Can I Run Llama 3.3 on AMD Radeon RX 7900 XTX?
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 AMD Radeon RX 7900 XTX's 24 GB, at ~26 tok/s (est.).
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AMD Radeon RX 7900 XTX Specs
| VRAM | 24 GB |
| Memory Bandwidth | 960 GB/s |
Llama 3.3 70B Instruct on the AMD Radeon RX 7900 XTX: 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 AMD Radeon RX 7900 XTX: 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 AMD Radeon RX 7900 XTX (at 8K context)
| Llama 3.3 70B Instruct | Q2_K_XS (Tight) · 23.7 GB at 8K context · ~26 tok/s (est.) |
At 2 hrs/day, buying (~$999) beats renting at $0.34/hr after about 4.1 years.
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FAQ
Is the AMD Radeon RX 7900 XTX 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 AMD Radeon RX 7900 XTX's 24 GB, at ~26 tok/s (est.).
What's the best Llama 3.3 size for the AMD Radeon RX 7900 XTX?
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 ~26 tokens/sec.
Llama 3.3 on Other GPUs
- Llama 3.3 on NVIDIA GeForce RTX 4090
- Llama 3.3 on NVIDIA GeForce RTX 3090
- Llama 3.3 on Apple M4 Pro
- Llama 3.3 on Apple M3
- Llama 3.3 on Apple M2
Popular Models on the AMD Radeon RX 7900 XTX
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VRAM Tier
Troubleshooting
- Ollama: "model requires more system memory than is available"
- Which GGUF quant should I download? (Q4 vs Q5 vs Q8)
Buying Guide
← Can I Run It? | Llama 3.3 Model Page | AMD Radeon RX 7900 XTX GPU Page | VRAM calculator | Check Your Hardware