Qwen2.5-Coder-32B-Instruct VRAM Requirements & GPU Benchmarks
Qwen2.5-Coder-32B-Instruct (32.76B total, 32.76B active, 64L, 8 KV heads, vocab 152064) requires 21.23 GB in 4-bit Q4_K_M, 34 GB in FP8, and 63.51 GB in FP16 at 8K context.
CLOUD-GPU PREISVERGLEICH & MIETEN-VS-KAUFEN RECHNER
Nicht genug lokaler VRAM? Cloud-GPU in 60 Sekunden starten (Qwen2.5-Coder-32B-Instruct: 21.23 GB in 4-bit)
Recommended Cloud Tier: RTX 4090 (24GB) Β· Full FP16 Needs: 63.51 GB
Amortisationszeit (Kauf vs. Miete): ~32 months (vs $1,699 hardware)
| Cloud Provider | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Est. Monthly Cost (4h/day) | Free Credit / Trial | Deploy |
|---|---|---|---|---|---|---|
| RunPod | $0.44 / hr | $1.39 / hr | $2.39 / hr | $52.80 / mo | Instant PyTorch / vLLM Templates + Serverless GPU | Auf RunPod starten β |
| Vast.ai | $0.29 / hr | $0.95 / hr | $1.79 / hr | $34.80 / mo | Save 50β70% on RTX 4090 / 5090 / A100 rentals | Spot-GPU mieten β |
| Vultr Cloud GPU | β | $1.79 / hr | $2.69 / hr | $214.80 / mo (A100) | $300 Free Credit | $300 Guthaben sichern β |
| DigitalOcean GPU | β | β | $2.99 / hr | $358.80 / mo (H100) | $200 Free Credit (60d) | $200 Startguthaben β |
| GPU Hardware | VRAM | Bandwidth | FP16 (63.51GB) | Q4_K_M (21.23GB) | Decode / Prefill Speed | Report |
|---|---|---|---|---|---|---|
| NVIDIA RTX 5090 | 32 GB | 1792 GB/s | Nein | Ja (66%) | ~62.3 / 1663 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX 4090 | 24 GB | 1008 GB/s | Nein | Ja (88%) | ~35 / 1311 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX 5080 | 16 GB | 960 GB/s | Nein | Nein | ~9.5 / 894 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX 4080 Super | 16 GB | 736 GB/s | Nein | Nein | ~8.9 / 829 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX 4070 Ti Super | 16 GB | 672 GB/s | Nein | Nein | ~8.7 / 700 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX 3090 | 24 GB | 936 GB/s | Nein | Ja (88%) | ~32.5 / 563 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX 4060 Ti 16GB | 16 GB | 288 GB/s | Nein | Nein | ~6.3 / 350 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX 3060 12GB | 12 GB | 360 GB/s | Nein | Nein | ~5.2 / 202 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX 6000 Ada | 48 GB | 960 GB/s | Nein | Ja (44%) | ~33.4 / 1446 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA RTX A6000 | 48 GB | 768 GB/s | Nein | Ja (44%) | ~26.7 / 614 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA L40S 48GB | 48 GB | 864 GB/s | Nein | Ja (44%) | ~30 / 2873 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA A100 80GB SXM | 80 GB | 2039 GB/s | Ja | Ja (27%) | ~70.8 / 2476 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA H100 80GB SXM | 80 GB | 3350 GB/s | Ja | Ja (27%) | ~116.4 / 7849 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA H200 141GB SXM | 141 GB | 4800 GB/s | Ja | Ja (15%) | ~166.8 / 7849 tok/s | Exakten VRAM & Speed berechnen β |
| NVIDIA Blackwell B200 192GB | 192 GB | 8000 GB/s | Ja | Ja (11%) | ~278 / 17857 tok/s | Exakten VRAM & Speed berechnen β |
| AMD Instinct MI300X 192GB | 192 GB | 5300 GB/s | Ja | Ja (11%) | ~184.1 / 10373 tok/s | Exakten VRAM & Speed berechnen β |
| 2x NVIDIA RTX 3090 (48GB) | 48 GB | 936 GB/s | Nein | Ja (44%) | ~32.5 / 1127 tok/s | Exakten VRAM & Speed berechnen β |
| 2x NVIDIA RTX 4090 (48GB) | 48 GB | 1008 GB/s | Nein | Ja (44%) | ~35 / 2622 tok/s | Exakten VRAM & Speed berechnen β |
| 4x NVIDIA A100 80GB (320GB) | 320 GB | 2039 GB/s | Ja | Ja (7%) | ~70.8 / 9905 tok/s | Exakten VRAM & Speed berechnen β |
| 8x NVIDIA H100 80GB (640GB) | 640 GB | 3350 GB/s | Ja | Ja (3%) | ~116.4 / 18000 tok/s | Exakten VRAM & Speed berechnen β |
| Apple M4 Max (128GB Unified) | 128 GB | 546 GB/s | Ja | Ja (17%) | ~20.7 / 292 tok/s | Exakten VRAM & Speed berechnen β |
| Apple M3 Ultra Mac Studio (512GB) | 512 GB | 819 GB/s | Ja | Ja (4%) | ~31.1 / 430 tok/s | Exakten VRAM & Speed berechnen β |
| Apple M4 Pro Mac Mini (64GB) | 64 GB | 273 GB/s | Ja | Ja (33%) | ~10.4 / 146 tok/s | Exakten VRAM & Speed berechnen β |