Can I Run Llama 3.3 on NVIDIA RTX 6000 Ada Generation?
Written by Jakub Rusinowski · Last updated July 21, 2026
Yes, comfortably — 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 ~24.3 GB of the NVIDIA RTX 6000 Ada Generation's 48 GB, at ~38 tok/s (est.).
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NVIDIA RTX 6000 Ada Generation Specs
| VRAM | 48 GB |
| Memory Bandwidth | 960 GB/s |
Llama 3.3 70B Instruct on the NVIDIA RTX 6000 Ada Generation: VRAM by quantization
| Quant | VRAM needed | Fits 48 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 | ✓ Yes | 8K | — |
| Q3_K_M | 33.3 GB | ✓ Yes | 32K | — |
| Q2_K | 26.5 GB | ✓ Yes | 64K | — |
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 48 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 RTX 6000 Ada Generation: 8K 23.7 GB · 32K 31.7 GB · 128K 64 GB ✗. Past 128K it no longer fits 48 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 RTX 6000 Ada Generation (at 8K context)
| Llama 3.3 70B Instruct | Q2_K_XS (Tight) · 23.7 GB at 8K context · ~38 tok/s (est.) |
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FAQ
Does the NVIDIA RTX 6000 Ada Generation have enough VRAM for Llama 3.3?
Yes, comfortably — 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 ~24.3 GB of the NVIDIA RTX 6000 Ada Generation's 48 GB, at ~38 tok/s (est.).
Which quantization of Llama 3.3 should I use on the NVIDIA RTX 6000 Ada Generation?
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 ~38 tokens/sec.
Llama 3.3 on Other GPUs
- Llama 3.3 on NVIDIA L40S
- Llama 3.3 on Apple M3 Pro
- Llama 3.3 on NVIDIA GeForce RTX 5090
- Llama 3.3 on Apple M4
- Llama 3.3 on Apple M2 Pro
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VRAM Tier
Troubleshooting
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