Wafer

TL;DR

The best GPU engineers work at Nvidia and OpenAI for $2M+ packages. The rest of us are stuck with PyTorch code that uses <50% of our hardware. We built Herdora for every team that can't afford a GPU optimization expert. Herdora takes your PyTorch and optimizes it with custom kernels. It monitors your inference workloads, catches inefficiencies, and fixes them automatically. You keep shipping fast. Herdora makes it run fast. No CUDA knowledge required, no million-dollar hires needed.

Ask: Are you trying to make your inference faster, writing CUDA by hand, or do you know someone who is? Fill our our interest form or send us an email contact@herdora.com to be added to our pilot program. See below for details.

https://youtu.be/K6nEevvlOro

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🧙 The Problem

Getting AI models to run fast on GPUs requires a rare breed of engineer.

These GPU wizards:

• Command $300k+ base salaries (if you can even find them)
• Spend months hand-writing custom kernels for each model
• Need to rewrite every kernel for new hardware

Even worse: If you want to run on alternative GPUs or other accelerators, you need different experts to rewrite all your kernels. Most teams are stuck choosing between:

• Paying $$$ for GPU talent
• Accepting <50% hardware utilization
• Locked to one chip longer than they want

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🐄 How Herdora solves it

Herdora does what those GPU engineering wizards do, automatically. Feed it your PyTorch code, and it generates the optimized kernels that would normally take months of manual work.

What you get:

• Auto optimization - Your PyTorch goes in, optimized GPU code comes out. Same model, 1.5 to 5x faster. The kernels Herdora writes are what a senior CUDA engineer would write after weeks of profiling and tuning.
• Hardware portability - NVIDIA can’t give you enough capacity? Want to try out AMD’s new MI350 series? Herdora makes your code fast on both in seconds. No rewrites, no performance penalties.
• Production monitoring - Herdora watches your models in production, spots inefficiencies, and fixes them. Think of it as having a GPU expert on-call 24/7.

You write PyTorch. Herdora makes it run like it was hand-tuned by the best in the business. Herdora then monitors prod to track performance. No CUDA expertise needed on your team.

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📢 Who This is For

You should reach out if any of this sounds familiar:

• You're an ML engineer but you don't have time to learn CUDA
• Your cloud bill is killing you because your models only use 30-50% of each GPU
• You want to try cheaper GPUs but can't risk your models running slower

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👥 The Team

• Steven Arellano - Researched LLMs at UChicago and worked as an engineer at Two Sigma, Google, and Sei Labs.
• Emilio Andere - Researched transformers for weather prediction at Argonne National Lab, adversarial ML at SANDLab, and an engineer at Elicit.

🤝 Friends since freshman year at UChicago, where we were also roommates

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🙏 Our Ask

If you're running ML workloads on GPUs (or know someone who is), we'd love to add you to our pilot program. Email us at contact@herdora.com or fill out our interest form with your model type and current GPU setup.

You'll get the same optimizations that companies pay GPU consultants $50k+ to deliver except automated and in days instead of months.

Currently Onboarding:

• ML teams running inference at scale
• Teams hitting latency requirements in production
• Companies with growing GPU compute bills
• Companies evaluating AMD or Intel GPUs

Send us an email: contact@herdora.com

Book a call: https://cal.com/herdora/20min

Interest: https://www.herdora.com/interest

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