🖥️ AI Datacenter - Multi-Vendor GPU Server Racks
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Rack A1 - NVIDIA
NVIDIA DGX H100
8x H100 80GB GPUs
NVIDIA RTX 5090
4x RTX 5090 32GB
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Rack B1 - AMD
AMD Instinct MI300X
8x MI300X 192GB
AMD Radeon Pro W7900
4x W7900 48GB
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Rack C1 - Intel
Intel Gaudi 3
8x Gaudi 3 AI Accelerators
Intel Arc B580
4x Arc B580 16GB
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Rack D1 - Specialized
Cerebras CS-3
Wafer-Scale Engine
Google TPU v5
TPU Pod Slice
32 GPUs
Total GPU Count
1.8 PetaFLOPS
Compute Power
18°C
Cooling Temperature
99.9%
Uptime SLA
GPU Performance Calculator for LLM Models
Estimate GPU requirements, inference performance, and costs for 50+ latest Large Language Models including GPT-4o, Claude 3.7, Llama 3.3, DeepSeek-V3, Qwen 2.5, Gemini 2.0, and many open source models (Updated November 2025)
Performance Estimate
GPU Memory Required
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VRAM needed per GPUGPUs Needed
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Minimum GPU countInference Speed
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Tokens per secondLatency (First Token)
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Time to first tokenEst. Monthly Cost
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Cloud GPU pricingPower Consumption
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TDP per GPURecommendations
- Select model and GPU configuration to see recommendations
Important Notes
- ✨ Updated November 2025 - Includes 50+ latest LLM models: GPT-4o, Claude 3.7 Sonnet, Gemini 2.0 Flash, Llama 3.3, DeepSeek-V3, Qwen 2.5, Phi-4, and many more open source models
- Estimates are based on typical configurations and may vary based on implementation details
- Training requires significantly more memory than inference (typically 3-4x model size)
- Actual performance depends on model optimization, batch size, and sequence length
- MoE (Mixture of Experts) models like DeepSeek-V3 and Mixtral use fewer active parameters during inference
- Costs shown are approximate cloud pricing; on-premises costs will differ significantly
- Multi-GPU setups require high-speed interconnects (NVLink, InfiniBand) for optimal performance
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