Baseten Blog | Page 3
33% faster LLM inference with FP8 quantization
Quantizing Mistral 7B to FP8 resulted in near-zero perplexity gains and yielded material performance improvements across latency, throughput, and cost.
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.
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.
Announcing our Series B
We’ve spent the last four and a half years building Baseten to be the most performant, scalable, and reliable way to run your machine learning workloads.
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.
New in February 2024
3x throughput with H100 GPUs, 40% lower SDXL latency with TensorRT, and multimodal open source models.
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.
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.
The best open source large language model
Explore the best open source large language models for 2024 for any budget, license, and use case.
