LLM Benchmark Performance at 3-Bit Quantisation

X post0xSeroDiscussion

A post on X highlights surprisingly high benchmark performance for a language model running at 3-bit quantisation (exl-3bits), achieving a 78.6% score alongside 100 tokens per second at 1400W peak power.

Key points

  1. Achieved a 78.6% benchmark score using 3-bit quantisation (exl-3bits).
  2. Inference throughput reached 100 tokens per second.
  3. Peak power consumption was recorded at 1400W.

Why it matters

Low-bit quantisation techniques aim to preserve model capabilities while boosting throughput, though high-performance setups can still draw substantial power at peak load.

Analysis

The author notes the balance between accuracy and speed at 3-bit quantisation with disbelief. However, the short post does not specify the exact model architecture or hardware hardware used in the test.

Original post

0xSero

@0xSero · 27 July 2026

How the heck is this possible scoring 78.6% @ exl-3bits 100 tok/s 1400w at peak. t.co/v3Tck0uSXJ

91 likes5 replies36 bookmarks11k views

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Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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