Baseten vs NVIDIA

Deploys and serves machine-learning models as production endpoints on managed GPU infrastructure.

Trains and fine-tunes models with reinforcement learning through the Loops SDK, deploying the result onto Baseten's inference stack.

Named inference runtime (automatic TensorRT/SGLang/vLLM builds, speculative decoding, custom kernel fusion, KV-cache optimisation) that underlies Baseten's Dedicated Inference, Model APIs and Training products.

Pre-optimised hosted endpoints for open-source frontier models, called without deploying or managing a deployment first.

Programming toolkit and libraries for running general-purpose computation on NVIDIA GPUs.

Suite of AI technologies (speech, intelligence, animation models and plugins) for building conversational, actionable in-game characters, mostly MIT-licensed with some components under NVIDIA's open model license.

Supported software suite for building and deploying AI workloads on NVIDIA hardware.

Free development-environment manager for creating, customizing and collaborating on AI applications across GPU systems, with enterprise support available through an NVIDIA AI Enterprise license.

Development platform for AI-driven biology and drug discovery: open models, libraries, datasets and NIM microservices for the full AI lifecycle in biopharma.

End-to-end autonomous-vehicle platform spanning training infrastructure, simulation and safety-certified in-vehicle compute for production autonomy from L2++ to L4.

Open-source model-deployment server for TensorRT, PyTorch, ONNX, OpenVINO and other frameworks across GPUs and CPUs, with production support and stable APIs through NVIDIA AI Enterprise.

Open robotics development platform combining simulation, CUDA-accelerated perception libraries, ROS 2 integration and humanoid tooling for building AI-powered robots.

Suite of libraries and microservices covering the AI agent lifecycle - data curation, customisation, evaluation, guardrails and monitoring.

Platform of OpenUSD-based libraries and microservices for building simulation-ready digital twins and synthetic-data environments used to train and validate physical AI and robots.

Suite of open-source, GPU-accelerated data-science libraries (cuDF, cuML, cuGraph, cuxfilter), rebranding to "CUDA-X for Data Science."

Deployable speech-AI library (ASR/TTS) for production inference, free to prototype via build.nvidia.com and licensed for production deployment through NVIDIA AI Enterprise.

Ecosystem of compilers, runtimes and optimization tools for high-performance deep-learning inference; TensorRT-LLM and Model Optimizer are free on GitHub, with commercial deployment options via NVIDIA AI Enterprise.

NVIDIA's AI cloud of GPU clusters and managed AI services, which NVIDIA's own page now presents as its internal environment for building its models, and which is still sold on Microsoft's Azure marketplace.

Prebuilt inference microservices that package foundation models with optimised serving engines and standard APIs, deployable as hosted endpoints or self-hosted containers.