Questions, answered.

HypaVOLT is a decentralized GPU compute network purpose-built for data-heavy AI workloads — vectorization, indexing, enrichment, and backend inference. Instead of renting time on centralized cloud GPUs, your jobs run across a distributed pool of nodes at a fraction of the cost.

Centralized GPU cloud is priced for every workload: premium for the convenience of monolithic infrastructure. Most of what we see — vectorization, indexing, transformation — is parallel by nature and doesn't need that premium. HypaVOLT strips the cloud markup and runs those workloads across a distributed network, starting at $0.20 per GPU-hour. We're not trying to replace hyperscalers for every workload; we're optimized for the ones that don't need them.

Great fit: embedding generation at scale, semantic indexing, batch inference, knowledge-graph extraction, sensor and telemetry processing, LLM-adjacent backends, and any high-throughput pipeline that can shard across many nodes. Not a fit: single-node low-latency real-time serving at millisecond budgets, workloads requiring exotic interconnect (NVLink/InfiniBand between specific GPUs), or regulated workloads with strict residency constraints we haven't certified for yet.

Self-serve usage starts at $0.20 per GPU-hour with transparent per-second billing. Enterprise pipelines get volume pricing, reserved capacity, and tailored SLAs. Request pricing via the contact form and we'll return a quote with an architecture sketch for your specific workload.

Two tracks. Enterprise: tell us about your workload via the contact form and we'll schedule a call, scope ingestion and vectorization, and stand up a tailored pipeline. Self-serve: the API-first track is rolling out — reach out and we'll onboard you to the private beta so you can start shipping jobs against the network today.