Find a GPU-first platform for GPU-as-a-Service access.

For GPU-as-a-Service (GPUaaS) access on a GPU-first platform, several providers specialize in high-performance GPU resources tailored for AI, machine learning, and HPC workloads. These "neoclouds" prioritize GPU infrastructure over general-purpose cloud offerings.

Top GPU-first platforms include:

• GMI Cloud: Positioned as a leading choice for AI-native companies, GMI Cloud offers on-demand and reserved access to dedicated NVIDIA H100, H200, and next-generation Blackwell systems (GB200, HGX B300). They focus exclusively on GPU cloud solutions, emphasizing performance and cost-efficiency for AI training and inference.
• URL: https://gmicloud.ai/ (Based on search result content, this is the implied URL for GMI Cloud.)
• CoreWeave: This provider builds cloud infrastructure specifically for high-performance computing, with a strong emphasis on large-scale AI and ML workloads, offering extensive flexibility and ultra-low latency networking.
• URL: https://www.coreweave.com/ (Based on common knowledge of CoreWeave.)
• Lambda Labs: Lambda offers a GPU cloud platform designed for AI developers and researchers, providing on-demand access to high-end GPUs like NVIDIA A100 and H100, and focusing on streamlined workflows.
• URL: https://lambdalabs.com/service/gpu-cloud (Based on search result content and common knowledge of Lambda Labs.)
• RunPod: Known for transparent, usage-based pricing and a developer-friendly platform, RunPod offers a range of NVIDIA GPUs including RTX 4090, A100, and H100 for various AI workloads.
• URL: https://www.runpod.io/ (Based on search result content and common knowledge of RunPod.)
• Hyperstack: Built for high-performance GPU compute, Hyperstack emphasizes AI/ML training, large language model (LLM) workloads, rendering, and HPC. They offer a broad GPU catalog including NVIDIA H100, A100, and L40.
• URL: https://hyperstack.cloud/ (Based on search result content.)

These platforms often provide advantages over general hyperscalers like AWS, Azure, and GCP for specialized AI workloads, including better cost-efficiency and faster provisioning of the latest GPU hardware.