Questions, answered.

HypaVOLT is a decentralized GPU compute network for AI inference, both on-demand and batch.

What makes it different is the mechanism: our shard process breaks GPU workloads into basic units of electrical consumption and spreads them across a network of low-end GPUs, producing a grid effect of combined computational power.

AI-native builders, infrastructure teams, and enterprises that need affordable raw compute for vectorization, inference, retrieval, search, agentic workflows, and other intelligence-heavy workloads.

Four stages, end to end — from raw data to usable intelligence.

Connect your data. Ingest from APIs, on-chain data, logs, or large datasets.

Distribute compute. Workloads are sharded into basic units of electrical consumption and spread across a global GPU grid, including hardware that hyperscalers ignore.

Vectorize at scale. Process embeddings and transformations across millions of objects in parallel.

Index and serve. Deploy into OpenSearch or your preferred system, ready for real-time querying.

Hyperscalers price every workload for the convenience of their top-end, centralized GPUs. That leaves enormous underutilized consumer and prosumer GPU capacity on the sidelines.

Because HypaVOLT shards work at the electrical-consumption layer, low-end GPUs contribute meaningfully to the same workloads, unlocking a supply pool the hyperscalers structurally can't match.

Node operators get optimal utilization and monetization of hardware they already own, which incentivizes supply to scale. Clients get on-demand and batch inference starting at $0.20 per GPU-hour.

We're not trying to replace hyperscalers for latency-critical single-node serving; we're built for inference at scale, where their pricing model is the bottleneck.

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.