PROJECT 01 · ML INFRASTRUCTURE
Atlas AI
Updated: May 2026
Built a distributed AI infrastructure platform for transformer systems, distributed training behavior, inference optimization, observability, and performance engineering under real systems constraints.
Problem
Modern AI systems are constrained not only by model quality, but by communication overhead, memory scaling, inference latency, synchronization cost, and observability limitations.
Technical Highlights
Python · Transformers · FastAPI · Distributed Systems · Observability
- Transformer infrastructure and KV-cache systems
- Distributed runtime and communication profiling
- Serving, observability, and benchmark automation
System Design
- Reverse-mode autograd and optimizer infrastructure
- Transformer runtime with KV cache and streaming generation
- Distributed multiprocessing runtime with communication profiling
- FastAPI inference server with observability endpoints
- Benchmark automation and regression-detection workflows
Architecture
Atlas AI integrates training infrastructure, transformer systems, distributed runtimes, serving, and observability into one ML systems platform.
Results and Insights
- Throughput degraded as autoregressive sequence length increased.
- Communication profiling exposed distributed-runtime bottlenecks.
- KV-cache growth materially affected transformer memory behavior.
- Streaming generation reduced perceived inference latency.
- Benchmark automation enabled regression-oriented validation.
Takeaway
Modern AI systems are constrained by memory behavior, communication overhead, inference latency, and observability—not only model architecture.
Transformer Benchmark
Generation throughput decreases as token count increases, revealing inference-scaling and KV-cache effects.
Serving and Observability
