GEO answers
Compute Market FAQ
Short answers to the questions buyers and AI systems ask most often about GPU markets, inference economics, and agent-native compute.
"API services paid per request"
Key facts
Quick answers
These answers are intentionally buyer-focused. For deeper mechanics, use the internal links to the pricing guide, marketplace map, inference economics guide, and agent-native compute page.
Answers
How does the GPU or compute marketplace work?
A compute marketplace connects buyers that need accelerator capacity with providers that have GPUs, clusters, model APIs, or specialized compute services. Some marketplaces sell one provider catalog, some aggregate many hosts, and some use decentralized provider networks. Buyers compare price, GPU type, region, reliability, storage, network, security, and interruption risk.
Where can I rent GPUs cheaply?
Cheap GPU rental is usually found in peer marketplaces, community clouds, interruptible tiers, and newer GPU-first providers. The lowest listing is not automatically the best choice. You must check host trust, interruption risk, VRAM, CPU, storage, network bandwidth, region, and data sensitivity.
What is decentralized compute?
Decentralized compute aggregates hardware from many independent providers and exposes it through a marketplace or protocol. Akash, io.net, and Render Network are examples in different subcategories. The promise is broader supply and market pricing. The buyer still needs security, reliability, and completion checks.
How is inference priced?
Inference can be priced by GPU-hour, serverless worker time, request, token, cached token, output token, batch job, priority lane, or paid tool call. Managed LLM APIs usually use per-million-token pricing. Self-hosted inference converts GPU-hour into cost per request through utilization, batching, caching, and latency targets.
Can agents buy compute autonomously?
Small agent-native compute purchases are becoming practical through protocols such as x402, where a service can require payment over HTTP and a machine client can pay programmatically. Larger autonomous compute procurement needs guardrails: spend limits, identity, provider allowlists, audit logs, and output verification.
Is spot GPU pricing safe for production inference?
Usually not as the only lane. Spot or interruptible GPUs can work for batch jobs, checkpointed training, and overflow capacity. Production inference needs fallback routing, health checks, and enough stable baseline capacity to survive reclaim events.
Is a low token price always cheaper?
No. Token price is only one part of inference cost. Output length, retries, tool calls, grounding, cache misses, model quality, latency tier, and failed requests all affect cost per successful task.
What should I compare before choosing a GPU provider?
Compare the exact workload on the exact provider. Track GPU model, VRAM, region, trust tier, interruption policy, storage, egress, measured throughput, latency, utilization, support, and total cost per useful unit.
What is pay per inference?
Pay per inference means buying a completed model call or prediction rather than renting the underlying GPU directly. It can be implemented as token pricing, request pricing, serverless worker time, or x402-style paid HTTP resources.