General Compute
UnclaimedAccelerate AI inference with purpose-built ASICs, achieving unparalleled speed and efficiency.
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TL;DR - General Compute
- Provides extremely fast AI inference using purpose-built ASICs.
- Offers an OpenAI-compatible API for easy integration and model deployment.
- Significantly reduces energy consumption and latency compared to GPUs.
Pricing: Free plan available
Best for: Growing teams
Pros & Cons
Pros
- Up to 7x faster inference speed compared to GPUs.
- Significantly lower energy consumption (17 kW vs. 120 kW for GPU equivalents).
- Lower energy cost ($0.035/kWh vs. $0.13 US commercial average).
- OpenAI-compatible API allows for quick and easy migration.
- Offers $200 free credit to try the service.
Cons
- Specific performance metrics (e.g., 0x faster, 0ms TTT) are presented with asterisks, indicating variability.
- Requires switching inference provider, which might involve some configuration for existing setups.
- The primary focus is on inference, not AI model training.
Key Features
Purpose-built AI accelerators (ASICs)OpenAI-compatible REST APISupport for deploying custom models (Bring Your Own Model)Custom deployments with SLAs and guaranteed capacityReal-time inference benchmark comparison toolSDKs, OpenAPI, and webhooks for developers
Pricing
Freemium
General Compute offers a generous free tier with optional paid upgrades for advanced features.
What is General Compute?
General Compute offers the world's fastest AI inference by utilizing purpose-built ASICs, rather than repurposed gaming GPUs. This specialized hardware is designed from scratch for AI inference, providing significantly higher throughput, lower energy consumption, and reduced latency compared to traditional GPU infrastructure. It aims to solve the 'GPU tax' problem by offering a more efficient and cost-effective solution for deploying AI models.
The platform is ideal for developers and organizations running large language models and other AI workloads that require high-speed, low-latency inference. It provides an OpenAI-compatible API, allowing for easy integration into existing applications with minimal code changes. Users can deploy their own models or leverage General Compute's optimized infrastructure, benefiting from features like custom deployments with SLAs and guaranteed capacity. The service also offers a free credit to help users experience the performance difference firsthand.
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General Compute FAQ
How does General Compute achieve faster AI inference compared to traditional GPU clouds?
General Compute achieves faster AI inference by using purpose-built ASICs (Application-Specific Integrated Circuits) designed specifically for AI inference tasks. Unlike GPUs, which were originally built for graphics and adapted for AI, these ASICs are optimized from the ground up for efficient and high-speed model execution, leading to significantly higher throughput and lower latency.
What is the 'OpenAI-compatible API' and how does it simplify integration?
The OpenAI-compatible API means that General Compute's API endpoints mimic the structure and functionality of OpenAI's API. This allows developers to switch their AI inference provider to General Compute by simply changing the base URL and API key in their existing code, without needing to rewrite their application logic or model integration.
Can I deploy my own custom AI models on General Compute's infrastructure?
Yes, General Compute supports deploying your own models. You can bring your own weights and deploy them on their optimized infrastructure, benefiting from the same speed and efficiency provided by their purpose-built accelerators.
What are the environmental and cost benefits of using General Compute over GPU-based inference?
General Compute offers significant environmental and cost benefits due to its energy-efficient ASICs. It consumes substantially less power (e.g., 17 kW per rack compared to 120 kW for GPU equivalents) and operates at a lower energy cost ($0.035/kWh vs. the US commercial average of $0.13/kWh), leading to reduced operational expenses and a smaller carbon footprint.
How does General Compute's 'OpenClaw' integration work for coding agents?
OpenClaw is a feature designed for coding agents. By providing a specific prompt to OpenClaw, it can automatically obtain a General Compute API key and reconfigure itself to use General Compute as its inference provider, enabling faster execution of agent-based tasks. A full walkthrough is available in their documentation.
What kind of performance can I expect for a model like GPT OSS 120B on General Compute?
While performance varies by model and geography, General Compute benchmarks show a significant improvement. For instance, a MiniMax M2.5 model achieved 950 tokens/second on General Compute compared to approximately 100 tokens/second on NVIDIA GPU Cloud, indicating a substantial increase in throughput for large language models like GPT OSS 120B.
Source: generalcompute.com