Serve millions of queries faster and cheaper.
Recommendation engines, search, chatbots, real-time personalization. At consumer scale, every millisecond of latency and every fraction of cost-per-inference compounds into real money. Gaia optimizes inference graphs and kernels automatically, so your serving stack runs faster on whatever hardware gives you the best economics.
Trusted by industry leading partners & customers
We Support
Memberships & Programs
Built for teams where inference cost is the margin.
At millions of queries per day, small gains compound fast. Gaia optimizes the full inference graph automatically, finding speedups that hand-tuned kernels miss, across every GPU you run on.
Maximum QPS, minimum latency.
Gaia profiles your serving model end-to-end, rewrites subgraphs, tunes precision, and optimizes batch scheduling. The result: more queries per GPU-second and tighter tail latency, without touching your model code.
Cost per query drops.
Same throughput on cheaper hardware, or significantly more throughput on the hardware you already have. Gaia makes AMD and Trainium viable alternatives to NVIDIA for workloads that were previously locked in, cutting your serving bill without cutting performance.
Every millisecond, recovered.
More queries per GPU-second.
Batch optimization, graph rewrites, operator fusion, precision tuning. Gaia restructures your inference graph for maximum throughput without degrading accuracy. At consumer scale, a 1.5x QPS gain means half the fleet, or twice the headroom for traffic spikes.
Any GPU.
Best economics.
Serve on NVIDIA H100/H200, AMD MI300X, or AWS Trainium. Pick the GPU with the best price-performance for your workload. Gaia generates optimized kernels for each target, so you are never locked into one vendor's silicon or one vendor's pricing.
Latency SLAs met. Infrastructure cost halved.
A consumer AI company serving recommendation and search at scale uses Gaia to meet strict latency SLAs on AMD hardware, eliminating their dependency on expensive NVIDIA-only infrastructure.
"We were locked into one GPU vendor because our serving stack only ran fast on their hardware. Gaia made AMD viable overnight. Same SLAs, half the cost."
Challenge
"The team's recommendation models had sub-15ms latency SLAs at the 99th percentile. Meeting those targets required top-tier NVIDIA GPUs across their entire serving fleet. AMD hardware was cheaper, but unoptimized inference graphs made it too slow. The infrastructure bill was the largest line item after headcount."
Solution
Gaia optimized the team's inference graphs for AMD MI300X, rewriting subgraphs and generating custom kernels that met the same p99 latency targets. The fleet migrated to a mix of NVIDIA and AMD, picking hardware by economics rather than software compatibility. Serving costs dropped while latency improved.
Real numbers, at real scale.
39%
Average p99 latency reduction
1.9x
Average QPS gain, same hardware
41%
Cost per query reduction
Your serving stack. Faster, on any GPU.
See Gaia optimize your inference workload for the hardware you run today, and the hardware that gives you better economics 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.
