Ternarization Reduces Qwen3-4B Size, But Performance and Speed Vary
Key takeaways
- Post-training ternarization can significantly reduce LLM storage size.
- This compression often leads to uneven degradation in task accuracy and increased perplexity.
- Reduced model size does not automatically mean faster inference; runtime performance must be separately benchmarked.
- Careful trade-off analysis between model size, accuracy, and speed is essential for deployment.
Who benefits
Summary
This study explores post-training ternarization of the Qwen3-4B language model, achieving significant storage reduction (from 8.29 GiB to 3.96 GiB) but observing uneven degradation in task capability and increased perplexity. Preliminary benchmarks also indicate slower inference speeds despite compression.
Why it matters
For professionals deploying large language models in resource-constrained environments, understanding the trade-offs of ultra-low-bit quantization is crucial. This research highlights that while storage can be significantly reduced, careful evaluation of performance degradation and potential inference slowdowns is necessary.
How to implement this in your domain
- 1Evaluate the specific task performance requirements before applying aggressive post-training quantization techniques like ternarization.
- 2Benchmark both storage reduction and inference speed post-quantization, as compression does not automatically translate to faster runtime.
- 3Prioritize tasks where a slight drop in accuracy is acceptable in exchange for significant memory savings.
- 4Explore activation quantization and hardware-specific optimizations in conjunction with weight quantization to achieve end-to-end performance gains.
Original post by Anirudh Malik, M Sparsh Mehra, Poojith Devan
"arXiv:2609.01962v1 Announce Type: new Abstract: Ultra-low-bit language models can reduce storage and memory bandwidth, but a nominal "1.58-bit" label does not fully describe the stored representation, retained capability, or runtime behavior. We study an end-to-end post-training…"
View on XOriginally posted by Anirudh Malik, M Sparsh Mehra, Poojith Devan on X · view source
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