Genomic variant analysis platform powered by the Evo2 DNA language model. Tackled GPU cost problem ($3.50/hr H100) by building serverless inference on Modal with cold-start optimization, cutting per-inference cost from $0.12 to $0.05 (60% FinOps savings) while enabling 1K+ variant analyses/hour for genomics researchers.

• Repositoryhttps://github.com/insertfahim/variant-analysis-evo2
• PlatformWeb (Next.js) / Serverless GPU (Modal)
• StackNext.js, Python, Modal (Serverless GPU), FastAPI
• Cost Savings60% FinOps savings ($0.12 → $0.05 per inference)
• Data Availability99.5% uptime via circuit breaker + fallback cache
• Throughput1K+ variant analyses/hour, auto-scaling 0→50 replicas

Key SRE & Infrastructure Features

• Serverless GPU Computing: Built serverless inference on Modal with cold-start optimization (model weight caching), auto-scaling 0→50 replicas based on demand
• Cost Optimization: Tackled $3.50/hr H100 GPU costs—cut per-inference cost from $0.12 to $0.05 through intelligent scheduling and batch processing (60% FinOps savings)
• Circuit Breaker Pattern: Integrated ClinVar/NCBI APIs with circuit breaker pattern and local fallback cache for resilience against upstream failures
• High Availability: Maintained 99.5% data availability despite upstream rate limits through intelligent caching and retry strategies
• Throughput: Enabled 1K+ variant analyses/hour for genomics researchers with horizontal auto-scaling
• Real-Time Processing: Instant analysis with GPU acceleration and optimized cold-start times

Technical Architecture

The platform features a full-stack architecture with a modern Next.js frontend and a high-performance Python backend. The AI inference is powered by Modal's serverless GPU infrastructure, providing auto-scaling compute resources for the Evo2 model processing.

Technical Achievements

• 60% cost reduction through serverless GPU optimization and intelligent workload scheduling
• 99.5% data availability via circuit breaker pattern with local fallback caching
• Auto-scaling infrastructure supporting 0→50 replicas based on real-time demand
• Cold-start optimization through model weight caching and pre-warming strategies
• 1K+ analyses/hour throughput for production genomics research workloads

This platform represents a significant advancement in making cutting-edge genomic AI accessible to researchers and clinicians worldwide, bridging the gap between state-of-the-art machine learning research and practical clinical applications.