Introduction: Infer-Forge—A Systematic Benchmarking Platform for LLM Inference Optimization

Infer-Forge is a systematic benchmarking platform for large language model (LLM) inference optimization, designed to address the bottleneck of high LLM inference costs that restrict large-scale applications. The platform provides one-stop inference evaluation, optimization strategy comparison, and decision support for production environment deployment, helping developers and operation teams find the optimal balance between latency, throughput, and cost.

Background: Urgent Need for LLM Inference Optimization

LLM inference cost is a key bottleneck restricting its large-scale application. Taking GPT-4-level models as an example, a single inference consumes considerable computing resources; in real-time scenarios (such as dialogue and code completion), latency affects user experience, while in batch scenarios (such as document analysis), throughput impacts operational costs. Infer-Forge is a systematic benchmarking platform designed to address this challenge.

Methodology: Technical Architecture and Core Features of Infer-Forge

Evaluation Engine Design

• Load Generator: Simulates real request patterns (Poisson arrival, fixed rate, etc.), sequence length distribution, concurrency control, and mixed workloads
• Performance Collector: Records end-to-end latency, first token latency, throughput, resource utilization, queuing delay, and other metrics
• Result Analyzer: Generates statistical summaries, distribution visualizations, bottleneck identification, and comparative analysis reports

Built-in Optimization Strategy Library

• Quantization: INT8/INT4 quantization, GPTQ/AWQ algorithms, and accuracy loss evaluation
• KV Cache Optimization: Paged cache, cache compression, dynamic allocation
• Batch Processing Optimization: Dynamic batching, continuous batching, request scheduling
• Speculative Decoding: Draft-verify architecture, tree decoding, and benefit evaluation

Multi-backend Support

Supports vLLM, TensorRT-LLM, llama.cpp, TGI, and custom backends, facilitating horizontal comparison.

Evidence: Practical Application Scenarios of Infer-Forge

• Model Selection Decision: Tests candidate model performance, compares cost-effectiveness of different-scale models, and evaluates the impact of quantization on task quality
• Optimization Strategy Validation: Quantifies optimization benefits, identifies compatibility issues, and assesses the impact on output quality
• Capacity Planning: Predicts GPU quantity, evaluates hardware cost-effectiveness, and plans elastic scaling strategies
• Continuous Performance Monitoring: Detects performance regression, tracks the effects of model/engine updates, and generates trend reports

Best Practices: Evaluation Methodology of Infer-Forge

Test Environment Standardization

• Hardware isolation, eliminate cold start effects via warm-up, multiple sampling for stable statistics, record environment information

Workload Design Principles

• Sample real production request features, cover extreme scenarios, progressive pressure application, simulate mixed request patterns

Result Interpretation Guidelines

• Focus on P99 tail latency, balance throughput and latency, calculate per-token cost, verify output quality

Conclusion and Outlook: Value and Future Development of Infer-Forge

Infer-Forge provides a professional and systematic benchmarking platform for LLM inference optimization. Through standardized processes, a rich strategy library, and in-depth analysis, it helps teams establish data-driven optimization decision mechanisms. Future plans include expanding multi-modal inference support, edge device optimization, energy consumption evaluation, and automatic optimization recommendations.

Project address: https://github.com/chuenchen309/infer-forge