Meta Llama on AWS
A production-grade reference for deploying, serving, and scaling Meta Llama models on GPU-backed infrastructure. This project focuses on inference performance, system architecture, and real-world GenAI workloads, emphasizing the decisions that directly impact latency, throughput, and cost rather than model internals or prompt engineering.
In production LLM systems, inference performance is rarely limited by model quality. Instead, GPU memory behavior, batching strategy, and serving architecture determine whether systems meet latency and cost targets.
Key Skills Demonstrated
- Production LLM deployment and inference architecture
- GPU-backed serving design and scaling intuition
- Latency, throughput, and cost tradeoff analysis
- Batching and concurrency-aware inference reasoning
- Customer-facing GenAI system enablement
Technical Focus Areas
Rather than optimizing individual kernels, this project highlights the system-level behaviors that dominate LLM inference performance in production. It explains how GPU memory bandwidth, request concurrency, and batching decisions shape end-to-end latency and cost efficiency.
Key Takeaways
- LLM inference is primarily memory-bound, with GPU memory behavior dominating decode performance.
- Serving architecture matters as much as model choice when optimizing for latency and cost.
- Batching improves throughput but must be tuned carefully to avoid tail-latency regression.
- Production GenAI systems require balancing throughput, latency, and reliability.
Why This Matters
This project reflects my focus on real-world GenAI systems, where GPU behavior, inference bottlenecks, and system design determine success. The lessons here translate directly to modern inference stacks such as TensorRT-LLM, Triton, and large-scale LLM serving platforms.