What is General Compute?

General Compute provides an OpenAI-compatible inference API backed by purpose-built ASIC accelerators for AI inference.It offers high throughput (example.minimax m2.5 ≈950 tokens/sec) and lower rack-level power use (example.

≈17 kW versus ≈120 kW for comparable GPU racks), improving tokens/sec and energy efficiency for production workloads.Developers can switch providers by changing the base URL and API key so existing OpenAI-based code runs without other changes.

API features include REST endpoints, OpenAPI specs, SDKs, webhooks, and streaming responses for real-time applications.Deployment options cover shared models, dedicated infrastructure with SLAs, custom scaling, and guaranteed capacity for sustained workloads.

Typical use cases include prototyping, model deployment, live inference and benchmarking for large language models such as gpt-oss-120b.

General Compute pricing Freemium

Self-serve api $0

Dedicated capacity custom

Verify on the official pricing page.

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General Compute's key features

OpenAI-compatible inference API (drop-in via base URL and API key)
Purpose-built ASIC accelerators for AI inference
High-throughput inference
Developer APIs: REST endpoints, OpenAPI specs, SDKs, webhooks, and streaming responses
Flexible deployment options: shared models, dedicated infrastructure with SLAs, custom scaling, and guaranteed capacity

General Compute use cases

Deploy a low-latency, high-throughput conversational AI for customer support using General Compute's drop-in OpenAI-compatible API running on ASIC-powered inference, leveraging real-time streaming, webhooks and existing SDKs for seamless integration and predictable scale
Serve large-scale personalized recommendations and search at peak traffic using dedicated, guaranteed-capacity ASIC inference to ensure consistent latency and energy-efficient model serving, integrated via REST/OpenAPI or SDKs for batch and streaming inference
Migrate mission-critical, compliance-sensitive LLM workloads to a dedicated inference deployment to guarantee capacity and performance, reduce operational costs with energy-efficient ASICs, and connect to event-driven pipelines using webhooks and streaming without changing your OpenAI-compatible code