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AIモデルライブラリ/Greatjedi/Qwen3.8-27B-Intel-Arc-Tuned-GGUF
モデル情報

Qwen3.8-27B-Intel-Arc-Tuned-GGUF

Greatjedi · Qwen3.8-27B-Intel-Arc-Tuned-GGUF

**Hardware-Accelerated Weights for Intel Arc GPUs (Battlemage Xe2 / Alchemist Xe / oneAPI SYCL)** *Engineered by Greatjedi & Gemini (DeepMind Antigravity Team)* If you own an **Intel Arc GPU** (such as the **Intel Arc Pro B60 24GB**, Arc A770 16GB, or Battlemage graphics cards) and have attempted to run popular 27B quantization variants (such as `IQ2_S`, `IQ3_XXS`, or Unsloth's "Dynamic" `UD-Q4_K_S`), you likely noticed your generation speed crawling at an agonizing **8.1 to 12.2 tokens/second**, despite the GPU having more than enough raw compute and 512 GB/s memory bandwidth. 1. **Intel Arc Matrix Engines (XMX)** utilize **DPAS** (Dot Product Accumulate Systolic) hardware. DPAS requires dense, linearly indexed integers (`INT2`, `INT4`, `INT8`) to feed its systolic pipeline at thousands of operations per clock cycle. 2. **I-Quants (`IQ2_S`, `IQ3_XXS`, `IQ4_XS`, `IQ3_S`)** use non-linear 8-dimensional vector lattice codebooks ($E_8$). They require runtime table lookups per weight element. 3. In `llama.cpp`'s SYCL backend, **there are zero kernel implementations for I-Quants**. Whenever an IQ tensor is evaluated, SYCL drops out of hardware systolic GEMM into slow CPU/host dequantization loops. 4. **The Hidden Catch in Unsloth "UD" Models**: When we reverse-engineered Unsloth's popular `Qwen3.8-27B-UD-Q4_K_S.gguf`, we discovered it secretly contains **201 I-Quant tensors** (`IQ4_XS`, `IQ3_S`, `IQ3_XXS`), causing it to suffer from the exact same dequantization stall on Intel Arc!

翻訳待ちのため原文を表示しています。

テキスト生成license:apache-2.0
コンテキスト長非公開
入力 → 出力テキスト → テキスト
モデルウェイト未確認
情報更新日時2026/11/2 19:16:35
OVERVIEW

モデル紹介

**Hardware-Accelerated Weights for Intel Arc GPUs (Battlemage Xe2 / Alchemist Xe / oneAPI SYCL)** *Engineered by Greatjedi & Gemini (DeepMind Antigravity Team)* If you own an **Intel Arc GPU** (such as the **Intel Arc Pro B60 24GB**, Arc A770 16GB, or Battlemage graphics cards) and have attempted to run popular 27B quantization variants (such as `IQ2_S`, `IQ3_XXS`, or Unsloth's "Dynamic" `UD-Q4_K_S`), you likely noticed your generation speed crawling at an agonizing **8.1 to 12.2 tokens/second**, despite the GPU having more than enough raw compute and 512 GB/s memory bandwidth. 1. **Intel Arc Matrix Engines (XMX)** utilize **DPAS** (Dot Product Accumulate Systolic) hardware. DPAS requires dense, linearly indexed integers (`INT2`, `INT4`, `INT8`) to feed its systolic pipeline at thousands of operations per clock cycle. 2. **I-Quants (`IQ2_S`, `IQ3_XXS`, `IQ4_XS`, `IQ3_S`)** use non-linear 8-dimensional vector lattice codebooks ($E_8$). They require runtime table lookups per weight element. 3. In `llama.cpp`'s SYCL backend, **there are zero kernel implementations for I-Quants**. Whenever an IQ tensor is evaluated, SYCL drops out of hardware systolic GEMM into slow CPU/host dequantization loops. 4. **The Hidden Catch in Unsloth "UD" Models**: When we reverse-engineered Unsloth's popular `Qwen3.8-27B-UD-Q4_K_S.gguf`, we discovered it secretly contains **201 I-Quant tensors** (`IQ4_XS`, `IQ3_S`, `IQ3_XXS`), causing it to suffer from the exact same dequantization stall on Intel Arc!

主な特徴

特点摘要待依据来源补齐，不凭模型名称推断能力。

制約と注意点

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INDEPENDENT BENCHMARKS

公開評価と実行設定

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よくある質問

オープンウェイトなら無料で商用利用できますか？

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出典と追跡記録

huggingface · Greatjedi/Qwen3.8-27B-Intel-Arc-Tuned-GGUF ↗2026/10/29 20:28:11
huggingface · enrich:e95e52a1-5e66-4182-ab5b-6fe7fb10feca ↗2026/10/30 08:03:48

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。