Autor: Jakub Rusinowski · Ostatnia aktualizacja: 12 lipca 2026
Yes, comfortably — you'll have ~3.8 GB of headroom running Llama 3.3 70B Instruct at Q2_K_XS (Tight) (20.2 GB, ~40 tok/s (est.)).
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| VRAM | 24 GB |
| Memory Bandwidth | 1008 GB/s |
| 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 4090: 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 70B Instruct | Q2_K_XS (Tight) · 20.2 GB · ~40 tok/s (est.) |
At 2 hrs/day, buying (~$1,599) beats renting at $0.34/hr after about 6.5 years.
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Cloud rates verified 2026-07 — estimates, and marketplace prices vary. Buying price is GPU MSRP only, not a full PC.
Yes, comfortably — you'll have ~3.8 GB of headroom running Llama 3.3 70B Instruct at Q2_K_XS (Tight) (20.2 GB, ~40 tok/s (est.)).
Llama 3.3 70B Instruct at Q2_K_XS (Tight) quantization (20.2 GB), estimated ~40 tokens/sec.
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