Startups Directory

Nscale builds and operates AI-native data centers providing vertically integrated GPU cloud infrastructure for training, fine-tuning, and running large language models. Unlike competitors reliant on expensive third-party colocation, Nscale owns and operates its own facilities, enabling cost-effective deployment of 10,000+ GPU clusters with up to 20 MW of contiguous capacity. The company offers serverless inference, managed Kubernetes orchestration, and domain-specific model fine-tuning through an API-driven platform with pay-per-use pricing.

Cerebras Systems designs and manufactures wafer-scale AI processors (WSE-3) that are 57x larger than leading GPUs, enabling faster AI training and inference with dramatically reduced power consumption. The company offers both on-premise hardware systems and cloud-based AI services through its own data centers. Built by a team of veterans from SeaMicro and AMD, Cerebras solves the fundamental problem of data movement in AI workloads by keeping computation on-silicon, delivering measurable speed and efficiency advantages for large-scale machine learning.

Ayar Labs develops TeraPHY, an industry-first optical I/O chiplet that replaces traditional electrical interconnects in semiconductor systems using silicon photonics. The solution delivers up to 1000x bandwidth density improvements and 1/10th the power consumption compared to conventional electrical I/O, enabling 8 Tbps of bandwidth at 10-nanosecond latencies. Built for AI accelerators, data centers, and distributed computing systems, TeraPHY eliminates GPU communication bottlenecks and solves the memory wall problem in high-performance computing.

Baseten is a serverless inference platform that converts machine learning models into production-ready APIs with auto-scaling GPU access across multiple cloud providers. It abstracts away infrastructure complexity—from GPU management and autoscaling to observability and billing—enabling ML teams to deploy and scale models without building custom infrastructure. The platform delivers up to 40% cost savings compared to in-house solutions and targets four to five nines of availability through model optimization and multi-cloud capacity management.

Axelera AI develops purpose-built hardware and software platforms that accelerate AI inference at the edge using proprietary digital in-memory computing (D-IMC) technology. Their flagship Metis™ AI Platform combines CPU and memory into a single chip, eliminating energy-intensive data movement between cloud and device, delivering 10x better energy efficiency than standard computing. The company serves industrial, defense, robotics, and retail customers who need low-latency, privacy-preserving AI processing on devices rather than cloud.

Positron AI designs purpose-built FPGA-based hardware (Atlas) optimized for generative AI inference workloads, achieving 93% memory bandwidth utilization versus 10-30% in GPU systems while consuming less than a third of the power of Nvidia H100s. The company serves enterprises and cloud providers running transformer-based models at scale, from content moderation to token-as-a-service platforms. Their next-generation chip (Asimov/Titan) targets memory-intensive applications like video, trading, and multi-trillion parameter models with 6x more RAM than competing solutions.

ScaleOps is an autonomous platform that continuously monitors and optimizes Kubernetes and AI infrastructure resources in real-time, dynamically adjusting CPU, memory, GPU, and replica counts based on live workload behavior. It eliminates manual configuration and operates with full context awareness to prevent performance issues and downtime that plague other automation tools. Customers typically achieve up to 80% cost reduction while improving performance and stability across their infrastructure.