Lead Developer Advocate
Model performance
33% faster LLM inference with FP8 quantization
Quantizing open-source LLMs to FP8 resulted in near-zero perplexity gains and yielded material performance improvements across latency, throughput, and cost.
Model performance
High performance ML inference with NVIDIA TensorRT
Use TensorRT to achieve 40% lower latency for SDXL and sub-200ms time to first token for Mixtral 8x7B on A100 and H100 GPUs.
Glossary
FP8: Efficient model inference with 8-bit floating point numbers
The FP8 data format has an expanded dynamic range versus INT8 which allows for quantizing weights and activations for more LLMs without loss of output quality.
Glossary
The benefits of globally distributed infrastructure for model serving
Multi-cloud and multi-region infrastructure for model serving provides availability, redundancy, lower latency, cost savings, and data residency compliance.
Model performance
40% faster Stable Diffusion XL inference with NVIDIA TensorRT
Using NVIDIA TensorRT to optimize each component of the SDXL pipeline, we improved SDXL inference latency by 40% and throughput by 70% on NVIDIA H100 GPUs.
Glossary
Why GPU utilization matters for model inference
Save money on high-traffic model inference workloads by increasing GPU utilization to maximize performance per dollar for LLMs, SDXL, Whisper, and more.
ML models
The best open source large language model
Explore the best open source large language models for 2025 for any budget, license, and use case.
Model performance
Unlocking the full power of NVIDIA H100 GPUs for ML inference with TensorRT
Double or triple throughput at same-or-better latencies by switching to H100 GPUs from A100s for model inference with TensorRT/TensorRT-LLM.
