✅ PASST IN DEN VRAM (FP8 (W8A8 / E4M3))
Qwen2.5-72B-Instruct on NVIDIA A100 80GB SXM (80GB VRAM)
Unquantized FP16 (138.88 GB) exceeds 80 GB, but 4-bit Q4_K_M requires 44.38 GB (55% VRAM utilization) at ~31.9 tokens/sec.
🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator
📊 Echtzeit-Speicher- & Performance-Telemetrie
44.38 GB / 80 GB
🟦 Modellgewichte: 41.39 GB
🟪 KV-Cache: 2.5 GB
🟨 Engine-Puffer: 0.5 GB
Benötigter GPU-VRAM44.38 GB
Benötigter System-RAM (CPU)0.00 GB
Verfügbarer Hardware-VRAM80 GB (1x NVIDIA A100 80GB SXM)
VRAM-Reserve / Defizit35.62 GB
Geschätzte Decode-Geschwindigkeit~31.9 tokens/sec
Geschätzte Prefill-Geschwindigkeit~1116 tokens/sec
Time-to-First-Token (4K Prompt)~3671 ms
Transformer-TopologieDense-GQA (80L, 8/64 KV/Q heads)
🚀 Startbefehl (vLLM / llama.cpp / Ollama)
ollama run qwen2.5:72b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf Qwen/Qwen2.5-72B-Instruct-GGUF -c 8192 -ngl 99 -fa
CLOUD-GPU PREISVERGLEICH & MIETEN-VS-KAUFEN RECHNER
Nicht genug lokaler VRAM? Cloud-GPU in 60 Sekunden starten (Qwen2.5-72B-Instruct: 44.38 GB in 4-bit)
Recommended Cloud Tier: L40S / RTX 6000 Ada (48GB) · Full FP16 Needs: 138.88 GB
Amortisationszeit (Kauf vs. Miete): ~32 months (vs $14,500 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 → |
| Vast.ai | $0.29 / hr | $0.95 / hr | $1.79 / hr | $34.80 / mo | Save 50–70% on RTX 4090 / 5090 / A100 rentals | Spot-GPU mieten → |
| Vultr Cloud GPU | — | $1.79 / hr | $2.69 / hr | $214.80 / mo (A100) | $300 Free Credit | $300 Guthaben sichern → |
| DigitalOcean GPU | — | — | $2.99 / hr | $358.80 / mo (H100) | $200 Free Credit (60d) | $200 Startguthaben → |
Alle Quantisierungsstufen im Detail: Qwen2.5-72B-Instruct × NVIDIA A100 80GB SXM
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA A100 80GB SXM (80GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 135.43 GB | 2.5 GB | 138.43 GB | Nein (+58.4GB) | ~1.2 / 1116 tok/s | 2x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 69.91 GB | 2.5 GB | 72.9 GB | KNAPP (<12% RESERVE) | ~18.7 / 1116 tok/s | 1x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 71.95 GB | 2.5 GB | 74.95 GB | KNAPP (<12% RESERVE) | ~18.1 / 1116 tok/s | 1x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 55.53 GB | 2.5 GB | 58.53 GB | Ja (73%) | ~23.5 / 1116 tok/s | 1x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 48.42 GB | 2.5 GB | 51.41 GB | Ja (64%) | ~27.1 / 1116 tok/s | 1x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 41.39 GB | 2.5 GB | 44.38 GB | Ja (55%) | ~31.9 / 1116 tok/s | 1x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 36.34 GB | 2.5 GB | 39.33 GB | Ja (49%) | ~36.3 / 1116 tok/s | 1x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 33.41 GB | 2.5 GB | 36.41 GB | Ja (46%) | ~39.4 / 1116 tok/s | 1x |
Dieses Modell auf anderen GPUs testen
Qwen2.5-72B-Instruct on NVIDIA RTX 5090 (32GB)Qwen2.5-72B-Instruct on NVIDIA RTX 4090 (24GB)Qwen2.5-72B-Instruct on NVIDIA RTX 5080 (16GB)Qwen2.5-72B-Instruct on NVIDIA RTX 4080 Super (16GB)Qwen2.5-72B-Instruct on NVIDIA RTX 4070 Ti Super (16GB)Qwen2.5-72B-Instruct on NVIDIA RTX 3090 (24GB)Qwen2.5-72B-Instruct on NVIDIA RTX 4060 Ti 16GB (16GB)Qwen2.5-72B-Instruct on NVIDIA RTX 3060 12GB (12GB)Qwen2.5-72B-Instruct on NVIDIA RTX 6000 Ada (48GB)Qwen2.5-72B-Instruct on NVIDIA RTX A6000 (48GB)
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