“Model inference”

Use for on-device or server inference that is not tied to one vendor model: ONNX Runtime, Sentis, Barracuda legacy, custom .onnx/.tflite/.pb assets, and…

Guide and resources for deployment and development using the ROCm/AMD ATOM (AiTer Optimized Model) inference backend.

Unified inference platform for deploying and scaling AI models on any cloud, with an open-source Python serving framework.

Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers.

Inference and training platform for hosted and dedicated models, deployable in your cloud or Baseten's.

Local AI over Ollama for low connectivity: status, local inference, model and deployment guides.

AI inference pricing for agents: live and historical model prices, provider comparison.

Connects NemoClaw to a local inference server.

Manage your Onde Inference account and models: apps, model catalog, fine-tuned GGUF deploys.

Ground a Stage's work in supplied Artifacts, source material, and explicit confirmations while separating model inference.

Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama…

Deep Infra AI integration for model inference.

OpenAI-compatible multi-model inference via ComputeFlux (Polkadot testnet, TEE-verifiable).

Run or design bulk expression differential analysis for the PA-LUAD feature using edgeR for count-model inference and limma for paired continuous-data checks.

Use when integrating ONNX/Sentis/Unity Inference in Unity: model assets, IWorker, runtime performance, or com.unity.sentis packages.

Use when product or application work adds, changes, reviews, tests, or evaluates model behavior, inference prompts, model context, model tools, routing…

Work with Prime Intellect products via the prime CLI and Python SDKs - verifiers environments and the Environments Hub, evaluations (local and hosted), Hosted…

Open-source platform for serving LLM, embedding and multimodal models on your own premises or in the cloud.

Access to hundreds of open models from partner inference providers through one Hugging Face token and SDK.

Inference infrastructure for AI teams: hosted open-source and fine-tuned models, plus tracing and evaluation.

Use verified service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.

Managed Ray platform for distributed training, batch inference and data processing on GPU clusters.

Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster.

Plain C/C++ LLM inference engine that runs quantized models on many hardware backends, with an OpenAI-compatible server.