← Can It Run? — GPU compatibility matrix · Local LLMs guide
Llama 3.1 8B needs 5 GB VRAM at Q4. The RTX 4070 Ti Super 16GB has 16 GB.
🦙 Llama 3.1 8B (Meta) is a 8B-parameter model for general chat, coding, instruction following. Strong all-rounder, comparable to GPT-3.5. 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) | 5 GB | ✅ |
| Q8_0 (near-lossless) | 8.5 GB | ✅ |
Expected performance
Running Llama 3.1 8B at Q4 on the RTX 4070 Ti Super 16GB, expect roughly ~68 tokens/sec in Ollama or llama.cpp — comfortably faster than reading speed, so chat feels responsive.
Headroom: 5 GB used of 16 GB leaves about 11 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
Enter your exact GPU + RAM, see every model you can run.
💾 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 Llama 3.1 8B?
Yes — runs at Q4 and full Q8 quality. Llama 3.1 8B needs 5 GB VRAM at Q4_K_M; the RTX 4070 Ti Super 16GB has 16 GB.
How much VRAM does Llama 3.1 8B need?
Llama 3.1 8B needs about 5 GB at Q4_K_M (recommended) and 8.5 GB at Q8_0.
How many tokens per second will the RTX 4070 Ti Super 16GB get on Llama 3.1 8B?
Roughly ~68 tokens/sec at Q4 in Ollama or llama.cpp.