Groundbreaking Qwen3.5 LLM Quantization for 24GB VRAM: Faster Inference on the Horizon!
infrastructure#llm📝 Blog|Analyzed: Feb 26, 2026 06:32•
Published: Feb 25, 2026 22:42
•1 min read
•r/LocalLLaMAAnalysis
This is exciting news for anyone looking to run powerful Generative AI models locally! A new quantization of the Qwen3.5 Large Language Model (LLM) is optimized for 24GB of VRAM, potentially leading to faster inference speeds, especially with Vulkan backends. The focus on specific quantization types offers a fresh approach to model optimization.
Key Takeaways
- •This new quantization is specifically designed to work well with 24GB of VRAM.
- •It leverages legacy llama.cpp quant types (q8_0/q4_0/q4_1) for potential speed improvements.
- •Users are encouraged to test and provide performance feedback on various hardware, including AMD and Mac.
Reference / Citation
View Original"Interestingly it has very good perplexity for the size, and *may be* faster than other leading quants especially on Vulkan backend?"
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