General Compute

General Compute: The World's Fastest AI Inference Infrastructure

In the rapidly evolving landscape of artificial intelligence, the hardware powering your models is just as critical as the models themselves. Most industry providers currently run AI workloads on repurposed gaming hardware—GPUs that were originally designed for rendering pixels, not processing neural networks. General Compute changes this paradigm by offering the world's fastest inference infrastructure, built from the ground up specifically for AI inference.

By moving away from the legacy architecture of the GPU, General Compute delivers a performance leap that traditional cloud providers cannot match. With speeds up to 7x faster than standard inference and a purpose-built environment, it is time to stop paying the "GPU tax" and experience the future of AI scaling.

What's General Compute?

General Compute is a specialized inference infrastructure provider that utilizes purpose-built AI accelerators (ASICs) to deliver high-speed, low-latency AI performance. Unlike other providers that rely on NVIDIA GPU clouds or repurposed graphics cards, General Compute has skipped 70 years of legacy architecture to build a system where the only job is fast inference.

While GPUs were adapted for training and eventually pressed into inference service, the General Compute hardware was designed from scratch for this single purpose. This specialization allows for a throughput of 1,000 tokens per second and a time to first token of less than 10ms. Whether you are a developer prototyping a new application or an enterprise deploying weights at scale, General Compute provides the hardware, speed, and reliability required for production-grade AI.

Key Features of General Compute

Purpose-Built AI Accelerators

At the heart of General Compute are purpose-built AI accelerators. These are not graphics cards; they are ASICs designed with one job in mind: fast inference. This architectural focus allows General Compute to bypass the overhead associated with rendering-focused hardware, resulting in significantly higher efficiency and speed.

Unmatched Throughput and Latency

Performance is the cornerstone of the General Compute platform. Users can expect:

• 7x Faster Inference: Outperform traditional GPU-based workloads with ease.
• 1,000 Tokens Per Second: High-speed data processing for even the most demanding models.
• <10ms Time to First Token: Eliminate the wait times that plague standard API calls.
• 950+ tok/s on MiniMax M2.5: Benchmark-leading performance compared to the ~100 tok/s seen on competitor hardware.

Extreme Energy Efficiency

General Compute infrastructure is designed for sustainability and cost-effectiveness. Our next-generation racks require only 17 kW, a stark contrast to the 120 kW consumed by GPU equivalents. Furthermore, our infrastructure is air-cooled, removing the expensive liquid cooling overhead often passed down to the customer.

Cost-Effective Infrastructure

By optimizing energy usage and hardware design, General Compute offers energy at $0.035/kWh, which is significantly lower than the US commercial average of $0.13/kWh. This efficiency allows us to provide premium performance without the premium price tag typically associated with high-end AI compute.

Full Developer Ecosystem

General Compute provides a comprehensive suite of tools for developers, including:

• OpenAI-Compatible API: Switch providers by simply changing your base URL and API key.
• Custom Deployments: Dedicated infrastructure with guaranteed capacity and SLAs.
• Bring Your Own Model (BYOM): Deploy your own weights on our optimized ASIC hardware.
• Developer Resources: Access to SDKs, OpenAPI specifications, webhooks, and MCP.

Use Case: OpenClaw and Coding Agents

One of the most powerful use cases for General Compute is powering coding agents like OpenClaw. Coding agents require rapid feedback loops and high throughput to maintain productivity. By connecting OpenClaw to General Compute, developers can achieve much faster inference, allowing the agent to set itself up and execute tasks with minimal delay.

To integrate, you can simply hand a prompt to OpenClaw, and it will grab a General Compute API key and swap its inference provider over automatically. This seamless transition ensures that your development environment is always running on the fastest possible hardware.

How to Use General Compute

Switching to General Compute is designed to be effortless. Because our API is OpenAI-compatible, you can migrate your existing code in as little as 30 seconds without requiring a GPU.

Quick Integration Guide

To start using the General Compute API, update your client configuration as follows:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.generalcompute.com",
    api_key="your-api-key",
)

response = client.chat.completions.create(
    model="gpt-oss-120b",
    messages=[{"role": "user", "content": "Hello!"}],
    stream=True,
)

Steps to Get Started

1. Get an API Key: Sign up to receive $200 in free credit to test the infrastructure.
2. Change Base URL: Point your existing OpenAI SDK to https://api.generalcompute.com.
3. Deploy Models: Use our hosted models (like GPT OSS 120B) or deploy your own custom weights.

FAQ

Q: How does General Compute compare to NVIDIA GPU Cloud? A: General Compute uses purpose-built accelerators rather than GPUs. In benchmarks (such as the MiniMax M2.5 model), General Compute achieves 950 tok/s compared to roughly 100 tok/s on NVIDIA-based clouds. Additionally, General Compute racks use significantly less power (17 kW vs 120 kW).

Q: Is there a cost to switch? A: No. General Compute offers $200 in free credit for new users to see the difference themselves. The API is OpenAI-compatible, so no major code changes are required.

Q: What kind of cooling does your infrastructure use? A: Our infrastructure is entirely air-cooled. This avoids the liquid cooling overhead costs that other providers often pass on to their users.

Q: Can I run my own custom models on General Compute? A: Yes. We support "Bring Your Own Model," allowing you to deploy your own weights on our optimized infrastructure while maintaining the same high speeds.

Q: What is the typical latency (Time to First Token)? A: General Compute typically achieves a time to first token of less than 10ms, though performance can vary slightly by model and geography.

Q: What models are currently supported via API? A: We support various models, including GPT OSS 120B and MiniMax M2.5, through our REST API and OpenAI-compatible endpoints.