Launching today
Galahad: The AI That Never Forgets
Make Any LLM 20x Faster & 100% Accurate
2 followers
Make Any LLM 20x Faster & 100% Accurate
2 followers
Galahad installs on the GPU servers you already rent and makes the same hardware do multiples of the work. Every number measured, sha-256 verified.
Hi Product Hunt 👋 We’re Corbenic, the team behind Galahad.
We built Galahad because we believe AI should have lasting memory that isn’t limited to what fits in a GPU’s VRAM. When models and agents repeatedly process the same context, we can end up paying for work they’ve already done. We wanted to keep that work useful.
Galahad is a persistent memory layer for AI models and agents. It saves reusable model state—the KV cache—to disk, so matching context can be restored instead of computed again, including after a restart. It also provides document memory for applications to store and retrieve relevant knowledge, with integrations for inference stacks such as vLLM and SGLang.
Our goal is to reduce repeated GPU work, make agent workflows more efficient, and lower the cost of running AI where memory reuse makes a difference.
We’re launching in beta. We strongly believe in what we’ve built, and we’re launching now to learn from people using it in real workloads. We want to understand where Galahad helps, what’s missing, which integrations matter, and what needs to work better.
Our longer-term ambition is to make persistent memory a practical part of AI infrastructure, helping models and agents make better use of the context and knowledge we give them without requiring everything to live in expensive GPU memory.
If you’re running models or building agents, we’d love to hear from you: what keeps getting recomputed, and what do you wish your AI could retain?
Thanks for taking a look, asking difficult questions, and helping us shape what comes next.