GMI Cloud

GMI Cloud is an AI infrastructure platform offering serverless inference through one API, dedicated GPU clusters and bare metal NVIDIA H100 and H200 servers, with published per-GPU-hour and per-token pricing.

GMI Cloud is an AI infrastructure platform that combines serverless model inference, dedicated GPU clusters and bare metal servers under a single account. It targets teams that run AI in production: startups training foundation models, companies serving generative video or audio, and enterprises or system integrators that need managed access to compute. The company positions itself as an NVIDIA Preferred Partner and builds its GPU offering on NVIDIA Reference Platform Cloud Architecture. In practice, the typical path starts in the web console, where a team calls models through a unified API. Inference is serverless by default, so scaling, request batching and cost-aware scheduling are handled automatically, and capacity scales to zero when idle. The model library lists LLMs from providers such as MiniMax, DeepSeek, OpenAI, Google and Z.ai, alongside image and audio models, with per-million-token pricing for language models and per-request pricing for media models. When shared serverless capacity stops being enough, the same team can move to dedicated bare metal GPUs with root access, custom software stacks and a cluster engine that orchestrates multi-node setups, with RDMA-ready networking for sustained throughput. GPU rental is priced per GPU-hour. The homepage lists NVIDIA H100 at $2.00 and H200 at $2.60, both available now, with Blackwell offered as a pre-order. The site cites customer results such as lower training cost for Mirelo AI and lower inference latency for Higgsfield, though these are vendor-reported figures based on specific workloads. Among alternatives, GMI Cloud sits between hyperscalers and pure model-API providers. It is closer to specialist GPU clouds in pricing transparency and hardware access, but adds a serverless inference layer and a multi-vendor model catalog so that prototypes and production deployments do not require re-architecting. Teams that only need a simple chat API or a no-code tool will find it more infrastructure-oriented than necessary.