Bring your own model.
We optimize it and deploy it.
Send us a fine-tuned or proprietary model. The agents find its fastest path, then we deploy it on our heterogeneous GPU cloud, or on yours.
Any model
worth serving fast.
The same loop runs for every model family. Each one gets its own tuning, because what makes a language model fast is not what makes a video model fast.
Language models
Fine-tuned or proprietary LLMs, including mixture-of-experts models with long context windows.
Video models
Diffusion video and audio generation, with custom LoRAs and pipelines around the base model.
World models
Real-time, frame-by-frame models where every millisecond of step time shows up on screen.
From weights to
production.
- 01
Send us your model
A fine-tuned or proprietary model. Weights stay private to you.
- 02
Agents optimize it
The loop profiles, tunes and verifies the model on each target chip, and keeps only what beats the baseline.
- 03
We deploy it
The optimized stack goes live on our GPU cloud, or on your own GPUs inside your own network.
Agents profile, propose, verify and learn. Every pass is faster.
Each pass profiles your model on the target chip, proposes a change, checks it against the baseline and keeps what is faster. Nothing ships on a hunch.
Every layer
of the stack.
From precision to kernels to scheduling. The agents work on these in parallel and merge only the changes that hold up.
Quantization
Lower-precision weights and activations where accuracy holds, higher precision where it does not.
KV cache and prefix reuse
Reuse work across requests that share a long prefix instead of recomputing it.
Speculative decoding
Verify several tokens per pass with a smaller draft model.
Custom kernels
Fused attention and norm kernels written for the exact chip your model runs on.
Batching and scheduling
Pack requests across the fleet so GPUs stop idling between jobs.
Parallelism
Split a model across GPUs along the axis that is not communication-bound.
Our cloud or yours.
Our GPU cloud
A heterogeneous fleet. Your workload runs on the chip where it is fastest and cheapest.
Your cloud
The same optimized stack, deployed on your own GPUs and inside your own network.
No per-model integration work
Onboarding a model is the agents' job, not yours.
Verified against a baseline
Every change is checked against the unoptimized model before it is kept.
Any chip
NVIDIA, AMD, Google TPU and AWS Trainium targets, picked per workload.
Stays optimized
The loop keeps running as new kernels, runtimes and hardware appear.
Common
questions.
- What kinds of models can I bring?
- Open-weight models, fine-tunes and proprietary models: language, video and world models. If you are unsure whether yours fits, email us and we will say so plainly.
- Can it run inside my own network?
- Yes. The deployment can run on your own GPUs inside your network. We agree how the optimization work is done before we start.
- Which hardware can you target?
- NVIDIA, AMD, Google TPU and AWS Trainium. We pick the target per workload, or you tell us the chips you already have.
- How is it priced?
- Private deployments are quoted per engagement, based on the model, the hardware and how it is deployed.
- How do I know the optimized model is as good as mine?
- Every change is verified against the baseline before the loop keeps it. Changes that do not hold up are dropped.
Tell us about
your model.
Email us the model, the hardware you have in mind and whether it should run on our cloud or yours, and we'll be in touch.
contact@zeus.rocks