← Can It Run? — GPU compatibility matrix · Local LLMs guide
Llama 3.3 70B needs 40 GB VRAM at Q4. The RTX 4060 8GB has 8 GB.
🦙 Llama 3.3 70B (Meta) is a 70B-parameter model for near-GPT-4 quality for complex tasks. Frontier-class open model, best for demanding use cases. The question is whether the RTX 4060 8GB’s 8 GB of VRAM can hold it — here’s the exact math.
VRAM requirements
| Quantization | VRAM needed | RTX 4060 8GB |
|---|---|---|
| Q4_K_M (recommended) | 40 GB | ❌ |
| Q8_0 (near-lossless) | 72 GB | ❌ |
Why it won’t fit (and what to do)
Llama 3.3 70B needs 40 GB at Q4 but the RTX 4060 8GB has only 8 GB — you’re 32 GB short. You can offload layers to system RAM, but expect single-digit tokens/sec (painfully slow for chat).
About the RTX 4060 8GB
Architecture: Ada · VRAM: 8 GB · Price: ~$300.
Good: Cheap, efficient Ada card, DLSS 3, low power draw
Watch out: Only 8GB VRAM — caps you at 7-8B models
Check current RTX 4060 8GB price on Amazon →
Related checks
Other models on the RTX 4060 8GB
🎯 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 4060 8GB run Llama 3.3 70B?
No — not enough VRAM without CPU offloading. Llama 3.3 70B needs 40 GB VRAM at Q4_K_M; the RTX 4060 8GB has 8 GB.
How much VRAM does Llama 3.3 70B need?
Llama 3.3 70B needs about 40 GB at Q4_K_M (recommended) and 72 GB at Q8_0.
How many tokens per second will the RTX 4060 8GB get on Llama 3.3 70B?
It won't fit in VRAM; with CPU offloading expect very slow single-digit tokens/sec.
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.