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Qwen3.8-27B model achieves 451K KV-cache on single RTX 5090

A user has successfully configured the Qwen3.8-27B model with vision capabilities and a 451K token KV-cache on a single RTX 5090 GPU. The setup, utilizing vLLM and a specific NVFP4 quantization, achieves an average speed of 120 tokens per second, even with a power limit of 400W. This configuration is noted to be accurate for coding tasks and supports up to three parallel sessions. AI

IMPACT Demonstrates efficient local deployment of large context models on consumer hardware.

RANK_REASON User-level configuration and benchmark of an existing model on consumer hardware.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Qwen3.8-27B model achieves 451K KV-cache on single RTX 5090

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/t4a8945 ·

    Qwen3.8-27B NVFP4 with vision + 451K token KV-cache on one RTX 5090 (power limited to 400W) at 120 tokens/s average

    <!-- SC_OFF --><div class="md"><p>Hello,</p> <p>So I've been trying lots of combinations in that never-ending landscape of options and settings.</p> <p>I wanted a proper quant of 3.8 27B running as fast as possible on my 5090 at 400W, with vision and with as much KV-cache as poss…