Geodd
Geodd is an experienced AI inference operator providing serverless inference, dedicated inference and dedicated GPU infrastructure for production AI workloads. Through DeployPad, customers can deploy supported models, manage inference services and access Geodd’s infrastructure through a single platform. Geodd’s physical datacenter infrastructure and technical capabilities span GPU operations, model deployment, runtime engineering and AI driven optimization. Its autonomous AI agents use hardware specific LLMs trained by the company to develop verified optimizations that improve model efficiency, consistency and reliability from the underlying infrastructure through to model execution.
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Wafer
Wafer delivers the fastest open source LLMs for enterprise through serverless and dedicated inference built for production AI workloads. Its serverless inference gives teams access to top open models with no infrastructure, no deployment overhead, and fast APIs, including GLM-5.2-Fast for low-latency inference with EAGLE speculative decoding and a per-stream throughput SLA, GLM-5.2 as a flagship model with stronger coding and reasoning capabilities, and more. Wafer’s technology uses agents that optimize inference across the stack, identifying and enhancing bottlenecks in orchestration, algorithms, serving engines, GPU kernels, and diverse hardware. It profiles the stack to see whether latency or throughput comes from scheduling, decoding, kernels, memory pressure, or hardware fit, then tries many paths and ships the measured winner. Instead of relying on a single switch or heuristic, Wafer searches model, engine, kernel, and hardware combinations.
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VESSL AI
Build, train, and deploy models faster at scale with fully managed infrastructure, tools, and workflows.
Deploy custom AI & LLMs on any infrastructure in seconds and scale inference with ease. Handle your most demanding tasks with batch job scheduling, only paying with per-second billing. Optimize costs with GPU usage, spot instances, and built-in automatic failover. Train with a single command with YAML, simplifying complex infrastructure setups. Automatically scale up workers during high traffic and scale down to zero during inactivity. Deploy cutting-edge models with persistent endpoints in a serverless environment, optimizing resource usage. Monitor system and inference metrics in real-time, including worker count, GPU utilization, latency, and throughput. Efficiently conduct A/B testing by splitting traffic among multiple models for evaluation.
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Chutes
Chutes is breakthrough serverless compute for AI, at scale: a leading open source, decentralized compute platform for deploying, scaling, and running open-source models in production. Built for hyperscaling AI-powered products, it gives developers high-performance AI inference for top state-of-the-art open source models, ephemeral jobs, batch processing jobs, and much more. Chutes works around the clock to provide the latest open-source models minutes after release, so when a new model lands, builders can get access to what is next first. There is a Chute for everything, not just the LLMs you would expect: Chutes runs image, video, speech, music, embeddings, content moderation, and custom model workloads, always on and ready to scale. With Chutes, teams bring the code and let the platform handle the rest, using fast APIs, the Chutes SDK, or one-click deployments to run serverless AI code without infrastructure setup.
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