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Mistral Small 3 24B needs 14 GB VRAM at Q4. The RTX 4070 Ti Super 16GB has 16 GB.
🌀 Mistral Small 3 24B (Mistral) is a 24B-parameter model for fast general chat, low-latency assistants. Near-large-model quality with fast, efficient inference. The question is whether the RTX 4070 Ti Super 16GB’s 16 GB of VRAM can hold it — here’s the exact math.
VRAM requirements
| Quantization | VRAM needed | RTX 4070 Ti Super 16GB |
|---|---|---|
| Q4_K_M (recommended) | 14 GB | ✅ |
| Q8_0 (near-lossless) | 25 GB | ❌ |
Expected performance
Running Mistral Small 3 24B at Q4 on the RTX 4070 Ti Super 16GB, expect roughly ~20 tokens/sec in Ollama or llama.cpp — comfortably faster than reading speed, so chat feels responsive.
Headroom: 14 GB used of 16 GB leaves about 2 GB free for KV cache (context). That’s fine for 4-8K context; for 32K+ you may need a smaller batch or a lower quant.
About the RTX 4070 Ti Super 16GB
Architecture: Ada · VRAM: 16 GB · Price: ~$800.
Good: 16GB with a wide bus — fast for 14B and squeezes 27B at Q4
Watch out: Pricey for 16GB; 24GB cards are better value for big models
Check current RTX 4070 Ti Super 16GB price on Amazon →
Related checks
Other models on the RTX 4070 Ti Super 16GB
🎯 LLM Hardware Checker
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💾 VRAM Requirements
Exact VRAM per model at Q4/Q5/Q8/FP16 with context scaling.
Frequently asked questions
Can the RTX 4070 Ti Super 16GB run Mistral Small 3 24B?
Yes — runs great at Q4 (recommended). Mistral Small 3 24B needs 14 GB VRAM at Q4_K_M; the RTX 4070 Ti Super 16GB has 16 GB.
How much VRAM does Mistral Small 3 24B need?
Mistral Small 3 24B needs about 14 GB at Q4_K_M (recommended) and 25 GB at Q8_0.
How many tokens per second will the RTX 4070 Ti Super 16GB get on Mistral Small 3 24B?
Roughly ~20 tokens/sec at Q4 in Ollama or llama.cpp.
About the speed figure. The tokens/sec number above is an estimate, not a measured benchmark. Real throughput depends on your runtime and backend (Ollama, llama.cpp, vLLM), the exact quantization you download, context length, memory bandwidth, and whether any layers are offloaded to CPU. Treat it as a rough guide to the tier of performance, not a promised result.