❌ EXCEDE LA VRAM DE 1 GPU (NEEDS 2x NVIDIA RTX 5090)
Qwen2.5-72B-Instruct on NVIDIA RTX 5090 (32GB VRAM)
You need 2x NVIDIA RTX 5090 (64 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
44.38 GB / 32 GB
🟦 Pesos del Modelo: 41.39 GB
🟪 Caché KV: 2.5 GB
🟨 Buffer del Motor: 0.5 GB
VRAM de GPU Requerida44.38 GB
RAM de Sistema Requerida (CPU)0.00 GB
VRAM Disponible en Hardware32 GB (1x NVIDIA RTX 5090)
Margen / Déficit de VRAM-12.38 GB
Velocidad de Generación (Decode)~4.3 tokens/sec
Velocidad de Procesamiento (Prefill)~749 tokens/sec
Tiempo al Primer Token (Prompt 4K)~5468 ms
Topología TransformerDense-GQA (80L, 8/64 KV/Q heads)
🚀 Comando de Lanzamiento Listo para Copiar
ollama run qwen2.5:72b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf Qwen/Qwen2.5-72B-Instruct-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 (Qwen2.5-72B-Instruct: 44.38 GB in 4-bit)
Recommended Cloud Tier: L40S / RTX 6000 Ada (48GB) · Full FP16 Needs: 138.88 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: Qwen2.5-72B-Instruct × 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 | 135.43 GB | 2.5 GB | 138.43 GB | No (+106.4GB) | ~1.2 / 749 tok/s | 5x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 69.91 GB | 2.5 GB | 72.9 GB | No (+40.9GB) | ~1.4 / 749 tok/s | 3x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 71.95 GB | 2.5 GB | 74.95 GB | No (+43.0GB) | ~1.3 / 749 tok/s | 3x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 55.53 GB | 2.5 GB | 58.53 GB | No (+26.5GB) | ~2.1 / 749 tok/s | 2x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 48.42 GB | 2.5 GB | 51.41 GB | No (+19.4GB) | ~2.8 / 749 tok/s | 2x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 41.39 GB | 2.5 GB | 44.38 GB | No (+12.4GB) | ~4.3 / 749 tok/s | 2x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 36.34 GB | 2.5 GB | 39.33 GB | No (+7.3GB) | ~6.8 / 749 tok/s | 2x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 33.41 GB | 2.5 GB | 36.41 GB | No (+4.4GB) | ~10.2 / 749 tok/s | 2x |
Probar este Modelo en Otras GPUs
Qwen2.5-72B-Instruct on NVIDIA RTX 4090 (24GB)Qwen2.5-72B-Instruct on NVIDIA RTX 5080 (16GB)Qwen2.5-72B-Instruct on NVIDIA RTX 4080 Super (16GB)Qwen2.5-72B-Instruct on NVIDIA RTX 4070 Ti Super (16GB)Qwen2.5-72B-Instruct on NVIDIA RTX 3090 (24GB)Qwen2.5-72B-Instruct on NVIDIA RTX 4060 Ti 16GB (16GB)Qwen2.5-72B-Instruct on NVIDIA RTX 3060 12GB (12GB)Qwen2.5-72B-Instruct on NVIDIA RTX 6000 Ada (48GB)Qwen2.5-72B-Instruct on NVIDIA RTX A6000 (48GB)Qwen2.5-72B-Instruct on NVIDIA L40S 48GB (48GB)
Otros Modelos en esta GPU
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