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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.