❌ ÜBERSCHREITET EINZEL-GPU VRAM (NEEDS 5x NVIDIA RTX 5080)
Mistral-Large-2-123B on NVIDIA RTX 5080 (16GB VRAM)
You need 5x NVIDIA RTX 5080 (80 GB total VRAM) for full GPU execution, or rent an A100/H100 80GB cloud instance.
🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator
📊 Echtzeit-Speicher- & Performance-Telemetrie
72.84 GB / 16 GB
🟦 Modellgewichte: 69.59 GB
🟪 KV-Cache: 2.75 GB
🟨 Engine-Puffer: 0.51 GB
Benötigter GPU-VRAM72.84 GB
Benötigter System-RAM (CPU)0.00 GB
Verfügbarer Hardware-VRAM16 GB (1x NVIDIA RTX 5080)
VRAM-Reserve / Defizit-56.84 GB
Geschätzte Decode-Geschwindigkeit~1.2 tokens/sec
Geschätzte Prefill-Geschwindigkeit~239 tokens/sec
Time-to-First-Token (4K Prompt)~17138 ms
Transformer-TopologieDense-GQA (88L, 8/96 KV/Q heads)
🚀 Startbefehl (vLLM / llama.cpp / Ollama)
ollama run mistral-large:123b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf mistralai/Mistral-Large-Instruct-2411-GGUF -c 8192 -ngl 99 -fa
CLOUD-GPU PREISVERGLEICH & MIETEN-VS-KAUFEN RECHNER
Nicht genug lokaler VRAM? Cloud-GPU in 60 Sekunden starten (Mistral-Large-2-123B: 72.84 GB in 4-bit)
Recommended Cloud Tier: NVIDIA A100 / H100 (80GB) · Full FP16 Needs: 232.07 GB
Amortisationszeit (Kauf vs. Miete): ~32 months (vs $999 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: Mistral-Large-2-123B × NVIDIA RTX 5080
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA RTX 5080 (16GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 228.36 GB | 2.75 GB | 231.62 GB | Nein (+215.6GB) | ~1.2 / 239 tok/s | 15x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 117.84 GB | 2.75 GB | 121.1 GB | Nein (+105.1GB) | ~1.2 / 239 tok/s | 8x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 121.32 GB | 2.75 GB | 124.57 GB | Nein (+108.6GB) | ~1.2 / 239 tok/s | 8x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 93.63 GB | 2.75 GB | 96.88 GB | Nein (+80.9GB) | ~1.2 / 239 tok/s | 7x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 81.54 GB | 2.75 GB | 84.79 GB | Nein (+68.8GB) | ~1.2 / 239 tok/s | 6x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 69.59 GB | 2.75 GB | 72.84 GB | Nein (+56.8GB) | ~1.2 / 239 tok/s | 5x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 61.13 GB | 2.75 GB | 64.38 GB | Nein (+48.4GB) | ~1.2 / 239 tok/s | 5x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 56.21 GB | 2.75 GB | 59.47 GB | Nein (+43.5GB) | ~1.3 / 239 tok/s | 4x |
Dieses Modell auf anderen GPUs testen
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