TurboQuant+ slashes LLM KV cache memory 4.6x

TurboQuant+ is a significant breakthrough for local inference, effectively solving the VRAM bottleneck for long-context models without the typical accuracy trade-offs.

• –Achieves 4.6x memory reduction and 8x speedup in attention computation using PolarQuant and 1-bit error correction.
• –Optimized for Apple Silicon (Metal kernels for M1-M5) and CUDA, making high-end performance accessible on consumer devices.
• –Seamlessly integrates with llama.cpp, allowing users to run 32k-128k context windows on hardware that previously struggled with 8k.
• –Future-proofs local LLMs by maintaining 100% recall on "needle-in-a-haystack" tests up to 100k+ tokens.