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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.
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About Galahad: The AI That Never Forgets on Product Hunt
“Make Any LLM 20x Faster & 100% Accurate ”
Galahad: The AI That Never Forgets was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #68 on the daily leaderboard. 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.
Galahad: The AI That Never Forgets was featured in SaaS (44.9k followers), Software Engineering (43.2k followers) and Artificial Intelligence (481.6k followers) on Product Hunt. Together, these topics include over 198.6k products, making this a competitive space to launch in.
Who hunted Galahad: The AI That Never Forgets?
Galahad: The AI That Never Forgets was hunted by CorbenicAI. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Galahad: The AI That Never Forgets stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
CorbenicAI
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.