How Much of a Boost Does TurboQuant Actually Give Your GPU?

TurboQuant (Zandieh et al., ICLR 2026) delivers 4–5× KV cache compression at 3-bit precision with 99.5% attention fidelity. But "4× smaller KV cache" doesn't mean your composite model score goes up 4×. The actual score gain depends on how much of your VRAM was already being eaten by the KV cache — and that varies a lot by GPU. Here's exactly how to think about the boost, and how to measure it for your own hardware.

The Runyard composite score is built from four dimensions weighted by use case: model quality (qual), inference speed, memory fit, and context window headroom. TurboQuant only affects one of those dimensions: context. It doesn't improve model weights, it doesn't speed up matrix multiplications, it doesn't change how well the model fits in VRAM. What it does is allow 4× longer context on the same VRAM — and longer context translates into a higher context score, which feeds into the composite.

The catch: if your GPU already had plenty of VRAM headroom for the context window you were using, TurboQuant's context score boost is modest. If your GPU was severely memory-constrained — capped at 4K or 8K context on a model that wants 32K — TurboQuant can push the context score from 40 to 90+, and the composite score moves significantly.

For a 6GB GPU running Llama 3.1 8B, TurboQuant raises the composite score by roughly 11 points — because the context window was severely capped before. For a 24GB GPU, the gain is only 3 points — the GPU already had enough headroom that the context score was already high. The tighter your VRAM, the bigger TurboQuant's impact.

Runyard calculates context score as: ctxScore = min(100, log10(maxCtx + 1) × 25). With TurboQuant on, maxCtx expands up to 4× (capped at the model's specification limit). A model previously hitting 8K context on a 12GB GPU reaches 32K with TurboQuant — pushing ctxScore from ~62 to ~88. That 26-point jump in ctxScore, weighted at ~20% in the General use case composite, contributes roughly 5 points to the overall score.

• ▸Large context models (32K–128K spec) on memory-constrained GPUs — the biggest gainers
• ▸MoE models like Qwen3-30B-A3B — only active experts load, TQ multiplies an already-efficient cache
• ▸Reasoning models (DeepSeek R1, QwQ) — long chain-of-thought generation eats context fast; TQ keeps the cache in VRAM
• ▸Multilingual models — longer system prompts and document context fit without truncation
• ▸Models already running "Perfect" fit — smallest gain, context was already not the bottleneck

The fastest way to know your exact TurboQuant boost is to run it on Runyard Compare. Set Device A to your current GPU with TurboQuant off. Set Device B to the same GPU. Toggle TurboQuant on Device B. The difference in composite score — row by row, model by model — is your TQ gain. Sort by score to see which models flip from "A wins" to "B wins" once the KV cache compression kicks in.

1. 1.Go to runyard.dev/compare
2. 2.Set both Device A and Device B to your GPU
3. 3.Keep Device A TurboQuant OFF (it has no toggle — it's always Normal)
4. 4.Toggle TurboQuant ON for Device B
5. 5.The score difference in every row = your TurboQuant gain for that model
6. 6.Models where Device B wins = models where TQ pushes you past your current limit