-hf-gguf-on-instinct-mi210-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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