wafer
Wafer is an advanced GPU development toolkit that integrates profiling, documentation, compiler exploration, and AI-powered optimization tools to enhance GPU engineering productivity.
https://www.wafer.ai/?ref=producthunt
Product Information
Updated:Dec 23, 2025
What is wafer
Wafer is a comprehensive development platform designed specifically for GPU engineers and developers, available as both Cursor and VSCode extensions. Founded in 2025 and based in San Francisco, the platform aims to maximize intelligence per watt by optimizing AI infrastructure. It provides an integrated environment that combines essential GPU development tools, documentation, and AI-assisted optimization capabilities, making high-performance GPU development more accessible and efficient.
Key Features of wafer
Wafer is a comprehensive GPU development and optimization platform that combines hardware and software solutions for AI acceleration. It offers tools for GPU profiling, documentation search, compiler exploration, and AI-assisted optimization, all integrated into popular IDEs like VSCode and Cursor. The platform aims to make GPU development more accessible and efficient by providing wafer-scale processing capabilities and tools that typically require specialized expertise.
GPU Profiling and Documentation: Integrated profiling tools and comprehensive GPU documentation search directly within the IDE, allowing developers to analyze and optimize code without context switching
AI-Powered Optimization: Built-in AI agent that can analyze profiling data, suggest optimizations, and automatically tune kernel hyperparameters for improved performance
Cost-Efficient GPU Workspaces: Flexible development environment with persistent CPU containers and on-demand GPU access, reducing costs by up to 95% compared to traditional setups
Compiler Explorer Integration: Real-time compilation of CUDA and CuteDSL code into PTX and SASS with source mapping capabilities
Use Cases of wafer
AI Model Development: Accelerating the development and optimization of large-scale AI models with efficient GPU utilization and automated optimization
High-Performance Computing: Optimizing complex computational workloads for scientific research and data analysis applications
GPU Kernel Engineering: Developing and fine-tuning GPU kernels for maximum performance in specialized applications
Pros
Integrated development environment with comprehensive GPU tools
Cost-effective GPU resource utilization
AI-assisted optimization reducing need for specialized expertise
Cons
May require initial learning curve for new users
Dependency on external GPU resources for full functionality
How to Use wafer
Install Wafer Extension: Install Wafer as an extension in either Cursor or Visual Studio Code IDE through their respective marketplaces
Choose a Pricing Plan: Select from available plans: Start (free with $5 credits/month), Hacker ($16/mo), Pro ($100/mo), or Enterprise (custom). Free tier allows you to try the basic features
Access GPU Development Tools: Once installed, you can access all GPU development tools directly in your IDE, including GPU profiling, documentation search, and compiler explorer
Profile Your Code: Use the integrated NVIDIA Compute Utility (NCU) to profile your GPU code directly from your editor without switching context
Use Documentation Search: Search through CUDA programming guides, API references, and optimization best practices using the built-in documentation search feature
Utilize GPU Workspaces: Develop on a persistent CPU environment and spin up GPU resources only when needed to run code, helping reduce costs
Leverage AI Agent: Use the AI agent to analyze profiling data and receive optimization suggestions for your code
Optimize Parameters: Ask the AI agent to automatically sweep kernel hyperparameters like tile sizes, thread counts, and unroll factors to optimize performance
Review Changes: Review any AI-suggested code changes through the code diff feature before applying them to your codebase
wafer FAQs
Wafer-scale integration is a technology that allows integration of hundreds of thousands of cores and massive on-chip memory onto a single wafer. It's important for AI because it improves efficiency by reducing costly off-chip communication and enables more data to be stored and processed locally, which is crucial for handling large AI models.
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