LLM-D Prism: A Unified Performance Analysis Platform for Distributed Inference Systems

LLM-D Prism is an interactive performance analysis tool for AI platform engineers and ML engineers, designed to address pain points in distributed inference infrastructure decision-making. It integrates benchmark data from cloud APIs, public repositories, and local experiments to help users make informed decisions balancing throughput, latency, cost, and quality, reducing the cognitive load and time cost of complex decisions.

Choosing an inference service solution in the AI infrastructure field faces multiple challenges: fragmented data sources (cloud vendors and open-source frameworks have inconsistent data formats and varying test conditions), multi-dimensional trade-offs (low latency vs. high cost, high throughput vs. first-token latency, quantization compression vs. output quality), scenario-dependent specificity (real-time dialogue prioritizes first-token latency, batch processing focuses on throughput), and rapid evolution of technology stacks (new engines, hardware, and optimization techniques emerge continuously).

Prism is positioned as a "unified data source" for distributed inference decision-making. Its core solutions include: 1. Data integration and standardization: Collect data from cloud APIs, public repositories, and local experiments; extract metadata, label standardized IDs, and unify formats and units via src/utils/dataParser.js. 2. Interactive analysis experience: Support multi-dimensional filtering, comparison views, trend analysis, and cost-benefit curve visualization. 3. Data reliability: All data is based on verified benchmark tests, not vendor marketing claims.

Technical Architecture: Frontend uses React19 + Tailwind CSS v4 + Recharts + Lucide React; backend uses BFF pattern (Node.js/Express) to proxy cloud APIs, inject credentials, and implement rate limiting. Data sources support GCS, GIQ, AWS S3, Google Drive/Sheets. Deployment options include local npm startup, Docker containerization (with hot reload support), Google Cloud Run (simplified via deploy.sh script), and plans to expand to multi-cloud platforms like AWS App Runner and Azure Container Apps. Configuration is implemented via environment variables, and authentication follows the principle of least privilege (ADC for local use, service accounts for production).

Prism provides four key values for engineers: 1. Shorten evaluation cycles (from days to minutes). 2. Optimize cost-effectiveness (visualize trade-off curves to find optimal configurations). 3. Support data-driven decisions (traceable verified data to avoid marketing misdirection). 4. Promote team collaboration (transparent platform facilitates communication between technical and business teams).

Current Limitations: Cloud vendor coverage is mainly focused on Google Cloud; AWS/Azure support is still being improved; data sources need continuous integration of more open-source and vendor data; real-time performance monitoring is pending development. Future Directions: Expand multi-cloud support, add more data sources, and develop real-time monitoring functions. Summary: Prism represents the trend of tooling in AI infrastructure, is an implementation of data-driven decision-making methodology, and is expected to become a standard reference platform in the field.