RTX 5090 32GBコスパ重視32 GB VRAM (+8)1792 GB/s (+1192)
B
Raises estimated decode speed by about 156%.82 tok/sデコード
Raises estimated decode speed by about 156%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Can Mistral Small 3.2 24B Instruct 2506 run on NVIDIA A10 24GB?
YES — Tight Fit
C51Usable
Estimated from fit model
Mistral Small 3.2 24B Instruct 2506 needs ~21.1 GB VRAM. NVIDIA A10 24GB has 24.0 GB. With Q4_K_M quantization, expect ~32 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: 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) — 21.1 GB, 32.0 tok/s, Tight fit
21.1 GB required24.0 GB available
88% VRAM used
Fit status
Tight fit
Decode
32.0 tok/s
TTFT
6056 ms
Safe context
33K
Memory
21.1 GB / 24.0 GB
Memory breakdown
Weights14.6 GB
KV Cache2.8 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsMistral Small 3.2 24B Instruct 2506 on NVIDIA A10 24GB
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: 32.0 tok/s decode · 6.1s TTFT (warm) · 80 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 32.0 tok/s | 3303 ms | 33K |
| Coding | C | Tight fit | 32.0 tok/s | 6056 ms | 33K |
| Agentic Coding | C | Runs with offload | 32.0 tok/s | 8809 ms | 33K |
| Reasoning | C | Tight fit | 32.0 tok/s | 7157 ms | 33K |
| RAG | C | Runs with offload | 32.0 tok/s | 11011 ms | 33K |
Quantization options
How Mistral Small 3.2 24B Instruct 2506 (24B params) fits at each quantization level on NVIDIA A10 24GB (24.0 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 3.2 24B Instruct 2506 on your machine.
Run
lms load hf-unsloth--mistral-small-3-2-24b-instruct-2506-gguf && lms server startアップグレードオプション
Mistral Small 3.2 24B Instruct 2506を快適に動かすハードウェア
RTX 5090 32GBコスパ重視RTX PRO 4500 Blackwell 32GBコスパ最良32 GB VRAM (+8)896 GB/s (+296)
C
Raises estimated decode speed by about 61%.51.4 tok/sデコード
Raises estimated decode speed by about 61%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
RTX 5000 Ada 32GBNVIDIAアップグレード32 GB VRAM (+8)
C
Adds memory headroom for longer context windows and future model growth.31.5 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$4,000 MSRP
Frequently asked questions
Can NVIDIA A10 24GB run Mistral Small 3.2 24B Instruct 2506?
Yes, NVIDIA A10 24GB can run Mistral Small 3.2 24B Instruct 2506 with a C grade (Tight fit). Expected decode speed: 32.0 tok/s.
How much VRAM does Mistral Small 3.2 24B Instruct 2506 need?
Mistral Small 3.2 24B Instruct 2506 (24B parameters) requires approximately 21.1 GB of memory with Q4_K_M quantization.
What is the best quantization for Mistral Small 3.2 24B Instruct 2506?
The recommended quantization for Mistral Small 3.2 24B Instruct 2506 is Q4_K_M, which balances quality and memory efficiency.
What speed will Mistral Small 3.2 24B Instruct 2506 run at on NVIDIA A10 24GB?
On NVIDIA A10 24GB, Mistral Small 3.2 24B Instruct 2506 achieves approximately 32.0 tokens per second decode speed with a time-to-first-token of 6056ms using Q4_K_M quantization.
Can NVIDIA A10 24GB run Mistral Small 3.2 24B Instruct 2506 for coding?
For coding workloads, Mistral Small 3.2 24B Instruct 2506 on NVIDIA A10 24GB receives a C grade with 32.0 tok/s and 33K context.
What context window can Mistral Small 3.2 24B Instruct 2506 use on NVIDIA A10 24GB?
On NVIDIA A10 24GB, Mistral Small 3.2 24B Instruct 2506 can safely use up to 33K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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