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Qwen3.8-27B model achieves 159 tokens/sec on R9700 GPU via iPad IDE

LemonSeed Studio, an iPad-based editor and IDE, has demonstrated on-device inference capabilities using an AMD GPU. The system achieved 159 tokens/sec with the Qwen3.8-27B model on an R9700 GPU, and 64 tokens/sec on a Strix Halo GPU. This engine, LSE, is designed to optimize model performance by fusing operations and generating custom kernels for various GPUs, supporting features like speculative decoding for faster inference. AI

IMPACT Demonstrates improved on-device inference capabilities for LLMs on consumer hardware, potentially enabling more powerful mobile AI applications.

RANK_REASON This is a demonstration of a specific software tool's capability with a particular model and hardware, rather than a new model release or significant industry event.

Read on r/LocalLLaMA →

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

Qwen3.8-27B model achieves 159 tokens/sec on R9700 GPU via iPad IDE

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0 / 100
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Tool
This is a demonstration of a specific software tool's capability with a particular model and hardware, rather than a new model release or significant industry event.
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
product, infra
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High
Clearly on-topic for AI-industry coverage.
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2 days old
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/TheOriginalG2 ·

    Qwen3.8-27B: 159 tok/s on R9700, 64 tok/s on Strix Halo

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1x18e95/qwen3827b_159_toks_on_r9700_64_toks_on_strix_halo/"> <img alt="Qwen3.8-27B: 159 tok/s on R9700, 64 tok/s on Strix Halo" src="https://preview.redd.it/3pwkx9zjfcuh1.jpg?width=140&amp;height=105&amp;auto=…