Qwen3.8-Flash-Next-NVIDIA-NVFP4-Strata

モデル紹介

> **Quantized by NVIDIA.** These are the weights of NVIDIA's checkpoint > `nvidia/Qwen3.8-Flash-Next-NVFP4` (NVIDIA Model > Optimizer), unchanged. This repository only converts their file format for Strata NVFP4. It is not made or endorsed by > NVIDIA. - **Accuracy:** how often the model's most likely next token is the Q8_0 model's. Measured on 5,153 tokens of code, docs, English and Ukrainian chats and agent transcripts, every model fed the same text. Every model ran with its experts on the CPU and the route tail skip off, so only the quantization differs. The Q8_0 model also has Q8_0 attention, Qwen's BF16 router and Qwen's own FP8 PLE, so it is the original's precision everywhere but the 8-bit weights. - **Speed:** RTX 5090 (32 GB), Ryzen 9 9950X3D, 128 GB of DDR5-5600, Windows 11, Strata NVFP4 0.1.42-nvfp4.1's engine with the arguments 0.1.42-nvfp4.2's setup writes for each model. Median of 3 runs: 4 greedy chats (up to 1,500 tokens each) and one 32,007-token prompt. - **RAM:** "RAM in use" is the engine and the server at the speed shown; "PC for this speed" is the installed RAM that holds it with Windows beside it. Smaller PCs still run the models, slower: the NVFP4 GPTQ and NVIDIA's on a 64 GB PC in the low-RAM mode decode ~115 tokens/s and read a 32K prompt in ~9 s (docs/NVFP4.md, "64 GB of RAM"); Unsloth's files go into a RAM-budget mode there and read experts from the SSD (UD-Q4_K_XL: 7-8.5 tokens/s at 64 GB, upstream's measurement); setup's minimum is 48 GB for them and the ISTA 2-bit files, 60-64 GB for the ISTA 3-bit ones. - **Unsloth's files keep every expert in RAM where they fit with 16 GiB more** (setup from 0.1.42-nvfp4.2): from ~88 GB of RAM for UD-Q4_K_XL and ~68 GB for UD-Q3_K_XL. Below that, setup's RAM-budget mode reads the rest from the GGUF on the SSD. Setup before 0.1.42-nvfp4.2 used the budget mode on every PC; on 128 GB UD-Q4_K_XL then decoded 118 tokens/s and read the 32K prompt in 30 s (run setup again to switch an existing install). - UD-Q3_K_XL's experts are i-quants despite the name: IQ3_XXS gate/up with IQ4_NL or Q8_0 down. - The GGUF models run upstream's route tail skip by default: ~10% faster decode, and answers a little further from Q8_0 than the table shows. - The two 128 GB models need a 128 GB PC (setup asks below that). Q8_0 runs there in the low-RAM mode by itself (100 GiB of experts locked in RAM); the mix keeps eve

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

具体的な制約を判断する出典が不足しています。実際の性能、遅延、データ方針、ライセンス、提供期間を確認してください。

出典別料金と費用分析

料金はチャネル・地域・設定によって異なります。各チャネルの料金は個別に表示し、提供元の統一公式料金とは見なしません。

公開評価と実行設定

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。

よくある質問

出典と追跡記録

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。