Baseten Blog | Page 1


New in April 2024

Use four new best in class LLMs, stream synthesized speech with XTTS, and deploy models with CI/CD

Hacks & projects

CI/CD for AI model deployments

In this article, we outline a continuous integration and continuous deployment (CI/CD) pipeline for using AI models in production.

Hacks & projects

Streaming real-time text to speech with XTTS V2

In this tutorial, we'll build a streaming endpoint for the XTTS V2 text to speech model with real-time narration and 200 ms time to first chunk.


Continuous vs dynamic batching for AI inference

Learn how to increase throughput with minimal impact on latency during model inference with continuous and dynamic batching.


New in March 2024

Fast Mistral 7B, fractional H100 GPUs, FP8 quantization, and API endpoints for model management.

GPU guides

Using fractional H100 GPUs for efficient model serving

Multi-Instance GPUs enable splitting a single H100 GPU across two model serving instances for performance that matches or beats an A100 GPU at a 20% lower cost.

Model performance

Benchmarking fast Mistral 7B inference

Running Mistral 7B in FP8 on H100 GPUs with TensorRT-LLM, we achieve best in class time to first token and tokens per second on independent benchmarks.

Model performance

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.

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.