OpenClaw Cuts Token Costs with Local Qwen3.5 9B
Run powerful AI lobsters locally with Qwen3.5 9B ToolHub. Cut token costs, add vision and search for under 6GB VRAM.
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After deploying lobsters like OpenClaw, one of the biggest issues is token consumption.
Beyond session compression, you can also use local models to help ease this problem.
Currently, the model series I use most locally is the Qwen family.
A few days ago, Alibaba’s Tongyi Qianwen open-source team released the Qwen3.5Q small-size model series, including 0.8B, 2B, 4B, and 9B variants.
The 9B version significantly leads its class across multiple dimensions — coding, reasoning, Agent tasks, multilingual support, and image-text understanding — and even surpasses Alibaba’s own larger MoE models in some benchmarks, or approaches GPT-OSS-20B level performance. Downloads on open-source platforms skyrocketed, with Hugging Face and ModelScope instantly flooded with glowing reviews. Elon Musk even personally liked a post on X with the comment: “impressive intelligence density.”
Qwen open-source models are now arguably the top choice for consumer-grade GPUs.
Running Qwen3.5-9B locally requires only 7–12GB of VRAM. With Q4/Q5 quantization, speeds are blazing fast — on my local RTX 4090 24GB GPU, I average 106.40 tokens/s.
