Lead Developer Advocate
Machine learning infrastructure that just works
Baseten provides all the infrastructure you need to deploy and serve ML models performantly, scalable, and cost-efficiently.
Lead Developer Advocate
Quantizing open-source LLMs to FP8 resulted in near-zero perplexity gains and yielded material performance improvements across latency, throughput, and cost.
Use TensorRT to achieve 40% lower latency for SDXL and sub-200ms time to first token for Mixtral 8x7B on A100 and H100 GPUs.
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.
Multi-cloud and multi-region infrastructure for model serving provides availability, redundancy, lower latency, cost savings, and data residency compliance.
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.
Save money on high-traffic model inference workloads by increasing GPU utilization to maximize performance per dollar for LLMs, SDXL, Whisper, and more.
Explore the best open source large language models for 2025 for any budget, license, and use case.
Double or triple throughput at same-or-better latencies by switching to H100 GPUs from A100s for model inference with TensorRT/TensorRT-LLM.
