❌ EXCEDE LA VRAM DE 1 GPU (NEEDS 2x NVIDIA RTX 4080 Super)
Google Gemma-3-27B-IT on NVIDIA RTX 4080 Super (16GB VRAM)
You need 2x NVIDIA RTX 4080 Super (32 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
20.34 GB / 16 GB
🟦 Pesos del Modelo: 15.97 GB
🟪 Caché KV: 3.88 GB
🟨 Buffer del Motor: 0.49 GB
VRAM de GPU Requerida20.34 GB
RAM de Sistema Requerida (CPU)0.00 GB
VRAM Disponible en Hardware16 GB (1x NVIDIA RTX 4080 Super)
Margen / Déficit de VRAM-4.34 GB
Velocidad de Generación (Decode)~11.7 tokens/sec
Velocidad de Procesamiento (Prefill)~991 tokens/sec
Tiempo al Primer Token (Prompt 4K)~4135 ms
Topología TransformerDense-GQA (62L, 16/32 KV/Q heads)
🚀 Comando de Lanzamiento Listo para Copiar
ollama run gemma3:27b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf google/gemma-3-27b-it-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 (Google Gemma-3-27B-IT: 20.34 GB in 4-bit)
Recommended Cloud Tier: RTX 4090 (24GB) · Full FP16 Needs: 55.85 GB
Meses para amortizar compra vs alquiler: ~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 | 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: Google Gemma-3-27B-IT × NVIDIA RTX 4080 Super
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA RTX 4080 Super (16GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 51.04 GB | 3.88 GB | 55.4 GB | No (+39.4GB) | ~1.4 / 991 tok/s | 4x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 26.4 GB | 3.88 GB | 30.76 GB | No (+14.8GB) | ~3.9 / 991 tok/s | 2x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 27.11 GB | 3.88 GB | 31.48 GB | No (+15.5GB) | ~3.7 / 991 tok/s | 2x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 20.92 GB | 3.88 GB | 25.29 GB | No (+9.3GB) | ~6 / 991 tok/s | 2x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 18.44 GB | 3.88 GB | 22.8 GB | No (+6.8GB) | ~8 / 991 tok/s | 2x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 15.97 GB | 3.88 GB | 20.34 GB | No (+4.3GB) | ~11.7 / 991 tok/s | 2x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 13.97 GB | 3.88 GB | 18.33 GB | No (+2.3GB) | ~17.7 / 991 tok/s | 2x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 12.83 GB | 3.88 GB | 17.2 GB | No (+1.2GB) | ~24.7 / 991 tok/s | 2x |
Probar este Modelo en Otras GPUs
Google Gemma-3-27B-IT on NVIDIA RTX 5090 (32GB)Google Gemma-3-27B-IT on NVIDIA RTX 4090 (24GB)Google Gemma-3-27B-IT on NVIDIA RTX 5080 (16GB)Google Gemma-3-27B-IT on NVIDIA RTX 4070 Ti Super (16GB)Google Gemma-3-27B-IT on NVIDIA RTX 3090 (24GB)Google Gemma-3-27B-IT on NVIDIA RTX 4060 Ti 16GB (16GB)Google Gemma-3-27B-IT on NVIDIA RTX 3060 12GB (12GB)Google Gemma-3-27B-IT on NVIDIA RTX 6000 Ada (48GB)Google Gemma-3-27B-IT on NVIDIA RTX A6000 (48GB)Google Gemma-3-27B-IT on NVIDIA L40S 48GB (48GB)
Otros Modelos en esta GPU
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