We run the silicon, serve the models, and operate the network that connects them. Train and infer across the US, Singapore, Japan, and Malaysia — with more locations coming.
We don't resell capacity. We provision the silicon, serve the models, and operate the fabric that connects them — so performance, pricing, and roadmap are ours to set, not yours to chase across vendors.
Bare-metal and GPU-accelerated VM capacity on AMD Instinct MI325X and MI355X systems. Reserve clusters for training; spin up VMs for inference, dev, and experimentation.
Frontier open-weight models served on our own AMD Instinct silicon — plus a unified gateway to 104+ models across every major provider. One OpenAI-compatible API for both, billed per million tokens.
A high-throughput, any-to-any network connecting our GPU sites across regions. Unlimited 400G ports — pay a flat port fee, run unlimited traffic. No per-bit egress, no transit surprises.
Dedicated GPU clusters for training, GPU-accelerated VMs for everything else. Reserve bare-metal where you need maximum performance, spin up VMs where you need flexibility. Transparent per-GPU-hour pricing, no committed-spend gymnastics.
We serve frontier open-weight models — Llama, Qwen, DeepSeek, Mixtral, and more — on AMD Instinct GPUs we own and operate. And through the same gateway, we route to 104+ closed and open models across every major provider. One API, one bill, one auth.
LLMs, embeddings, text-to-speech, speech-to-text, image generation, image editing, video generation — all behind a single OpenAI-compatible endpoint. Switch model classes with a single field change in your request.
For open-weight models, your tokens are generated on our GPUs, on our fabric — no third-party API in the path. For closed models, the gateway handles auth, billing, and version routing across every major provider.
AI workloads don't stop at the rack. Our network fabric reaches 44 cities — with multiple data centers wired up in each — so a training job, a dataset, and an inference endpoint can live in different buildings, or different countries, without the network becoming the bottleneck.
Pay a flat port fee, run unlimited traffic. No per-gigabyte egress meters, no transit surcharges between sites. Move datasets, checkpoints, and inference traffic as freely as your budget says you should.
GPU clusters across data centers, edge POPs, and customer cages share one flat address space. No NAT, no overlay tax, no MPLS-era complexity — workloads move sites without re-architecting.
Tuned for the bursty, east-west, loss-sensitive traffic that AI workloads actually generate. Inference flows don't fight training flows; storage doesn't compete with checkpoints.
Per-flow visibility into queue depth, latency, and link utilization. When a training run stalls, we know which span to look at before you do.
We control every layer from the raised floor to the API. That vertical integration is why we can hold latency, throughput, and availability commitments end-to-end — and why our roadmap is ours, not a hyperscaler's.
Every token we serve and every cluster we reserve runs on AMD Instinct accelerators. The flagship MI355X pairs CDNA 4 Matrix Cores with native FP8/FP6/FP4 production compute and 8 TB/s of bandwidth — purpose-built for high-concurrency LLM inference.
For the largest workloads, the MI325X brings 256 GB of HBM3E per GPU, so trillion-parameter models fit in memory with far less sharding. Paired with the open ROCm™ stack and native PyTorch support, you get frontier capacity without proprietary lock-in.
Reserve compute, get an API key, or talk to an engineer about your training schedule. We answer within one business day.