Autor: Jakub Rusinowski · Ostatnia aktualizacja: 11 września 2026
Not at Q4_K_M — that needs 19.3 GB and the NVIDIA GeForce RTX 4070 Ti Super has 16 GB. Drop to Q2_K and it fits in 13.5 GB, with up to 8K of context.
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| Model | Qwen 2.5 Coder 32B |
| Parameters | 32B |
| Quantization | Q4_K_M |
| VRAM needed | 19.3 GB |
| NVIDIA GeForce RTX 4070 Ti Super VRAM | 16 GB |
| Verdict | Runs at a lower quant |
| Fits at | Q2_K (13.5 GB) |
| Max context | 8K |
| Quant | VRAM needed | Fits 16 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 65.9 GB | ✗ No | — | — |
| Q8_0 | 35.9 GB | ✗ No | — | — |
| Q6_K | 28.1 GB | ✗ No | — | — |
| Q5_K_M | 24.6 GB | ✗ No | — | — |
| Q4_K_M | 21.2 GB | ✗ No | — | — |
| Q3_K_M | 15.5 GB | ✓ Yes | 4K | — |
| Q2_K | 12.4 GB | ✓ Yes | 16K | — |
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 16 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 Q4_K_M on the NVIDIA GeForce RTX 4070 Ti Super: 8K 22.3 GB ✗ · 32K 28.7 GB ✗ · 128K 54.5 GB ✗. Past 8K it no longer fits 16 GB — a q8_0 KV cache buys roughly half of that back, which the calculator will price for you.
Quantize to Q2_K. That is the cheapest fix — it costs some quality but no money.
No compatible GPU? Qwen 2.5 Coder 32B on 32 GB of system RAM, CPU only: runs comfortably.
Running Qwen 2.5 Coder 32B on this card:
Qwen 2.5 Coder 32B needs ~19 GB but this GPU has 16 GB. Rent a RTX 4090 (24 GB)-class GPU by the hour instead of buying one:
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Cloud rates verified 2026-07 — estimates, and marketplace prices vary. Buying price is GPU MSRP only, not a full PC.
All Qwen 2.5 Family sizes on the NVIDIA GeForce RTX 4070 Ti Super | Qwen 2.5 Coder 32B model page | NVIDIA GeForce RTX 4070 Ti Super GPU page | Check your hardware