❌ EXCEDE LA VRAM DE 1 GPU (NEEDS 3x NVIDIA RTX 5090)
Mixtral-8x22B-Instruct (MoE) on NVIDIA RTX 5090 (32GB VRAM)
You need 3x NVIDIA RTX 5090 (96 GB total VRAM) for full GPU execution, or rent an A100/H100 80GB cloud instance.
🎛️ Ajustador Interactivo de VRAM, Contexto y Offload
📊 Telemetría de Memoria y Rendimiento en Tiempo Real
81.38 GB / 32 GB
🟦 Pesos del Modelo: 79.14 GB
🟪 Caché KV: 1.75 GB
🟨 Buffer del Motor: 0.49 GB
VRAM de GPU Requerida81.38 GB
RAM de Sistema Requerida (CPU)0.00 GB
VRAM Disponible en Hardware32 GB (1x NVIDIA RTX 5090)
Margen / Déficit de VRAM-49.38 GB
Velocidad de Generación (Decode)~4 tokens/sec
Velocidad de Procesamiento (Prefill)~1393 tokens/sec
Tiempo al Primer Token (Prompt 4K)~2940 ms
Topología TransformerMoE-GQA (56L, 8/48 KV/Q heads)
🚀 Comando de Lanzamiento Listo para Copiar
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
COMPARADOR CLOUD GPU Y CALCULADORA ALQUILAR VS COMPRAR
¿No tienes suficiente VRAM? Alquila una GPU en la nube en 60 segundos (Mixtral-8x22B-Instruct (MoE): 81.38 GB in 4-bit)
Recommended Cloud Tier: Multi-GPU H100 / H200 Node · Full FP16 Needs: 264.58 GB
Meses para amortizar compra vs alquiler: ~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 | Desplegar en RunPod → |
| Vast.ai | $0.29 / hr | $0.95 / hr | $1.79 / hr | $34.80 / mo | Save 50–70% on RTX 4090 / 5090 / A100 rentals | Alquilar GPU Spot → |
| Vultr Cloud GPU | — | $1.79 / hr | $2.69 / hr | $214.80 / mo (A100) | $300 Free Credit | Reclamar $300 Gratis → |
| DigitalOcean GPU | — | — | $2.99 / hr | $358.80 / mo (H100) | $200 Free Credit (60d) | Obtener $200 Gratis → |
Desglose Completo por Formato de Cuantización: Mixtral-8x22B-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 | 261.89 GB | 1.75 GB | 264.13 GB | No (+232.1GB) | ~1.2 / 1393 tok/s | 9x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 135.05 GB | 1.75 GB | 137.29 GB | No (+105.3GB) | ~1.9 / 1393 tok/s | 5x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 139.13 GB | 1.75 GB | 141.37 GB | No (+109.4GB) | ~1.8 / 1393 tok/s | 5x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 107.37 GB | 1.75 GB | 109.62 GB | No (+77.6GB) | ~2.6 / 1393 tok/s | 4x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 93.17 GB | 1.75 GB | 95.42 GB | No (+63.4GB) | ~3.1 / 1393 tok/s | 3x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 79.14 GB | 1.75 GB | 81.38 GB | No (+49.4GB) | ~4 / 1393 tok/s | 3x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 69.62 GB | 1.75 GB | 71.87 GB | No (+39.9GB) | ~4.9 / 1393 tok/s | 3x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 64.05 GB | 1.75 GB | 66.29 GB | No (+34.3GB) | ~5.7 / 1393 tok/s | 3x |
Probar este Modelo en Otras GPUs
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)Mixtral-8x22B-Instruct (MoE) on NVIDIA L40S 48GB (48GB)
Otros Modelos en esta GPU
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