Can I Run Llama 3.3 on NVIDIA GeForce RTX 3090?
Written by Jakub Rusinowski · Last updated July 12, 2026
Yes, but it's tight — Llama 3.3 70B Instruct at Q2_K_XS (Tight) needs 23.7 GB at 8K context (20.2 GB weights + 3.5 GB KV cache/overhead), leaving ~0.3 GB of the NVIDIA GeForce RTX 3090's 24 GB, at ~25 tok/s (est.).
See what else this hardware can run →
As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.
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 | 143.5 GB | ✗ No | — | — |
| Q8_0 | 77.9 GB | ✗ No | — | — |
| Q6_K | 60.9 GB | ✗ No | — | — |
| Q5_K_M | 53.1 GB | ✗ No | — | — |
| Q4_K_M | 45.7 GB | ✗ No | — | — |
| Q3_K_M | 33.3 GB | ✗ No | — | — |
| Q2_K | 26.5 GB | ✗ No | — | — |
Assumes an 8K-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.
Llama 3.3 Sizes That Fit the NVIDIA GeForce RTX 3090 (at 8K context)
| Llama 3.3 70B Instruct | Q2_K_XS (Tight) · 23.7 GB at 8K context · ~25 tok/s (est.) |
At 2 hrs/day, buying (~$1,499) beats renting at $0.34/hr after about 6.1 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.
FAQ
Is the NVIDIA GeForce RTX 3090 enough to run Llama 3.3 locally?
Yes, but it's tight — Llama 3.3 70B Instruct at Q2_K_XS (Tight) needs 23.7 GB at 8K context (20.2 GB weights + 3.5 GB KV cache/overhead), leaving ~0.3 GB of the NVIDIA GeForce RTX 3090's 24 GB, at ~25 tok/s (est.).
What's the best Llama 3.3 size for the NVIDIA GeForce RTX 3090?
Llama 3.3 70B Instruct at Q2_K_XS (Tight) quantization needs 23.7 GB at 8K context (20.2 GB weights + 3.5 GB KV cache/overhead), estimated ~25 tokens/sec.
Llama 3.3 on Other GPUs
- Llama 3.3 on NVIDIA GeForce RTX 4090
- Llama 3.3 on AMD Radeon RX 7900 XTX
- Llama 3.3 on Apple M4 Pro
- Llama 3.3 on Apple M3
- Llama 3.3 on Apple M2
Popular Models on the NVIDIA GeForce RTX 3090
- DeepSeek R1 on NVIDIA GeForce RTX 3090
- Qwen 3 on NVIDIA GeForce RTX 3090
- Qwen 2.5 Family on NVIDIA GeForce RTX 3090
VRAM Tier
Troubleshooting
- CUDA out of memory — why it happens and how to fix it
- Which GGUF quant should I download? (Q4 vs Q5 vs Q8)
Buying Guide
← Can I Run It? | Llama 3.3 Model Page | NVIDIA GeForce RTX 3090 GPU Page | VRAM calculator | Check Your Hardware