Qwen3.8-Flash-Next-ROCmFP4-FAST-imatrix-GGUF

모델 소개

A 180 B model that runs **entirely on the GPU** of a 128 GB unified-memory box — 87.06 GiB at 4.23 bpw, within 2.5% perplexity of the unquantized model. Sized for the 96 GiB VRAM carve-out of a Strix Halo (Ryzen AI MAX+ 395 / Radeon 8060S). *Throughput against context depth, against our closest mainline-compatible quant run on both this fork and stock llama.cpp — numbers and discussion under Holding up at long context.* - **Everything stays on the GPU.** Not the experts, not the 51.2 B-parameter n-gram table. No host-RAM fallback, no CPU compute in the hot path. - **Fast prefill that holds at depth** — 423 t/s at 512 tokens, 357 t/s at 8k, still 245 t/s at 32k and 138 t/s at 128k. Prefill is compute-bound and the ROCmFP4 types are cheaper to dequantize. Matters most for long prompts, agentic loops, and anything that re-reads a large context. - **Better perplexity than that same mainline tier**, by about two standard errors, and better than AesSedai's IQ4_XS and IQ3_S at 20–30 GB smaller. - **Vision included** — the model's own tower, at f16, losslessly converted. - **Speculative decoding included** — the model's own MTP head. Up to **40 tok/s generation** observed on a Strix Halo with adaptive drafting; acceptance is content-dependent, so the rate moves with what you are generating. - **imatrix-calibrated** on 1540 chunks from two corpora, so the low-bit tensors are fitted to real activations rather than assumed.

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：other。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。