✅ EJECUTA FP16 NATIVO Y TODAS LAS CUANTIZACIONES
GLM-4-32B-0414 on NVIDIA A100 80GB SXM (80GB VRAM)
In 4-bit GGUF Q4_K_M, GLM-4-32B-0414 consumes 20.77 GB (18.37 GB weights + 1.91 GB KV cache at 8K context), achieving ~72.5 tokens/sec decode and ~2535 tokens/sec prefill.
🎛️ Ajustador Interactivo de VRAM, Contexto y Offload
📊 Telemetría de Memoria y Rendimiento en Tiempo Real
20.77 GB / 80 GB
🟦 Pesos del Modelo: 18.37 GB
🟪 Caché KV: 1.91 GB
🟨 Buffer del Motor: 0.49 GB
VRAM de GPU Requerida20.77 GB
RAM de Sistema Requerida (CPU)0.00 GB
VRAM Disponible en Hardware80 GB (1x NVIDIA A100 80GB SXM)
Margen / Déficit de VRAM59.23 GB
Velocidad de Generación (Decode)~72.5 tokens/sec
Velocidad de Procesamiento (Prefill)~2535 tokens/sec
Tiempo al Primer Token (Prompt 4K)~1616 ms
Topología TransformerDense-GQA (61L, 8/48 KV/Q heads)
🚀 Comando de Lanzamiento Listo para Copiar
ollama run glm4:32b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf THUDM/GLM-4-32B-0414-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 (GLM-4-32B-0414: 20.77 GB in 4-bit)
Recommended Cloud Tier: RTX 4090 (24GB) · Full FP16 Needs: 62.45 GB
Meses para amortizar compra vs alquiler: ~32 months (vs $14,500 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: GLM-4-32B-0414 × NVIDIA A100 80GB SXM
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA A100 80GB SXM (80GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 59.6 GB | 1.91 GB | 62 GB | Sí (78%) | ~21.9 / 2535 tok/s | 1x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 30.79 GB | 1.91 GB | 33.19 GB | Sí (41%) | ~42.5 / 2535 tok/s | 1x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 31.66 GB | 1.91 GB | 34.06 GB | Sí (43%) | ~41.2 / 2535 tok/s | 1x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 24.44 GB | 1.91 GB | 26.84 GB | Sí (34%) | ~53.4 / 2535 tok/s | 1x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 21.39 GB | 1.91 GB | 23.78 GB | Sí (30%) | ~61.6 / 2535 tok/s | 1x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 18.37 GB | 1.91 GB | 20.77 GB | Sí (26%) | ~72.5 / 2535 tok/s | 1x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 16.1 GB | 1.91 GB | 18.5 GB | Sí (23%) | ~82.4 / 2535 tok/s | 1x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 14.8 GB | 1.91 GB | 17.2 GB | Sí (22%) | ~89.6 / 2535 tok/s | 1x |
Probar este Modelo en Otras GPUs
GLM-4-32B-0414 on NVIDIA RTX 5090 (32GB)GLM-4-32B-0414 on NVIDIA RTX 4090 (24GB)GLM-4-32B-0414 on NVIDIA RTX 5080 (16GB)GLM-4-32B-0414 on NVIDIA RTX 4080 Super (16GB)GLM-4-32B-0414 on NVIDIA RTX 4070 Ti Super (16GB)GLM-4-32B-0414 on NVIDIA RTX 3090 (24GB)GLM-4-32B-0414 on NVIDIA RTX 4060 Ti 16GB (16GB)GLM-4-32B-0414 on NVIDIA RTX 3060 12GB (12GB)GLM-4-32B-0414 on NVIDIA RTX 6000 Ada (48GB)GLM-4-32B-0414 on NVIDIA RTX A6000 (48GB)
Otros Modelos en esta GPU
DeepSeek-R1 671B (MoE) on NVIDIA A100 80GB SXMDeepSeek-V3-0324 671B (MoE) on NVIDIA A100 80GB SXMDeepSeek-R1-Distill-Llama-70B on NVIDIA A100 80GB SXMDeepSeek-R1-Distill-Qwen-32B on NVIDIA A100 80GB SXMDeepSeek-R1-Distill-Qwen-14B on NVIDIA A100 80GB SXMDeepSeek-R1-Distill-Llama-8B on NVIDIA A100 80GB SXMDeepSeek-R1-Distill-Qwen-7B on NVIDIA A100 80GB SXMQwen3-235B-A22B (MoE) on NVIDIA A100 80GB SXMQwen3-30B-A3B (MoE) on NVIDIA A100 80GB SXMQwen3-32B on NVIDIA A100 80GB SXM