I Tested TurboQuant KV Cache Compression on Consumer GPUs. Here's What Actually Happened.

Summary of TurboQuant Benchmark Results

Overview

TurboQuant (TQ) is a technique designed to compress KV caches in large language models (LLMs), aiming to reduce VRAM usage and enable larger context lengths on consumer GPUs. However, it introduces significant overhead that impacts performance and VRAM usage at smaller contexts.

Key Findings

1. VRAM Usage:

   • At small context sizes (4K-16K), TQ3_0 uses more VRAM than the FP16 baseline.
   • The crossover point where TQ starts saving VRAM varies by model:
     • Llama 8B: around 32K context
     • Qwen 14B: around 16K context
     • Qwen 32B: around 8K context

2. Throughput Penalty:

   • TQ introduces a significant throughput penalty, reducing interactive performance by up to 5x.
   • This makes it unsuitable for real-time applications like chatbots.

Benchmarks and Results

1. Llama 8B (32GB VRAM)
   • At 4K context: FP16 uses 6

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