Can I Run Llama 3.3 on NVIDIA GeForce RTX 3090?

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Superseded model. Llama 3.3 has been superseded by Llama 4.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 →

作者： Jakub Rusinowski · 最后更新： 2026年7月12日

Yes, comfortably — you'll have ~3.8 GB of headroom running Llama 3.3 70B Instruct at Q2_K_XS (Tight) (20.2 GB, ~25 tok/s (est.)).

See what else this hardware can run →

购买此硬件 NVIDIA GeForce RTX 3090 24GB — 24 GB VRAM · 350 W board power 立即云端部署 RunPod 上的 RTX 3090 — 低至 $0.46/小时 · 价格核实于 2026-08

或在 Vast.ai 比较

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NVIDIA GeForce RTX 3090 24GB

24 GB VRAM · 350 W board power

2026年价格波动较大——请以当前商品页价格为准。

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NVIDIA GeForce RTX 3090 Specs

VRAM	24 GB
Memory Bandwidth	936 GB/s

Llama 3.3 70B Instruct on the NVIDIA GeForce RTX 3090: VRAM by quantization

Quant	VRAM needed	Fits 24 GB?	Max context	Whole PC
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	✗ No	—	—
Q2_K	25.2 GB	✗ No	—	—

Assumes a 4K-token context with an f16 KV cache. A longer window needs more; a quantized KV cache needs less. “Max context” is the largest window that still fits in 24 GB. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.

Context costs VRAM too. At Q2_K_XS (Tight) on the NVIDIA GeForce RTX 3090: 8K 23.7 GB · 32K 31.7 GB ✗ · 128K 64 GB ✗. Past 32K it no longer fits 24 GB — a q8_0 KV cache buys roughly half of that back, which the calculator will price for you.

No compatible GPU? Llama 3.3 70B Instruct on 256 GB of system RAM, CPU only: runs, but too slowly to be worth it.