← Can It Run? — GPU compatibility matrix · Local LLMs guide
Llama 3.3 70B needs 40 GB VRAM at Q4. The RTX 5090 32GB has 32 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 5090 32GB’s 32 GB of VRAM can hold it — here’s the exact math.
VRAM requirements
| Quantization | VRAM needed | RTX 5090 32GB |
|---|---|---|
| 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 5090 32GB has only 32 GB — you’re 8 GB short. You can offload layers to system RAM, but expect single-digit tokens/sec (painfully slow for chat).
About the RTX 5090 32GB
Architecture: Blackwell · VRAM: 32 GB · Price: ~$2,000+.
Good: 32GB + Blackwell speed — runs 70B at Q4 on a single card
Watch out: Very expensive, high power draw, hard to buy at MSRP
Check current RTX 5090 32GB price on Amazon →
Related checks
Other models on the RTX 5090 32GB
🎯 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 5090 32GB 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 5090 32GB has 32 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 5090 32GB 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.