Luma Research Lab builds the cloud stack that turns fragmented, idle, or hard-to-use AI compute into one reliable training and inference substrate — across Trainium, GPUs, spot fleets, and older accelerators. Zero porting. No vendor lock-in.
Our Mission
We believe AI should be open, reproducible, and deployable anywhere — not locked to a single cloud or proprietary stack.
Our sealed compile layer runs any Hugging Face or custom model on AWS Trainium, Nvidia GPUs, AMD ROCm, and Google TPU — with no code changes from the customer. The compiler runs in a Nitro Enclave; only an opaque training artifact leaves, protecting Luma IP.
Mix H100s, A100s, L4s, Trainium nodes, and TPUs into a single logical training job. Luma's orchestration layer abstracts the hardware differences — your training loop sees one cluster, regardless of what's underneath.
Automated checkpointing and failover let reclaimed spot nodes swap out while the job continues — turning volatile spot fleets and idle prior-generation accelerators into reliable, cost-effective training capacity.
How It Works
A cloud stack that solves GPU scarcity — by making every accelerator, spot instance, and idle chip usable for AI training and inference.
Any Hugging Face or customer model runs on AWS Trainium with zero porting work. Luma's compiler runs inside a Nitro Enclave — customers receive an opaque training artifact (NEFF) they can execute but cannot inspect, keeping compiler IP fully protected.
Explore infrastructure →Luma binds a mixture of accelerators — H100s, A100s, L4s, Trainium nodes, TPUs — across the internet into one logical training cluster. No customer-side complexity. One job spec, whatever hardware is available.
Explore infrastructure →Automated checkpointing and failover migrate jobs off reclaimed spot nodes with no lost work. Prior-generation accelerators that would otherwise sit idle become usable again — dramatically expanding available AI compute supply at lower cost.
Explore infrastructure →Once the infrastructure layer is in place, domain-specific fine-tuning runs on whatever compute is cheapest or most available. We deliver fine-tuned checkpoints tailored to specific industries — without the customer managing the hardware complexity.
View model coverage →By the numbers
Model Support
Our infrastructure layer has been validated across all major model architectures — from classic encoders to modern vision-language-action systems and mixture-of-experts.
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