LLM GPU Inference Calculator: A Hardware Planning Assistant for Large Model Deployment

LLM GPU Inference Calculator: A Hardware Planning Assistant for Large Model Deployment

This is a GitHub tool maintained by enesarac (original link: https://github.com/enesarac/llm-gpu-inference-calculator, updated on 2026-05-23). Its core value lies in helping users estimate memory requirements, time to first token (TTFT), latency, and throughput when deploying large language models, providing data support for GPU selection and model configuration, and solving hardware planning challenges in private deployment.

Background: Dilemmas in Hardware Selection for Large Model Deployment

With the implementation of LLM applications, the demand for private deployment is growing, but teams often face confusion: How much memory does a certain model need? Can the current GPU meet the TTFT requirements? What concurrency can a single card support? How much memory is saved after quantization? What is the impact of different precisions on performance? These answers are scattered in documents, and there is a lack of a unified calculation tool.

Core Value of the Tool: Key Indicator Calculation and Hardware Matching

1. TTFT Estimation: Based on model parameters, GPU computing power, and bandwidth, evaluate the user waiting experience for interactive applications;
2. Memory Requirement Calculation: Integrate model weights, KV cache, activations, and framework overhead, supporting memory saving analysis for precisions like FP16/INT8/INT4;
3. Latency and Throughput Analysis: Estimate performance under different batch sizes and sequence lengths to find the optimal configuration;
4. GPU-Model Matching Suggestions: Determine whether consumer-grade (e.g., RTX4090) or enterprise-grade (e.g., A100/H100) GPUs can support the target model and concurrent services.

Key Calculation Principles

Memory Usage Composition

• Model Weights: FP16 (2 bytes per parameter), INT8 (1 byte), INT4 (0.5 bytes);
• KV Cache: The formula is 2 * number of layers * hidden dimension * sequence length * batch size * precision byte count;
• Activations: Related to sequence length and batch size;
• Framework Overhead: Reserve 10-20% margin.

Performance Estimation Factors

• Computing Bottleneck: Matrix multiplication computation, but the generation phase is more limited by memory bandwidth;
• Bandwidth Bottleneck: Weight loading speed, quantization can accelerate (as weights become smaller).

TTFT Calculation

Time to first token is affected by prompt processing (prefill), with complexity related to the square of input length (standard attention) or linear (optimized version).

Practical Application Scenarios

1. Individual Developers: Determine the model size that local GPUs (e.g., RTX3090) can run, and the performance loss after quantization;
2. Enterprise Deployment: Evaluate server configuration (number of GPUs, consumer vs. enterprise grade), concurrency capacity, and cost-effectiveness of quantization strategies;
3. Cloud Service Cost: Estimate inference costs for different configurations, balancing performance and price;
4. Model Optimization Verification: Compare theoretical memory savings and speed improvements after quantization/pruning, and evaluate optimization effects.

Usage Suggestions and Notes

• Theory vs. Practice: The calculation results are for reference only. Actual performance is affected by model implementation (vLLM/TensorRT-LLM), CUDA version, system memory, etc., and actual pressure testing is required for verification;
• Precision vs. Speed Trade-off: INT8 quantization has little impact on quality, while INT4 may have a significant drop, requiring task-specific evaluation;
• Batching Strategy: Continuous/inflight batching can improve throughput in high-concurrency scenarios, and it is necessary to understand the trade-off between batch size and latency.

Summary

The LLM GPU Inference Calculator fills the tool gap in the deployment planning phase. Through systematic calculations, it helps users make informed decisions before hardware investment, narrows down the range of optional solutions, and reduces trial-and-error costs. However, the final deployment plan still needs to be determined by combining business scenarios and actual performance tests.