Language Model Hardware Requirements Calculator
Industry-Standard VRAM and TFLOPS Estimation for Small and Large Language Models
Accurate Hardware Planning for LM Deployment
This calculator provides industry-standard estimates for VRAM requirements (model weights, KV cache, overhead) and compute requirements (TFLOPS for prefill, decode, and KV cache operations) to help you plan your language model deployment.
Configuration Inputs
Hardware Requirements
Click Calculate Requirements to see results
Use Case Guidelines
Interactive Chat
Generation Speed: 20-50 tok/s
Latency: 20-50ms per token
Time to First Token: 0.5-2.0 sec
Ideal for: Chatbots, customer service, conversational AI
Code Generation
Generation Speed: 30-60 tok/s
Latency: 15-35ms per token
Time to First Token: 0.5-1.5 sec
Ideal for: GitHub Copilot-style tools, code completion
Long-form Content
Generation Speed: 40-80 tok/s
Latency: 12-25ms per token
Time to First Token: 1.0-3.0 sec
Ideal for: Article generation, documentation, reports
Real-time Autocomplete
Generation Speed: 60-100 tok/s
Latency: 10-15ms per token
Time to First Token: less than 0.3 sec
Ideal for: Real-time suggestions, inline completions
Batch Processing
Generation Speed: 100-200+ tok/s
Latency: 5-10ms per token
Time to First Token: 5.0-10.0 sec
Ideal for: Background jobs, large-scale analysis
Understanding Hardware Requirements
Total VRAM = Model Weights + KV Cache + Framework Overhead. Model weights are the primary component. KV cache grows with sequence length and batch size.
Compute required to process the input context and generate KV cache. Formula: 2 × Params × Context_Length. This is a one-time cost at the start of generation.
Compute required to generate each output token. Formula: 2 × Params. This repeats for every token generated. Memory bandwidth often becomes the bottleneck, not compute.
Compute required to update the KV cache for each new token. Formula: 2 × Layers × Hidden_dim. Small compared to decode, but grows with model depth.