Boost Qwen 3.5 Performance with Bf16 KV Cache: A Performance Power-Up!

infrastructure #llm 📝 Blog|Analyzed: Mar 2, 2026 06:33•

Published: Mar 2, 2026 05:13

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1 min read

Analysis

Exciting news for Generative AI enthusiasts! The Qwen 3.5 Large Language Model (LLM) demonstrates significantly improved performance when using a bf16 KV cache. This is a crucial optimization, ensuring optimal inference on local setups and unlocking the full potential of this powerful model.

Key Takeaways

•Qwen 3.5's performance on local engines benefits greatly from the bf16 KV cache configuration.
•The article showcases benchmark results to back the claim of better perplexity scores.
•This optimization applies to local inference setups, like those using llama.cpp.

Reference / Citation

View Original

"If you're running Qwen 3.5 35B A3B locally on engines like llama.cpp, you need to manually set your KV cache to bf16 (-ctk bf16 -ctv bf16) instead of the default fp16."

r/LocalLLaMAMar 2, 2026 05:13

* Cited for critical analysis under Article 32.

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