作者： Jakub Rusinowski · 最后更新： 2024年12月8日
Yes, comfortably — you'll have ~6 GB of headroom running Llama 3.3 70B Instruct at Q2_K_XS (Tight) (26 GB, ~69 tok/s (est.)).
在亚马逊查看价格 — NVIDIA GeForce RTX 5090 32GB
| VRAM | 32 GB |
| Memory Bandwidth | 1792 GB/s |
| Quant | VRAM needed | Fits 32 GB? | Max context |
|---|---|---|---|
| F16 | 142.1 GB | ✗ No | — |
| Q8_0 | 76.5 GB | ✗ No | — |
| Q6_K | 59.5 GB | ✗ No | — |
| Q5_K_M | 51.8 GB | ✗ No | — |
| Q4_K_M | 44.4 GB | ✗ No | — |
| Q3_K_M | 32 GB | ✓ Yes | 4K |
| Q2_K | 25.2 GB | ✓ Yes | 16K |
VRAM needed assumes a 4K-token context with an f16 KV cache; “Max context” is the largest window that still fits in 32 GB. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.
| Llama 3.3 70B Instruct | Q2_K_XS (Tight) · 26 GB · ~69 tok/s (est.) |
At 2 hrs/day, buying (~$1,999) beats renting at $0.55/hr after about 5.0 years.
Affiliate links — we may earn a commission if you sign up, at no extra cost to you.
Cloud rates verified 2026-07 — estimates, and marketplace prices vary. Buying price is GPU MSRP only, not a full PC.
Yes, comfortably — you'll have ~6 GB of headroom running Llama 3.3 70B Instruct at Q2_K_XS (Tight) (26 GB, ~69 tok/s (est.)).
Llama 3.3 70B Instruct at Q2_K_XS (Tight) quantization (26 GB), estimated ~69 tokens/sec.
← Can I Run It? | Llama 3.3 Model Page | NVIDIA GeForce RTX 5090 GPU Page | Check Your Hardware