✅ PASST IN DEN VRAM (GGUF Q4_K_M / AWQ (4.83 bpw))
Mixtral-8x7B-Instruct (MoE) on NVIDIA RTX 5090 (32GB VRAM)
Unquantized FP16 (88.92 GB) exceeds 32 GB, but 4-bit Q4_K_M requires 27.8 GB (87% VRAM utilization) at ~158.1 tokens/sec.
🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator
📊 Echtzeit-Speicher- & Performance-Telemetrie
27.8 GB / 32 GB
🟦 Modellgewichte: 26.31 GB
🟪 KV-Cache: 1 GB
🟨 Engine-Puffer: 0.49 GB
Benötigter GPU-VRAM27.8 GB
Benötigter System-RAM (CPU)0.00 GB
Verfügbarer Hardware-VRAM32 GB (1x NVIDIA RTX 5090)
VRAM-Reserve / Defizit4.20 GB
Geschätzte Decode-Geschwindigkeit~158.1 tokens/sec
Geschätzte Prefill-Geschwindigkeit~4222 tokens/sec
Time-to-First-Token (4K Prompt)~970 ms
Transformer-TopologieMoE-GQA (32L, 8/32 KV/Q heads)
🚀 Startbefehl (vLLM / llama.cpp / Ollama)
ollama run mixtral:8x7b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf mistralai/Mixtral-8x7B-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-8x7B-Instruct (MoE): 27.8 GB in 4-bit)
Recommended Cloud Tier: L40S / RTX 6000 Ada (48GB) · Full FP16 Needs: 88.92 GB
Amortisationszeit (Kauf vs. Miete): ~32 months (vs $1,999 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: Mixtral-8x7B-Instruct (MoE) × NVIDIA RTX 5090
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA RTX 5090 (32GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 86.99 GB | 1 GB | 88.47 GB | Nein (+56.5GB) | ~3.5 / 4222 tok/s | 3x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 44.86 GB | 1 GB | 46.35 GB | Nein (+14.4GB) | ~13 / 4222 tok/s | 2x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 46.21 GB | 1 GB | 47.7 GB | Nein (+15.7GB) | ~11.9 / 4222 tok/s | 2x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 35.66 GB | 1 GB | 37.15 GB | Nein (+5.1GB) | ~31 / 4222 tok/s | 2x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 30.96 GB | 1 GB | 32.45 GB | Nein (+0.5GB) | ~105.2 / 4222 tok/s | 2x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 26.31 GB | 1 GB | 27.8 GB | Ja (87%) | ~158.1 / 4222 tok/s | 1x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 23.14 GB | 1 GB | 24.63 GB | Ja (77%) | ~179.7 / 4222 tok/s | 1x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 21.29 GB | 1 GB | 22.78 GB | Ja (71%) | ~195.3 / 4222 tok/s | 1x |
Dieses Modell auf anderen GPUs testen
Mixtral-8x7B-Instruct (MoE) on NVIDIA RTX 4090 (24GB)Mixtral-8x7B-Instruct (MoE) on NVIDIA RTX 5080 (16GB)Mixtral-8x7B-Instruct (MoE) on NVIDIA RTX 4080 Super (16GB)Mixtral-8x7B-Instruct (MoE) on NVIDIA RTX 4070 Ti Super (16GB)Mixtral-8x7B-Instruct (MoE) on NVIDIA RTX 3090 (24GB)Mixtral-8x7B-Instruct (MoE) on NVIDIA RTX 4060 Ti 16GB (16GB)Mixtral-8x7B-Instruct (MoE) on NVIDIA RTX 3060 12GB (12GB)Mixtral-8x7B-Instruct (MoE) on NVIDIA RTX 6000 Ada (48GB)Mixtral-8x7B-Instruct (MoE) on NVIDIA RTX A6000 (48GB)Mixtral-8x7B-Instruct (MoE) on NVIDIA L40S 48GB (48GB)
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