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

모델 소개

**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!

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

구체적 한계를 판단할 출처가 부족합니다. 실제 성능, 지연, 데이터 정책, 라이선스와 버전 수명을 확인하세요.

출처별 요금 및 비용 분석

요금은 채널, 지역, 설정에 따라 다릅니다. 각 채널 요금은 개별 표시되며 제공사의 통일 공식 요금이 아닙니다.

공개 평가 및 실행 설정

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

자주 묻는 질문

출처 및 추적 기록

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。