The AI-native operating system for GPU-scale ML workloads.
We make elastic GPU compute fast, accessible, and production-ready — so engineers can ship models, not manage infrastructure.
| Product | Description |
|---|---|
| Compute Pods | Instant-ready GPU environments on H100/200, B200/300 and beyond |
| Launch Templates | Pre-configured deployment templates for zero-friction project starts |
| Elastic Deployment | Auto-scaling inference and training across regions |
| Model APIs | Unified routing across model providers for cost and latency optimization |
| Quantization Tools | Compress models for faster inference with minimal accuracy loss |
Run large language models in decentralized, heterogeneous environments with computational offloading. Built for teams that need to push inference beyond centralized data centers.
A high-performance matrix multiplication kernel for LLM inference on AWS Trainium. Minimizes data movement across memory hierarchies, maximizes SRAM and compute engine utilization, and eliminates expensive matrix transpose operations. Achieves up to 2.22× kernel-level speedup and 2.49× end-to-end LLM inference speedup with a 4.78× reduction in HBM-SBUF memory traffic.
High-performance distributed GPU kernels for AMD MI300X accelerators, optimizing the primitives that power modern LLMs — all-to-all communication (MoE), GEMM-ReduceScatter (tensor parallelism), and AllGather-GEMM (distributed inference). Built with zero-copy IPC and XCD-aware scheduling across 8 compute dies.
→ AMD Inference Kernels GitHub Repo
- ⚡ On-demand, elastic GPU compute — scale from a single GPU to large clusters, instantly
- 🔒 SOC 2 compliant — enterprise-grade security and compliance baked in
- 🌐 Multi-region availability — reliable uptime for production workloads
- 🧩 Persistent storage — state that survives across deployments
- 🛠️ Batteries included — from quick-start pods to full ML orchestration pipelines
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Multi-silicon. Multi-cloud. One platform built for enterprise AI at any scale.
Thank you for visiting Yotta Labs on GitHub! We look forward to collaborating with you.