Google TurboQuant
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    Google TurboQuant: 6x Memory Savings for LLM Inference.

    TurboQuant bridges the gap between theoretical math and actual GPU hardware. Unlike older VQ methods that bottleneck on high-dimensional data, it uses a data-oblivious rotation trick to hit near-optimal distortion. Bottom line: you get the compression limits promised by Shannon's theory, but with the speed required for real-world inference.
    Read Full Paper on arXiv
    Technical Background

    Why KV Cache is Actually What’s Killing Your Inference Budget

    If you're running LLMs in production, you quickly realize the model weights aren't your biggest headache—it's the KV cache. Here’s the reality of why it’s such a money pit:

    • •It’s a Memory Hog. While model weights stay fixed, the KV cache just keeps growing. For 32k or 128k context windows, it can easily eat up gigabytes per request. You end up buying $30k GPUs not for the compute, but just to hold all those keys and values.
    • •It Hits the Bandwidth Wall. People think inference is about TFLOPS, but it’s really about memory bandwidth. Every new token means shuffling that huge cache around the GPU. It’s like trying to drink a gallon of water through a straw—your hardware just sits idle, waiting for data.
    • •It’s a Scaling Trap. Long context is everywhere now, but cost scales linearly with it. Longer prompts = bigger cache = fewer concurrent users. It absolutely crushes your margins.

    This is why quantization has become non-negotiable. Tools like TurboQuant aren’t just nice-to-have—they’re how you make LLM unit economics actually work at scale.

    Inside TurboQuant

    The Two-Stage Magic That Saves Your VRAM

    Stage 1: PolarQuant (The Heavy Lifter)
    First, we hit the KV cache with PolarQuant—our zero-overhead polar quantization. It spins vectors into polar coordinates, turning messy, hard-to-compress data into clean, uniform angles that compress down to 3 bitswithout any extra metadata. No scale factors, no zero points—just pure, unadulterated compression.
    Stage 2: QJL (The Precision Polish)
    We don’t stop there. A 1-bit residual transform (QJL) cleans up any tiny errors from the first stage, keeping inference quality near FP32 levels. It’s like sanding down a rough edge—small effort, huge payoff. The result? Up to 8x VRAM savings. You’ll run 32k+ context windows on GPUs you already own, serve more users at once, and stop letting KV cache bloat eat into your margins. This isn’t just “quantization”—it’s a VRAM rescue mission. And it’s why TurboQuant stands out from every other “compression tool” on the market.
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