Launching today
ZeroGPU
The compute efficient layer for AI inference
701 followers
The compute efficient layer for AI inference
701 followers
The world can't build compute fast enough to keep up with AI demand. So we took a different path. ZeroGPU is AI infrastructure powered by small language models running on a hybrid edge network reusing compute that already exists. Not every task needs a frontier model. Our purpose-built, edge-optimized models run 10x faster, 50% cheaper and offload 70–80% of production tasks to small models with frontier-level accuracy.
the production results with a real customer make the story stronger for me, I always like seeing actual usage examples instead of purely benchmark-based claims.
@shawn_idrees If you'd like to explore it before spending time on a full evaluation, the API docs are probably the best place to start: docs.zerogpu.ai/api-reference/responses.
ZeroGPU is OpenAI-compatible, so the request format should feel very familiar. There's also an interactive playground and dedicated pages for the classification and extraction models, where you can see example inputs, confidence scores, and response formats.
And of course, if you'd like a recommendation for a specific use case, feel free to share a bit about your workload. We'd be happy to point you in the right direction.
Interesting! This would actually save a lot of companies struggling to find some runway right now. Do you guys have your own GPUs?
ZeroGPU
@praneethpike We actually don't need any GPUs. Our models are optimized and trained to run on CPUs. We also support models from hugging face that are optimized for edge and fine tune them to different domains and use cases.
So yes we are faster and cheaper. I see a lot of startups struggling to maintain AI features because of the token bill, this is especially true in developing countries where these costs cannot be passed down to the users.
We are here to make AI more accessible - this tweet by Brian Armstrong from @Coinbase sums up really well.
Netlify
Hey PH fam 👋
Excited to bring ZeroGPU to the global tech and startup community today!
Here's something every AI builder knows but rarely talks about openly:
You're probably overpaying for AI inference. A lot.
Most apps route everything through frontier models like GPT-4 or Claude. Classification. Moderation. PII detection. Document parsing. Tasks that run thousands of times a day inside your app or agent loop.
That's like hiring a rocket scientist to sort your mail. Every. Single. Day.
And then paying them. Every. Single. Time.
At scale? That's not a cost problem. That's a business model problem.
ZeroGPU fixes this by routing your high-volume, repeatable tasks to specialized small and nano language models on an edge inference network. Automatically. No GPU provisioning. No cluster management.
Early customers are already seeing 10x latency improvements with significant cost savings. That's not a rounding error.
What makes this special:
OpenAI-compatible API (drop-in, no rewrite needed)
Purpose-built ZLMs for classification, extraction, moderation, summarization, PII detection + more
Bring your own model and ZeroGPU handles optimization, deployment, and scaling
Frontier models stay focused on what they're actually good at: complex reasoning
When @its_maddy_a first pitched me the idea, I was blown away. It's one of those concepts that sounds obvious in hindsight but nobody had actually built it cleanly for production AI workloads.
And the smartest people in tech are seeing the same shift coming. Brian Armstrong, CEO of Coinbase, is predicting that 80% of workloads will run on 99% cheaper models within 12 to 18 months.
ZeroGPU is already building that infrastructure. Today.
Check it out and drop your questions below! 👇
zero.xyz
@its_maddy_a @thisiskp_ very exciting stuff - congrats!
ZeroGPU
I have the opportunity to work on ZeroGPU as an AI Architect/Engineer, and what excites me the most is the vision behind it: making AI inference more accessible, scalable, and cost-efficient by leveraging distributed edge resources rather than relying solely on centralized GPU infrastructure.
From an engineering perspective, building reliable distributed LLM inference across heterogeneous devices is a fascinating challenge. It requires solving problems around orchestration, latency, fault tolerance, workload distribution, and model execution at scale while maintaining a seamless developer experience.
What impressed me throughout the journey is the team's focus on turning a technically ambitious concept into a practical platform that developers can actually use. As AI adoption continues to grow, infrastructure efficiency becomes just as important as model quality, and I believe decentralized approaches like ZeroGPU will play an increasingly important role in the ecosystem.
Proud to be part of the team building this. Looking forward to seeing what the community creates with it 🚀
ZeroGPU
@nemanja_igic Its been a ride, but this is just beginning. We are on to something big! Thank you!
DIY UX Test
The pay-for-efficiency angle is refreshing when most platforms just bill raw GPU-hours. Curious how you handle cold starts on the serverless layer — that's usually where the "compute-efficient" promise breaks for spiky workloads.
At Dappier, we've been using ZeroGPU in production for several weeks, specifically for a set of classification tasks. It has helped us reduce latency on these tasks vs general purpose LLMs by at least 10x. This latency reduction has helped us to reduce not only our LLM costs significantly but also associated cloud costs that are reliant on the task results.
Stripo.email
Congrats on the launch! 🚀 The idea of moving repetitive AI workloads away from expensive frontier models makes a lot of sense.
@alina_tyslenok_ Thank you! That's exactly the idea. Frontier models are incredible, but a lot of AI volume is repetitive work that can be handled much faster and cheaper with specialized models.