Make every GPU in your fleet competitive.
GPU cloud and managed AI providers need to offer optimized compute across hardware vendors. Gaia automatically optimizes customer workloads for AMD, NVIDIA, and Trainium so every instance in your fleet delivers peak performance. Your customers get choice. You get utilization.
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Your hardware is only as good as the software running on it.
GPU cloud providers compete on performance and price. Gaia makes your full fleet competitive by optimizing every customer workload for the hardware it actually runs on. More hardware options, higher utilization, better margins.
Make every hardware competitive.
Nvidia H200, AMD MI300X, AWS Trainium, and other accelerators match or beat bigger or newer hardware performance when workloads are optimized by Gaia.
Higher utilization, better margins.
When every GPU in your fleet runs optimized workloads, customers actually use the hardware you provision. Utilization goes up. Unit economics improve across the board.
Every GPU in your fleet. Fully optimized.
Offer your customers optimized inference on any accelerator you provision. NVIDIA, AMD, Trainium. Gaia profiles each workload and generates hardware-specific kernels so performance is competitive regardless of vendor. Your fleet becomes a single optimized compute layer.
Customer brings a model. Gaia handles the rest.
Every new customer workload used to mean manual performance engineering for your target hardware. Gaia automates that entirely. Customer uploads a model, Gaia optimizes it for your fleet. No per-customer kernel work. No engineering bottleneck on onboarding.
AMD capacity, finally utilized.
A GPU cloud provider expanding their fleet with AMD MI300X capacity partnered with yasp to close the performance gap that kept customers on NVIDIA-only instances.
"We had millions in AMD hardware sitting underutilized. Within a month of deploying Gaia, AMD utilization matched NVIDIA. Our customers don't care which GPU they're on anymore. They care that it's fast."
Challenge
"The provider added significant AMD MI300X capacity to diversify their fleet and improve margins. But customers wouldn't use AMD instances. Performance on unoptimized workloads lagged NVIDIA by 20-35%. The AMD hardware sat idle while NVIDIA instances ran at capacity. The investment wasn't paying off."
Solution
Gaia optimized customer workloads for MI300X automatically. Within weeks, AMD inference throughput matched or exceeded H100 baselines on the same models. Customers migrated workloads to AMD instances. Utilization on AMD hardware reached parity with NVIDIA. The provider passed cost savings to customers and improved their own margins.
Real numbers, across real hardware.
98%
Performance parity, AMD vs NVIDIA
2.4x
Utilization improvement, non-NVIDIA
Your fleet. Every GPU, fully utilized.
See Gaia optimize your customer workloads across NVIDIA, AMD, and Trainium. Turn idle capacity into competitive advantage.
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.
