AI Deployment & SRE
TensorRT-LLM & NVIDIA Triton: Enterprise Inference Optimization & FP8 Quantization
Achieve maximum GPU utilization and lowest latency on NVIDIA H100/A100 clusters with TensorRT-LLM and Triton Inference Server.
FP8 Quantization and In-Flight Batching
TensorRT-LLM compiles custom CUDA kernels with FP8 precision, doubling inference throughput while maintaining FP16 output quality.
Related Technical Guides
Deepen your understanding with these closely related production architectures and tutorials:
vLLM Production Serving: PagedAttention, Continuous Batching & Tensor Parallelism
Deploy open-source LLMs with 10x-24x higher throughput using vLLM's PagedAttention virtual memory architecture and continuous batching.
Speculative Decoding: 2x-3x Faster Inference with Zero Loss in Output Quality
Accelerate token generation using a lightweight draft model paired with a large model verifier without sacrificing perplexity or accuracy.
Generative Engine Optimization (GEO) Guide: Complete AI Search Readiness Checklist & Best Practices
A comprehensive guide to ranking, getting cited, and becoming a primary authoritative source on ChatGPT, Perplexity, and Google AI Overviews through modern GEO architecture and schemas.