✅ LÄUFT NATIV IN FP16 & ALLEN QUANTISIERUNGEN
Mixtral-8x22B-Instruct (MoE) on 4x NVIDIA A100 80GB (320GB) (320GB VRAM)
In 4-bit GGUF Q4_K_M, Mixtral-8x22B-Instruct (MoE) consumes 81.38 GB (79.14 GB weights + 1.75 GB KV cache at 8K context), achieving ~59.4 tokens/sec decode and ~8299 tokens/sec prefill.
🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator
📊 Echtzeit-Speicher- & Performance-Telemetrie
81.38 GB / 320 GB
🟦 Modellgewichte: 79.14 GB
🟪 KV-Cache: 1.75 GB
🟨 Engine-Puffer: 0.49 GB
Benötigter GPU-VRAM81.38 GB
Benötigter System-RAM (CPU)0.00 GB
Verfügbarer Hardware-VRAM320 GB (1x 4x NVIDIA A100 80GB (320GB))
VRAM-Reserve / Defizit238.62 GB
Geschätzte Decode-Geschwindigkeit~59.4 tokens/sec
Geschätzte Prefill-Geschwindigkeit~8299 tokens/sec
Time-to-First-Token (4K Prompt)~494 ms
Transformer-TopologieMoE-GQA (56L, 8/48 KV/Q heads)
🚀 Startbefehl (vLLM / llama.cpp / Ollama)
ollama run mixtral:8x22b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf mistralai/Mixtral-8x22B-Instruct-v0.1-GGUF -c 8192 -ngl 99 -fa
CLOUD-GPU PREISVERGLEICH & MIETEN-VS-KAUFEN RECHNER
Nicht genug lokaler VRAM? Cloud-GPU in 60 Sekunden starten (Mixtral-8x22B-Instruct (MoE): 81.38 GB in 4-bit)
Recommended Cloud Tier: Multi-GPU H100 / H200 Node · Full FP16 Needs: 264.58 GB
Amortisationszeit (Kauf vs. Miete): ~32 months (vs $58,000 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: Mixtral-8x22B-Instruct (MoE) × 4x NVIDIA A100 80GB (320GB)
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x 4x NVIDIA A100 80GB (320GB) (320GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 261.89 GB | 1.75 GB | 264.13 GB | Ja (83%) | ~17.9 / 8299 tok/s | 1x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 135.05 GB | 1.75 GB | 137.29 GB | Ja (43%) | ~34.8 / 8299 tok/s | 1x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 139.13 GB | 1.75 GB | 141.37 GB | Ja (44%) | ~33.7 / 8299 tok/s | 1x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 107.37 GB | 1.75 GB | 109.62 GB | Ja (34%) | ~43.7 / 8299 tok/s | 1x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 93.17 GB | 1.75 GB | 95.42 GB | Ja (30%) | ~50.4 / 8299 tok/s | 1x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 79.14 GB | 1.75 GB | 81.38 GB | Ja (25%) | ~59.4 / 8299 tok/s | 1x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 69.62 GB | 1.75 GB | 71.87 GB | Ja (22%) | ~67.5 / 8299 tok/s | 1x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 64.05 GB | 1.75 GB | 66.29 GB | Ja (21%) | ~73.3 / 8299 tok/s | 1x |
Dieses Modell auf anderen GPUs testen
Mixtral-8x22B-Instruct (MoE) on NVIDIA RTX 5090 (32GB)Mixtral-8x22B-Instruct (MoE) on NVIDIA RTX 4090 (24GB)Mixtral-8x22B-Instruct (MoE) on NVIDIA RTX 5080 (16GB)Mixtral-8x22B-Instruct (MoE) on NVIDIA RTX 4080 Super (16GB)Mixtral-8x22B-Instruct (MoE) on NVIDIA RTX 4070 Ti Super (16GB)Mixtral-8x22B-Instruct (MoE) on NVIDIA RTX 3090 (24GB)Mixtral-8x22B-Instruct (MoE) on NVIDIA RTX 4060 Ti 16GB (16GB)Mixtral-8x22B-Instruct (MoE) on NVIDIA RTX 3060 12GB (12GB)Mixtral-8x22B-Instruct (MoE) on NVIDIA RTX 6000 Ada (48GB)Mixtral-8x22B-Instruct (MoE) on NVIDIA RTX A6000 (48GB)
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