✅ PASST IN DEN VRAM (FP8 (W8A8 / E4M3))
Llama-3.1-405B-Instruct on 8x NVIDIA H100 80GB (640GB) (640GB VRAM)
Unquantized FP16 (759.28 GB) exceeds 640 GB, but 4-bit Q4_K_M requires 233.02 GB (36% VRAM utilization) at ~9.4 tokens/sec.
🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator
📊 Echtzeit-Speicher- & Performance-Telemetrie
233.02 GB / 640 GB
🟦 Modellgewichte: 228.57 GB
🟪 KV-Cache: 3.94 GB
🟨 Engine-Puffer: 0.52 GB
Benötigter GPU-VRAM233.02 GB
Benötigter System-RAM (CPU)0.00 GB
Verfügbarer Hardware-VRAM640 GB (1x 8x NVIDIA H100 80GB (640GB))
VRAM-Reserve / Defizit406.98 GB
Geschätzte Decode-Geschwindigkeit~9.4 tokens/sec
Geschätzte Prefill-Geschwindigkeit~5079 tokens/sec
Time-to-First-Token (4K Prompt)~806 ms
Transformer-TopologieDense-GQA (126L, 8/128 KV/Q heads)
🚀 Startbefehl (vLLM / llama.cpp / Ollama)
ollama run llama3.1:405b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-3.1-405B-Instruct-GGUF -c 8192 -ngl 99 -fa
CLOUD-GPU PREISVERGLEICH & MIETEN-VS-KAUFEN RECHNER
Nicht genug lokaler VRAM? Cloud-GPU in 60 Sekunden starten (Llama-3.1-405B-Instruct: 233.02 GB in 4-bit)
Recommended Cloud Tier: Multi-GPU H100 / H200 Node · Full FP16 Needs: 759.28 GB
Amortisationszeit (Kauf vs. Miete): ~32 months (vs $230,000 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: Llama-3.1-405B-Instruct × 8x NVIDIA H100 80GB (640GB)
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x 8x NVIDIA H100 80GB (640GB) (640GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 754.37 GB | 3.94 GB | 758.82 GB | Nein (+118.8GB) | ~1.2 / 5079 tok/s | 2x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 389.1 GB | 3.94 GB | 393.55 GB | Ja (61%) | ~5.5 / 5079 tok/s | 1x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 400.76 GB | 3.94 GB | 405.21 GB | Ja (63%) | ~5.3 / 5079 tok/s | 1x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 309.29 GB | 3.94 GB | 313.74 GB | Ja (49%) | ~6.9 / 5079 tok/s | 1x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 268.7 GB | 3.94 GB | 273.15 GB | Ja (43%) | ~8 / 5079 tok/s | 1x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 228.57 GB | 3.94 GB | 233.02 GB | Ja (36%) | ~9.4 / 5079 tok/s | 1x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 200.99 GB | 3.94 GB | 205.44 GB | Ja (32%) | ~10.7 / 5079 tok/s | 1x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 184.88 GB | 3.94 GB | 189.34 GB | Ja (30%) | ~11.6 / 5079 tok/s | 1x |
Dieses Modell auf anderen GPUs testen
Llama-3.1-405B-Instruct on NVIDIA RTX 5090 (32GB)Llama-3.1-405B-Instruct on NVIDIA RTX 4090 (24GB)Llama-3.1-405B-Instruct on NVIDIA RTX 5080 (16GB)Llama-3.1-405B-Instruct on NVIDIA RTX 4080 Super (16GB)Llama-3.1-405B-Instruct on NVIDIA RTX 4070 Ti Super (16GB)Llama-3.1-405B-Instruct on NVIDIA RTX 3090 (24GB)Llama-3.1-405B-Instruct on NVIDIA RTX 4060 Ti 16GB (16GB)Llama-3.1-405B-Instruct on NVIDIA RTX 3060 12GB (12GB)Llama-3.1-405B-Instruct on NVIDIA RTX 6000 Ada (48GB)Llama-3.1-405B-Instruct on NVIDIA RTX A6000 (48GB)
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