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Qwen3.8-27B model hits 880 tok/s on single RTX 5090 with NInfer engine

A user has achieved impressive performance with the Qwen3.8-27B model on a single RTX 5090 GPU, reaching 880 tokens/second with 4-bit NVFP4 quantization and a full 262k context. This speed was attained using the NInfer engine, which the user reports as significantly faster than other engines like llama.cpp. The benchmark results indicate that this NVFP4 quantized model performs comparably to integer quantized versions on tasks like HumanEval+ and AIME, while being substantially faster. AI

IMPACT Demonstrates significant speedups for local LLM inference on consumer hardware, potentially lowering barriers to entry for advanced model usage.

RANK_REASON User-reported performance benchmark of a specific model on consumer hardware using a novel engine.

Read on r/LocalLLaMA →

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

Qwen3.8-27B model hits 880 tok/s on single RTX 5090 with NInfer engine

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/Ond7 ·

    880 tok/s on one 5090 Qwen3.8-27B in 4-bit NVFP4, full 262k context

    <!-- SC_OFF --><div class="md"><p>Numbers first, on a single RTX 5090, Running CachyOS with COSMIC, and the entire desktop costs about 150 MB of VRAM.</p> <p>880 tok/s aggregate at 6 parallel requests (peaked at 967 on one run). 200+ tok/s single stream with MTP speculative decod…