Can I Run Llama 3.3 on NVIDIA GeForce RTX 5090?

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

Superseded model. Llama 3.3 has been superseded by Llama 4. This page is kept for reference; the newer family is a better starting point. View Llama 4 →

Written by Jakub Rusinowski · Last updated July 12, 2026

Yes, comfortably — you'll have ~11.8 GB of headroom running Llama 3.3 70B Instruct at Q2_K_XS (Tight) (20.212500000000002 GB, ~61 tok/s (est.)).

See what else this hardware can run →

Buy This HardwareNVIDIA GeForce RTX 5090 32GB — 32 GB VRAM · 575 W board power Deploy in the Cloud NowRTX 5090 on RunPod

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NVIDIA GeForce RTX 5090 32GB

32 GB VRAM · 575 W board power

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NVIDIA GeForce RTX 5090 Specs

VRAM	32 GB
Memory Bandwidth	1792 GB/s

Llama 3.3 70B Instruct on the NVIDIA GeForce RTX 5090: VRAM by quantization

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.