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Qwen3.8-27B Jetson Thor achieves 40 tok/s with INT4 quantization

By PulseAugur Editorial · [1 sources] ·

Embedl has released Qwen3.8-27B Jetson Thor, a quantized large language model that achieves 40 tokens/second with INT4 weights and other optimizations. The model demonstrates accuracy comparable to its FP16 counterpart on benchmarks like GSM8K and IFEval, while utilizing techniques such as FlashHead for faster processing and speculative decoding. This release is available for local deployment and offers an OpenAI-compatible API, with a focus on efficient performance on edge devices. AI

IMPACT Optimized LLM performance for edge devices, potentially enabling more capable local AI applications.

RANK_REASON This is a release of a specific model variant with performance metrics, but not from a frontier lab.

Read on r/LocalLLaMA →

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

Qwen3.8-27B Jetson Thor achieves 40 tok/s with INT4 quantization

How we ranked this

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2 / 100
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Newsworthiness bucket
Tool
This is a release of a specific model variant with performance metrics, but not from a frontier lab.
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Single-source cluster
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
model release, infra
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High
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Same-day
Cluster formed today. Ranking reflects the current source set at time of score.

Full methodology in our editorial standards.

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/No-Dragonfly6246 ·

    Qwen3.8-27B Jetson Thor: 40 tok/s at FP16-level accuracy (INT4 + MTP + FlashHead)

    <table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1xg44nj/qwen3827b_jetson_thor_40_toks_at_fp16level/"> <img alt="Qwen3.8-27B Jetson Thor: 40 tok/s at FP16-level accuracy (INT4 + MTP + FlashHead)" src="https://external-preview.redd.it/A7EAb8HWjl2ONFglktOtYkml…

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