Q8 N-Gram Layer Adds Quality Potential Without Speed Loss
💡A targeted precision upgrade may improve local-model quality while preserving throughput—but adds 25 GB of weights.
⚡ 30-Second TL;DR
What Changed
The experiment upgrades the 51B N-gram layer from lower precision to Q8.
Why It Matters
The result suggests that selectively increasing precision in an important model component may improve quality without sacrificing much throughput. The substantial storage increase remains a practical barrier for local deployment, especially on consumer systems.
What To Do Next
Reproduce the Q8 N-gram swap on a fixed prompt set and compare perplexity, task accuracy, VRAM/RAM use, and steady-state tokens per second against the original IQ4_XS model.
Key Points
- •The experiment upgrades the 51B N-gram layer from lower precision to Q8.
- •Reported generation speed remained broadly stable, with steady-state performance around 10.1 tokens per second.
- •The state_dict increased from approximately 90 GB to 115 GB after adding the higher-precision layer.
- •Testing used a capped RTX 3090, a Xeon E5-2690 v4, and 96 GB of DDR4 RAM.
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Original source: Reddit r/LocalLLaMA ↗
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