Blazing Fast 100 TPS: Qwen3.6-27B Achieves Massive 256k Context Window on a Single RTX 5090

infrastructure #gpu 📝 Blog|Analyzed: Apr 26, 2026 09:19•

Published: Apr 26, 2026 08:37

•

1 min read

Analysis

This showcase is a thrilling demonstration of how community-driven optimization is pushing the boundaries of local Large Language Model (LLM) performance. By utilizing an efficient INT4 quantization and vllm, the developer achieved a blistering 105-108 tokens per second for Inference. This breakthrough ensures that massive, native 256k Context Windows are now highly accessible on consumer hardware, unlocking incredible Scalability for local AI enthusiasts.

Key Takeaways

•Achieves an impressive 105-108 tokens per second generation speed using the AutoRound INT4 quantized model.
•Successfully runs a massive native 256k Context Window on just a single RTX 5090 GPU.
•Leverages vllm 0.19 with MTP speculative decoding and FP8 KV caching for maximum efficiency.

Reference / Citation

View Original

"Thanks to the community the Qwen3.6-27B speed keeps getting better. The following improves upon my recipe from yesterday and delivered a whopping 100+ tps (TG)."

r/LocalLLaMAApr 26, 2026 08:37

* Cited for critical analysis under Article 32.

Older

Stop Guessing Which AI Model is Best — Test Them All at Once with ChatPlayground AI

Newer

OpenAI Enhances Safety Alignment to Prevent Automated Copyright Infringement