Launched this week
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 name is 'ZeroGPU' but you mention cloud fallback — so there are still GPUs somewhere. Is the name aspirational, or is there genuinely no GPU in the path for most calls? Curious what the architecture actually looks like.
ZeroGPU
@sneha_reddy12 Fair catch on the name. It's not aspirational we optimized our models to run on CPUs and edge devices, so there's no GPU provisioning and no competing for scarce datacenter GPUs. Our models can run anywhere.
When we mention cloud fallback, it's about consistently delivering on our response-time promise and because our models also run on-prem and within VPC as well, it's how we support enterprise deployments.
But no GPUs were harmed in making ZeroGPU.
@its_maddy_a understood . Thanks for taking time to reply here.
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.
ZeroGPU
@oleksii_sekundant Our models are super light weight making cold starts faster - since they are also edge network the cold start is not a huge latency overhead. For high token volumes we always reserve instances and adapt fast for high spikes.
We also support batch processing which ends up being even more cost efficient.
I feel like a lot of AI apps are probably overusing expensive models by default. Did anything in your benchmark results surprise you?
ZeroGPU
@gizem_ozturk Yes our models outperform frontier nano models in repeated workflows. For example our client @Dappier has seen 10x faster responses and 50% cost reduction with GPT-5.4 nano level intelligence. Our models also hallucinate less as they are specialized and are trained to do one task but do it perfectly.
Thank you for your support!
ZeroGPU
ZeroGPU
@sa206 Our orchestration layer routes the traffic to the closest edge node of origin request.
Our models run on CPU and edge optimized to run anywhere. How we route the task changes based on many factors to keep the latency low. For example - in our case study our p95 is sub-100ms which is unheard of for a real time inference call.
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
Hey, I'm Nishitha,
I am a AI engineer at ZeroGPU,
The past few months building this have been a really rewarding stretch. Getting specialized small models to hold their own against frontier models on real production workloads took a lot of benchmarking and a lot of iteration.
A big part of the work was making it genuinely easy to adopt an OpenAI-compatible API, so you can point your workloads at ZeroGPU and go live without changing your stack. We spent a lot of time making sure the model catalog covers the high-volume tasks that come up again and again, and that each one is fast and reliable in production.
Seeing @Dappier run it in production at 10× lower latency made all of it worth it.
This community has shaped so many products I admire, so it means a lot to share ZeroGPU here. Would love to hear what you think, especially from anyone working on inference at scale.
ZeroGPU
@nishitha_t Yes rewarding and grueling. We are solving hard problems and thank you for being part of this journey. Upward and onward from here.