Ship your model to every chip, without per-target perf teams.
Foundation model builders need hardware-agnostic optimization. Gaia automates kernel-level tuning across AMD, NVIDIA, and AWS Trainium so your team ships to any target without dedicated performance engineers per vendor.
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Built for model labs shipping to every hardware target.
One optimization pipeline for every chip your customers run. Gaia handles the kernel work so your ML team stays focused on the model, not the hardware.
One pipeline for every hardware target.
AMD MI300X, NVIDIA H100, H200, AWS Trainium. Gaia optimizes your model for each target from the same source. Add a chip, run Gaia. No new team, no new toolchain, no months of porting.
Machine-speed kernel iteration.
Graph rewrites, precision tuning, memory layout optimization, custom kernel generation. Gaia searches a space of optimizations no human perf engineer can cover manually. It finds speedups your team didn't know existed.
Optimize once. Deploy to MI300X, H100, H200, Trainium.
Gaia takes your model and produces optimized artifacts for each hardware target. No per-chip performance teams. No separate optimization pipelines. One source model, every target your customers need.
Graph rewrites. Precision tuning. Custom kernels.
Gaia doesn't just quantize and call it done. It rewrites compute graphs, tunes numerical precision per layer, generates custom kernels, and validates against your accuracy constraints. Optimization at a depth humans can't sustain across multiple targets.
Let your customers choose the chip. Gaia handles the rest.
Ship your model to customers on AMD, NVIDIA, or Trainium. They pick the hardware that fits their infrastructure. You deliver optimized performance on all of them. No vendor lock-in for you or your customers.
From months of kernel work to a single pipeline.
A model lab shipping a 70B-parameter LLM to three major cloud providers used Gaia to eliminate per-target optimization bottlenecks and ship to all hardware targets from one source.
"We used to treat each hardware target as a separate engineering project. Now we point Gaia at the model and get optimized builds for every chip our customers run. Our team builds better models instead of writing kernels."
Challenge
"Each new hardware target required months of dedicated kernel engineering. The team maintained separate optimization pipelines for AMD, NVIDIA, and Trainium. Adding a cloud provider meant hiring another perf engineer. The optimization backlog, not the model, was the bottleneck to distribution."
Solution
Gaia optimized the 70B model for all three targets from one source. Custom attention kernels, precision tuning, and memory layout were handled automatically per chip. The team collapsed three separate optimization efforts into a single pipeline. New targets now take hours, not quarters.
Products used
Kronos · yasp.agent · yasp.codegen · KernelDB
Real numbers, across real hardware.
3+
Hardware targets from one source
1.4x
Average throughput gain vs vendor default
hours
Not months, per new target
6.25x
Largest verified kernel speedup
Your model. Optimized for every chip.
See Gaia optimize your model for the hardware your customers run today, and the chips they'll run tomorrow.
Ship with Kronos.
Scale with Gaia.
Kronos takes your model from research to a self-contained binary on Nvidia silicon. Gaia delivers maximum throughput across Nvidia, AMD, and AWS Trainium. Same agentic platform under the hood.
Kronos
Path to Nvidia production
PyTorch model in, self-contained binary out. Compiles for Jetson Orin, Drive AGX, H100, H200, and other Nvidia silicon. Custom modules compile natively. Weeks of deployment work collapse into a single guided pipeline.
