Launching today
General Compute
AI models that run on an inference cloud optimized for speed
130 followers
AI models that run on an inference cloud optimized for speed
130 followers
GPUs are built for training, not inference. General Compute is an inference cloud running on ASICs — purpose-built alternatives to Nvidia silicon designed specifically for inference. We deliver 5x faster responses and higher per-user throughput for latency-sensitive workloads like coding and voice agents. Our OpenAI-compatible API means you swap your base URL, keep your existing workflows, and run real-time AI on infrastructure built for the job.
Neutron
Hey Product Hunt, I'm Jason, Co-founder & CTO of General Compute!
The Problem
Agents are the most exciting thing happening in AI right now but the infra they run on was designed for chatbots, not autonomous workflows. When an agent has to make 20, 50, sometimes hundreds of sequential LLM calls to complete a task, latency compounds into a ceiling on what's actually possible.
Most inference providers today hit you with one of two tradeoffs:
❌ GPU-based stacks – Great for training, but memory-bandwidth bottlenecks mean your agent runs slowly (~120 tokens/second)
❌ "Fast" inference with catches – Some providers deliver speed but lock you into small models, limited context windows, or pricing that breaks at agent-scale token volume. Speed without intelligence isn’t worth the trade off.
After years building voice agents and real-time AI products ourselves, we got tired of waiting. So we built General Compute.
How General Compute is Different 🚀
GC is an ASIC-first inference cloud built on multiple chips, including SambaNova. SN uses a 3 tier memory architecture and dataflow, which is a fancy way of saying “It’s really fast cause we don’t have the same bottlenecks”.
🔹 Agent first (OpenClaw) – Agents can sign up on their own and manage their own API keys. OpenClaw can move its inference just by pointing it at our website.
🔹 Built for agent workloads – Tuned for both coding agents and voice AI (TTFT), the things that matter when you're chaining dozens of calls. Your agent finishes in seconds, not minutes.
🔹 Speed without the tradeoffs – Frontier open models, full context windows, and pricing that actually works at production scale.
Who is this for?
If you're building AI agents, voice AI ,or even just using OpenClaw or OpenCode and want faster inference, then GC is built for you. Faster inference isn't just a nice-to-have; it unlocks use cases that weren't viable before.
🔗 Get started today
Sign up at https://generalcompute.com and start running your workloads on ASICs today. We are offering $200 in free credit to anyone that signs up through the Product Hunt launch (up from the normal $5 in credit)
this is a very real agent infra problem. Chatbot latency is annoying, but agent latency compounds into a hard ceiling when workflows need dozens of sequential LLM calls. how General Compute balances raw speed with reasoning quality on longer agent workflows, especially when there is large context, tool use, retries, and coding tasks. Is the biggest gain in TTFT/throughput, or do you also see better end-to-end task completion?
Bababot
Congratulations to the launch.