General Compute
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General Compute
ASIC-powered AI inference infrastructure. 7x faster than conventional GPUs and fully compatible with the OpenAI API.
General Information about General Compute
General Compute is defined as a high-performance AI inference infrastructure specifically designed to optimize the execution of large language models (LLMs). Unlike traditional providers that use repurposed graphics processing units (GPUs), this platform utilizes custom AI accelerators (ASICs) built from the ground up exclusively for inference. This technical approach eliminates the overhead of legacy image-processing architectures, providing a much more efficient and faster solution for developers and companies requiring production-grade AI deployments.
The operation of General Compute is based on an optimized hardware architecture that achieves an inference speed up to 7 times faster than conventional GPU-based cloud infrastructures. Thanks to its specialized chips, the tool can reach processing rates of over 1,000 tokens per second, with a Time to First Token (TTFT) of less than 300 milliseconds. This responsiveness is critical for real-time applications, such as coding agents or automated customer service systems running on a remote computer or server.
Developer integration is straightforward and simplified via an OpenAI-compatible API. This allows for the migration of existing workloads by simply changing the base URL and API key in the code, without needing to rewrite application logic. Additionally, the platform offers support for SDKs, webhooks, and MCP, facilitating connections with tools like OpenClaw, a coding agent that can self-configure to use this infrastructure and immediately improve its performance.
Key functional capabilities of General Compute include:
- Custom model deployment: Allows for running your own weights (BYOM) on its optimized infrastructure while maintaining the same speed as pre-configured models.
- On-demand scalability: Offers everything from a self-service API for rapid prototyping to dedicated capacity with 99.9% Service Level Agreements (SLAs) for massive production environments.
- Superior energy efficiency: Its racks consume only 17 kW compared to the 120 kW of equivalent GPU solutions, optimizing resource usage.
- Air-cooled infrastructure: Eliminates the complexity and costs associated with liquid cooling, ensuring a stable operating environment.
This tool is ideal for software engineers and solutions architects looking to reduce AI inference latency and optimize model performance without the limitations of traditional graphics hardware. By focusing solely on the execution phase rather than training, General Compute provides a specialized environment where speed and stability are the top priorities for developing modern AI applications.
Features and Use Cases of General Compute
How General Compute Works
Frequently Asked Questions about General Compute
What sets General Compute apart from other GPU-based inference providers?
Unlike providers that repurpose gaming hardware, General Compute uses ASIC accelerators designed exclusively for inference, achieving speeds seven times faster.
How can I integrate General Compute into my application if I’m already using OpenAI?
Our API is fully compatible with OpenAI, so you only need to change the base URL and your API key in your existing code to get up and running in thirty seconds.
What performance advantages does General Compute’s infrastructure offer?
Our platform allows you to reach over 1,000 tokens per second with a time-to-first-token of less than 300 milliseconds.
Is there a free trial available for General Compute?
Yes, we offer $200 in free credit for new users upon registration, allowing you to test model performance at no initial cost.
Can I use custom models or private weights on your hardware?
Yes, we support the deployment of proprietary models and private weights on our optimized infrastructure, maintaining the same speeds as our standard models.
What is OpenClaw and how does it simplify working with General Compute?
OpenClaw is a programming agent that can be automatically configured to obtain an API key and switch inference providers seamlessly.
Why is General Compute's power consumption lower than traditional GPU clouds?
By using specialized hardware and air cooling, we consume only 17 kilowatts per rack compared to 120 kilowatts for GPUs, which drastically reduces operating costs.
What pricing plans do you offer?
We provide a pay-as-you-go model through our self-service API, as well as dedicated capacity options with service-level agreements (SLAs) for large-scale production environments.
General Compute Pricing
Self-serve API: $200 in free credit for new accounts. Once the credit is exhausted, pricing follows a pay-as-you-go model.
Immediate access to an OpenAI-compatible API.
High-speed inference powered by ASIC accelerators (up to 1,000 tokens per second).
Time to First Token (TTFT) under 300 ms.
Optimized infrastructure with low energy consumption.
Dedicated capacity: Custom pricing (contact sales).
Reserved dedicated infrastructure for production workloads.
Guaranteed capacity and custom scaling.
99.9% uptime SLA.
Direct support for high-volume requirements.
Bring your model: Custom pricing (contact sales).
Deploy private models and weights on optimized infrastructure.
Serving layer specifically tuned to the customer's workload.
Maintain the same inference speeds as the system's standard models.
General Compute Screenshots
