Kernel-level inference optimization across
NVIDIA, AMD, and AWS Trainium. Backed by published research,
open-source tools, and production deployments — no hardware lock-in.
2.3×
SGLang Speedup on Nvidia H200
4.3×
Inference Throughput on AMD GPUs (RL Rollout)
2.09×
NeuronMM Speedup on AWS Trainium
Multi-Silicon Optimization
One Team. Every Chip.
Most providers optimize for one architecture. Yotta has published peer-reviewed research and production-grade kernels across all three major AI silicon platforms.
H200 / H100 / A100
Industry-Standard, Fully Optimized
Yotta's inference stack is battle-tested on NVIDIA's flagship GPUs. From H100 to H200, we deliver maximum throughput through SGLang integration, FP8 quantization, and adaptive VRAM-aware scheduling.
SGLang runtime integration for 2.3× end-to-end speedup
Adaptive frame segmentation maximizing Tensor Core utilization
Near zero-cost LoRA serving (+4s overhead)
FP8 quantization support on H200 4th Gen Tensor Cores
Read: Wan2.x Video Generation (NVIDIA vs. AMD)
2.3×
Wan2.x Video Speedup (SGLang)
65.5%
Preprocessing Reduction
40.1%
Total Latency Reduction
Pioneering Work
SGLang Meets AWS Trainium
Yotta Labs pioneered the integration of SGLang — the leading LLM serving framework — with AWS Trainium hardware. This breakthrough unlocks Trainium's cost efficiency — up to 30–40% better price-performance than NVIDIA H100 EC2 instances per AWS published benchmarks — without sacrificing the developer experience of the SGLang ecosystem.
2.3× End-to-End Speedup
SGLang on H200: 696s → 297s for 20-step video generation
Near Zero-Cost LoRA Serving
+4s overhead to load adapters; near-zero throughput hit on long prompts
NeuronMM Open Source
Custom Trainium matmul kernel available at github.com/PASAUCMerced/NeuronMM
Technical Research
Research That Ships to Production
Our team publishes technical reports on every major optimization. Read
the research, then deploy it on Yotta's platform.
Research
Aug 18, 2026
Why the Slow Operator Is Slow: Attributing GPU Hardware Counters to PyTorch Operators
Wall time tells you where, not why. Operator Profiler attributes the full GPU hardware counter set to PyTorch operators, with evidence behind every number.
Academic Research
Research
Jul 25, 2026
Difflet Engineering Report: Diffusion Inference on AWS Trainium
The full engineering report behind Difflet: architecture, compilation, parallelism, model loading, resident serving, and the measurements that validate each claim.
AWS Trainium
Distributed Inference
Research
Mar 16, 2026
From 11 Minutes to 4 Minutes: End-to-End Acceleration for Wan Video Generation on NVIDIA H200 vs. AMD MI300X
1. Introduction Wan is a diffusion-based generative model for high-quality video generation, producing detailed and temporally consistent outputs via iterative denoising. It showcases strong performance in visual generation tasks, particularly in producing consistent motion and style across frames.
Nvidia GPU
AMD GPU
Why Yotta
Why Yotta for Inference?
The difference between a GPU cloud and an AI infrastructure partner
Kernel-Level Expertise
Our team writes custom GPU kernels — not just wrappers. From NeuronMM on Trainium to fused GEMM kernels on AMD, we optimize at the hardware instruction level.
True Multi-Silicon
Unlike providers locked to NVIDIA, Yotta has published research and production-grade optimizations across NVIDIA, AMD, and AWS Trainium — giving you hardware flexibility without performance compromise.
Research-Backed, Production-Ready
Led by Chief Scientist Dong Li and backed by NSF funding, our optimizations are peer-reviewed, open-sourced, and deployed in real production workloads.
Orchestration-First Philosophy
Performance is determined by orchestration strategy, not just raw hardware. Yotta's DeOS platform routes inference workloads to the optimal silicon based on cost, latency, and availability.
Bring us your model, your hardware, and your latency budget.
We'll show you a deployment that hits it — and what it costs.
