Sloth-like speed? More like a context beast. 🦥🚀 Most of us have hit that wall where a 6GB or 8GB GPU just gives up the ghost as soon as you feed it a long PDF. Seeing an implementation of Google's TurboQuant (ICLR 2026) this early is a game-changer for the local LLM scene. Why this is a big deal: - VRAM Magic: It’s basically "downloading more RAM" but for your GPU. Compressing the KV cache from 16-bit to 4-bit means you can actually run massive contexts (32k+ tokens) on a "budget" card like an RTX 3060. - Privacy First: Fully air-gapped. No data leaving your machine, just pure local inference. - No More out of memory (OOM) crashes: The hardware monitoring is a huge touch. Having the UI intercept the C++ logs to prevent crashes makes the experience feel like a pro workstation rather than a fragile script. - A sleek GUI interface to test your GGUF models with ease! Love seeing tools that make elite AI accessible to people who don't have a cluster of H100s in their basement. Can’t wait to see how far we can push the context limits on consumer hardware!

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p/quansloth

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15h ago

Based on Google's TurboQuant — Quansloth runs massive context models on consumer hardware with ease!

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Sloth-like speed? More like a context beast.

Most of us have hit that wall where a 6GB or 8GB GPU just gives up the ghost as soon as you feed it a long PDF. Quansloth (Apache 2.0 License at GitHub) an implementation of TurboQuant (ICLR 2026) this early is a game-changer for the local LLM scene. Why this is a big deal:

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Quansloth

GUI Based on the implementation of Google's TurboQuant

GUI Based on the implementation of Google's TurboQuant

Forum Threads

Based on Google's TurboQuant — Quansloth runs massive context models on consumer hardware with ease!

Forum Threads

Based on Google's TurboQuant — Quansloth runs massive context models on consumer hardware with ease!