Qwen3.5-4B inference accelerated with quantization and speculative decoding

Researchers have developed an efficient inference system for the Qwen3.5-4B language model, achieving a 6.978x speedup on an NVIDIA A10G GPU. Their approach combines a quantized target model with speculative decoding, employing quantization-aware distillation to maintain accuracy. A specialized drafter model, optimized for the quantized target, was trained in two stages to accelerate decoding. This method also incorporates quantization and sliding-window attention to reduce overhead and improve long-context latency, ultimately ranking third in the Efficient Qwen Competition. AI

IMPACT This research offers practical insights into optimizing LLM inference for resource-constrained environments, potentially lowering deployment costs.

RANK_REASON The item is a research paper detailing a method for efficient inference of a specific LLM. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Bo-Kyeong Kim
• Hugging Face
• NVIDIA A10G
• Qwen3.5 4B

• model release
• infra

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