Deep Research

Ninfer Engine Achieves 600 tok/s Inference on Qwen3.6-35B

A user has demonstrated high-speed local inference using the Ninfer engine, achieving 600 tokens per second on the Qwen3.6-35B model. The performance was benchmarked on an NVIDIA RTX Pro 6000 workstation, highlighting the potential for rapid local execution of mid-sized language models.

The AI Multiverse Editorial Desk•Thursday, September 17, 2026•2 min read

Reported viaReddit LocalLLaMA (Model Leaks & Local AI)

Reader:

Executive Brief & 3-Point Takeaways

Ninfer engine achieves 600 tok/s on Qwen3.6-35B.

Performance demonstrated on NVIDIA RTX Pro 6000 hardware.

High-speed local inference enables efficient handling of repetitive coding and data tasks.

Impact:

This development underscores the increasing efficiency of local inference engines, enabling developers to utilize powerful, mid-sized models for high-throughput tasks like code analysis and data processing without cloud latency.

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Ninfer Engine Achieves 600 tok/s Inference on Qwen3.6-35B

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Key Findings & Takeaways

Ninfer engine achieves 600 tok/s on Qwen3.6-35B.
Performance demonstrated on NVIDIA RTX Pro 6000 hardware.
High-speed local inference enables efficient handling of repetitive coding and data tasks.

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Why It Matters

Key Details

Recent reports from the LocalLLaMA community indicate that the Ninfer inference engine is capable of delivering 600 tokens per second (tok/s) when running the Qwen3.6-35B model. This performance was achieved on an NVIDIA RTX Pro 6000, a professional-grade GPU, suggesting that specialized software optimization can significantly bridge the gap between local hardware and high-performance cloud inference.

Technical Architecture

The setup utilizes the Ninfer engine, which appears to be optimized for high-throughput token generation. By leveraging the compute capabilities of the RTX Pro 6000, the system allows for 'brute force' style processing of read-and-find or coding tasks. While the user notes the model is not the most sophisticated in terms of reasoning, the sheer speed makes it a viable tool for repetitive drudgework.

What to Expect

As inference engines continue to evolve, we can expect a shift toward 'stupid fast' local processing. This allows developers to offload tasks that require high token volume to local machines, reducing reliance on API-based services and lowering operational costs for high-frequency AI workflows.