FEATURED POSTS
Inference
Mar 17, 2026
Mini-SGLang-Neuron: Bringing Lightweight LLM Inference to AWS Trainium and Inferentia
A lightweight inference framework integrating SGLang with AWS Neuron to enable efficient LLM serving on Trainium and Inferentia across multi-hardware environments.
Products
Feb 06, 2026
Launch Templates: Infrastructure Portability for Production AI
AI infrastructure should not dictate how you build models.
News
Jan 16, 2026
Yotta Labs Welcomes Jack Dongarra: A Signal for the Next Era of AI Infrastructure
Dr. Jack Dongarra, 2021 ACM A.M. Turing Award recipient and architect of modern performance benchmarking, has joined Yotta Labs as a Technical & Strategic Advisor. As AI infrastructure reaches a new inflection point, Yotta Labs is applying decades of hard-won HPC lessons to build an intelligent orchestration layer for scalable, interoperable GPU systems.
News
Jan 05, 2026
Academic Research Credit Support Program Launch
Artificial intelligence research is advancing at an unprecedented pace — yet access to scalable, reliable compute remains one of the biggest constraints facing researchers today. Across universities, research labs, and independent research communities, ambitious ideas are often slowed by limited GPU availability, high infrastructure costs, and rigid cloud environments not designed for experimentation. Researchers are forced to make tradeoffs: smaller models, fewer experiments, or long wait times for shared resources. At Yotta Labs, we believe infrastructure should enable discovery — not stand in its way. Today, we’re excited to announce the launch of the Yotta Labs Academic Research Support Program, an initiative designed to provide researchers with access to modern, production-grade AI infrastructure, backed by dedicated support and flexible pricing.
Research
Nov 12, 2025
NeuronMM: High-Performance Matrix Multiplication for LLM Inference on AWS Trainium
Enabling high-performance of AI workloads on heterogeneous hardware is one of the major missions at Yotta Labs. Yotta Labs has explored various AI accelerators (such as NVIDIA GPU, AMD GPU, and AWS Trainium) to optimize performance and reduce production costs. Recently, our chief scientist Dong Li, leading a team of researchers, made significant breakthroughs in building high-performance matrix multiplication (matmul) for LLM inference on Trainium. Evaluating with nine datasets and four recent LLMs, we show that NeuronMM largely outperforms the state-of–the-art matmul implemented by AWS on Trainium: at the level of matmul kernel, NeuronMM achieves an average 1.35× speedup (up to 2.22×), which translates to an average 1.66× speedup (up to 2.49×) for end-to-end LLM inference. The code is released at https://github.com/PASAUCMerced/NeuronMM.
Research
Oct 23, 2025
Optimizing Distributed Inference Kernels for AMD DEVELOPER CHALLENGE 2025: All-to-All, GEMM-ReduceScatter, and AllGather-GEMM
This technical report presents our optimization work for the AMD Developer Challenge 2025: Distributed Inference Kernels, where we develop high-performance implementations of three critical distributed GPU kernels for single-node 8× AMD MI300X configurations. We optimize All-to-All communication for Mixture-of-Experts (MoE) models, GEMM-ReduceScatter, and AllGather-GEMM kernels through fine-grained per-token synchronization, kernel fusion techniques, and hardware-aware optimizations that leverage MI300X's 8 XCD architecture. These optimizations demonstrate significant performance improvements through communication-computation overlap, reduced memory allocations, and ROCm-specific tuning, providing practical insights for developers working with distributed kernels on AMD GPUs.
Research
Oct 13, 2025
Performance Optimization for Reinforcement Learning on AMD GPUs
This blog presents our performance optimization and parameter tuning methodology for Reinforcement Learning (RL) workloads using the Verl framework on AMD’s MI300X GPU platform. By capitalizing on the MI300X’s 192GB of unified memory per GPU, we test and optimize the parallelism strategy to minimize the inter-GPU communication on the three phases in GPRO; we also explore the performance with various parallelisms and reveal the nontrivial relationship between the parallelism degree and performance.
News
Sep 23, 2025
NSF SBIR | Decentralized Artificial Intelligence (AI) Computing Operating System for Accessible and Cost-Effective AI
Yotta Labs Awarded Competitive Grant from the U.S. National Science Foundation to Advance Decentralized AI for Accessible and Cost-Effective Computing
Inference
May 14, 2026
Why LLM Inference Has Low GPU Utilization: CPU, PCIe, Memory Bandwidth, and KV Cache Bottlenecks
Low GPU utilization in LLM inference does not always mean the GPU is weak. In many production systems, the real bottleneck comes from CPU overhead, PCIe transfers, memory bandwidth, KV cache pressure, batching strategy, and orchestration inefficiency.
TensorRT-LLM
Distributed Inference
Inference
May 12, 2026
TensorRT-LLM vs vLLM vs SGLang vs TGI: Which Inference Engine Actually Performs Best in Production?
Comparing TensorRT-LLM, vLLM, SGLang, and Hugging Face TGI for production LLM inference. Performance, batching, latency, GPU utilization, deployment complexity, and what actually matters at scale.
TensorRT-LLM
Distributed Inference
Infrastructure
May 06, 2026
Vast.ai Alternatives: How Yotta Labs Compares for Production GPU Workloads
Comparing Vast.ai, Yotta Labs, and CoreWeave for production AI infrastructure, spot GPU pricing, failover, orchestration, and enterprise-scale reliability.
Distributed Inference
Cost Optimization
Hardware
May 06, 2026
Cheapest Alternatives to AWS for RTX 5090 GPU Access With Fast Cold Start Times
For CTOs and AI engineers who need Blackwell-generation performance without hyperscaler pricing. Compare RTX 5090 cloud pricing, cold start performance, serverless deployment options, and production readiness across Yotta Labs, Vast.ai, and RunPod.
GPU Pods
Cost Optimization
Infrastructure
May 04, 2026
Yotta Labs vs RunPod: Which GPU Platform Is Actually Cheaper for Multi-Provider AI Workloads?
Yotta Labs is cheaper than RunPod on most key GPUs for multi-provider AI workloads, including RTX 4090, RTX 5090, and H100. RunPod is slightly cheaper on H200, but Yotta Labs offers broader multi-cloud orchestration, lower vendor lock-in, and cross-cloud failover.
Distributed Inference
Cost Optimization
Infrastructure
May 01, 2026
Best GPU Cloud Platforms for AI Researchers and Developers (2026)
Comparing GPU cloud platforms for AI training, inference, scalability, failover, and multi-cloud deployment across NVIDIA and AMD hardware.
AMD GPU
Distributed Inference
Inference
Apr 29, 2026
Unsloth vs Traditional Fine-Tuning: Faster GRPO Training Explained
Fine-tuning LLMs is evolving beyond brute-force training. In this guide, we break down how Unsloth changes modern fine-tuning workflows, how GRPO improves reasoning performance, and how teams can run these workloads more efficiently across distributed GPU infrastructure.
Distributed Inference
Cost Optimization
Inference
Apr 27, 2026
What Actually Limits LLM Inference Speed? (GPU vs Memory vs KV Cache Explained)
Faster GPUs don’t always mean faster inference. In real-world systems, LLM performance is often limited by memory bandwidth, KV cache behavior, and system design—not raw compute. Here’s what actually determines inference speed at scale.
Batching
Distributed Inference
Products
Apr 26, 2026
How to Build an LLM-as-a-Judge System (SkyRL + GRPO Guide)
LLM evaluation is the real bottleneck in modern AI. In this guide, learn how to build an LLM-as-a-Judge system using SkyRL and deploy it instantly with Yotta Labs—no complex setup required. This guide shows how to automate LLM evaluation using SkyRL.
GPU Pods
Cost Optimization
Deep dives into multi-silicon AI optimization, infrastructure architecture, and the science behind Yotta's performance breakthroughs.