Baseten Blog
Engineering meets ML infrastructure. Dive into curated insights, expert tutorials, and innovative techniques that make deploying ML models less daunting and more accessible. Explore the topics that resonate with today's tech landscape, and empower your developer journey with expert knowledge.
Announcing Baseten’s $75M Series C
Baseten raised a $75M Series C to power mission-critical AI inference for leading AI companies.
Model performance
View all Model performanceHow we built high-throughput embedding, reranker, and classifier inference with TensorRT-LLM
Discover how we optimized embedding, reranker, and classifier inference using TensorRT-LLM, doubling throughput and achieving ultra-low latency at scale.
How multi-node inference works for massive LLMs like DeepSeek-R1
Running DeepSeek-R1 on H100 GPUs requires multi-node inference to connect the 16 H100s needed to hold the model weights.
Driving model performance optimization: 2024 highlights
Baseten's model performance team works to optimize customer models for latency, throughput, quality, cost, features, and developer efficiency.
How we built production-ready speculative decoding with TensorRT-LLM
Our TensorRT-LLM Engine Builder now supports speculative decoding, which can improve LLM inference speeds.
AI Engineering
View all AI EngineeringDeploying custom ComfyUI workflows as APIs
Easily package your ComfyUI workflow to use any custom node or model checkpoint.
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.
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.
How to serve your ComfyUI model behind an API endpoint
This guide details deploying ComfyUI image generation pipelines via API for app integration, using Truss for packaging & production deployment.
Infrastructure
View all InfrastructureAccelerating inference with NVIDIA B200 GPUs
NVIDIA B200 GPUs improve cost, throughput, and latency for use cases like code generation, search, reasoning, agents, and more.
Testing Llama 3.3 70B inference performance on NVIDIA GH200 in Lambda Cloud
The NVIDIA GH200 Superchip combines an NVIDIA Hopper GPU with an ARM CPU via high-bandwidth interconnect
Evaluating NVIDIA H200 Tensor Core GPUs for LLM inference
Are NVIDIA H200 GPUs cost-effective for model inference? We tested an 8xH200 cluster provided by Lambda to discover suitable inference workload profiles.
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.
AI models
View all AI modelsThe best open-source embedding models
Discover the best open-source embedding models for search, RAG, and recommendations—curated picks for performance, speed, and cost-efficiency.
Private, secure DeepSeek-R1 in production in US & EU data centers
Dedicated deployments of DeepSeek-R1 and DeepSeek-V3 offer private, secure, high-performance inference that's cheaper than OpenAI
The best open-source image generation model
Explore the strengths and weaknesses of state-of-the-art image generation models like FLUX.1, Stable Diffusion 3, SDXL Lightning, and Playground 2.5.
Comparing few-step image generation models
Few-step image generation models like LCMs, SDXL Turbo, and SDXL Lightning can generate images fast, but there's a tradeoff when it comes to speed vs quality.
Glossary
View all GlossaryA quick introduction to speculative decoding
Speculative decoding improves LLM inference latency by using a smaller model to generate draft tokens that the larger target model can accept during inference.
Building high-performance compound AI applications with MongoDB Atlas and Baseten
Using MongoDB Atlas and Baseten’s Chains framework for compound AI, you can build high-performance compound AI systems.
Compound AI systems explained
Compound AI systems combine multiple models and processing steps, and are forming the next generation of AI products.
How latent consistency models work
Latent Consistency Models (LCMs) improve on generative AI methods to produce high-quality images in just 2-4 steps, taking less than a second for inference.
Community
View all CommunityBuilding performant embedding workflows with Chroma and Baseten
Integrate Chroma’s open-source vector database with Baseten’s fast inference engine for efficient, real-time embedding inference in your AI-native apps.
SPC hackathon winners build with Llama 3.1 on Baseten
SPC hackathon winner TestNinja and finalist VibeCheck used Baseten to power apps for test generation and mood board creation.
Ten reasons to join Baseten
Baseten is a Series B startup building infrastructure for AI. We're actively hiring for many roles — here are ten reasons to join the Baseten team.
What I learned as a forward-deployed engineer working at an AI startup
My first six months at Baseten exposed me to a huge range of exciting engineering challenges as I learned how to make an impact as a forward-deployed engineer.
Product
View all ProductIntroducing Baseten Embeddings Inference: The fastest embeddings solution available
Baseten Embeddings Inference (BEI) delivers 2x higher throughput and 10% lower latency for production embedding, reranker and classification models at scale.
Baseten Chains is now GA for production compound AI systems
Baseten Chains delivers ultra-low-latency compound AI at scale, with custom hardware per model and simplified model orchestration.
New observability features: activity logging, LLM metrics, and metrics dashboard customization
We added three new observability features for improved monitoring and debugging: an activity log, LLM metrics, and customizable metrics dashboards.
Introducing our Speculative Decoding Engine Builder integration for ultra-low-latency LLM inference
Our new Speculative Decoding integration can cut latency in half for production LLM workloads.
News
View all NewsExport your model inference metrics to your favorite observability tool
Export model inference metrics like response time and hardware utilization to observability platforms like Grafana, New Relic, Datadog, and Prometheus.
Baseten partners with Google Cloud to deliver high-performance AI infrastructure to a broader audience
Baseten is now on Google Cloud Marketplace, empowering organizations with the tools to build and scale AI applications effortlessly.
Introducing Baseten Hybrid: control and flexibility in your cloud and ours
Baseten Hybrid is a multi-cloud solution that enables you to run inference in your cloud—with optional spillover into ours.
Introducing function calling and structured output for open-source and fine-tuned LLMs
Add function calling and structured output capabilities to any open-source or fine-tuned large language model supported by TensorRT-LLM automatically.
