The RadixArk Qwen3.8-27B-NVFP4 model is the quantized version of Qwen/Qwen3.8-27B. The quantization was produced at RadixArk using NVIDIA Model Optimizer, following a mixed NVFP4 W4A4 recipe. Run on SGLang: launch command and per-platform recipes in the Qwen3.8-27B cookbook. This model is not owned or developed by RadixArk. It is a quantized derivative of Qwen's model; see the upstream Qwen3.8-27B model card for the source model's capabilities, training information, limitations, and license. Global Developers looking to deploy an off-the-shelf, pre-quantized model in AI agent systems, chatbots, RAG systems, and other AI-powered applications. Hugging Face 08/14/2026 via…
Open weights
apache-2.0
18.2B parameters
262,144 tokens
Model Optimizer
NVFP4 checkpoint of an abliterated Swift-Qwen3.8-27B (UkisAI's reasoning-efficient fine-tune of Qwen3.8-27B). For vLLM and SGLang. GGUFs for llama.cpp: The Swift 1.5 version is source). - Swift's own NVFP4 recipe, unmodified, from ukisai/Swift-Qwen3.8-27B-NVFP4, calibrated with NVIDIA ModelOpt. - MTP head and vision tower in BF16, bit-identical to the source. 21.9 GB, NVIDIA ModelOpt mixed-precision format. Needs a vLLM with ModelOpt mixed-precision support (tested on 0.29.0). No --quantization flag. Sampling, as for Swift and Qwen: temperature 1.0, topp 0.95, topk 20, minp 0. The model thinks before answering by default. Tested on an RTX 5090 (32 GB) with vLLM 0.29.0: NVFP4 layers on…
Open weights
other
18.2B parameters
262,144 tokens
vllm
NVFP4 checkpoint of an abliterated Swift 1.5 Qwen3.8-27B (UkisAI's reasoning-efficient fine-tune of Qwen3.8-27B). For vLLM and SGLang. GGUFs for llama.cpp: (measured on the BF16 source). - Swift's own NVFP4 recipe, unmodified, from ukisai/Swift-Qwen3.8-27B-NVFP4, calibrated with NVIDIA ModelOpt. The module split matches UkisAI's Swift 1.5 NVFP4 exactly. - MTP head and vision tower in BF16, bit-identical to the source. 21.9 GB, NVIDIA ModelOpt mixed-precision format. Needs a vLLM with ModelOpt mixed-precision support. No --quantization flag. Sampling, as for Swift and Qwen: temperature 1.0, topp 0.95, topk 20, minp 0. The model thinks before answering by default. Same format, recipe, module…
Open weights
other
18.2B parameters
262,144 tokens
vllm
Quantized version of https://huggingface.co/Qwen/Qwen3.8-27B
Open weights
apache-2.0
17.6B parameters
262,144 tokens
transformers
WORK IN PROGRESS A mixed NVFP4/MXFP8 quantization-aware distillation of Qwen3.8-27B, trained for one epoch. The student learns from the original BF16 teacher while its MLP weights are quantized in the forward pass. Distillation updates the MLP weights and text normalization weights to account for quantization error. This is a trained distillation checkpoint, not a post-training conversion of the original weights. Attention/GDN projections and the LM head were frozen in their MXFP8 representations during distillation. Packed NVFP4 and MXFP8 weights reconstruct to the same BF16 weight values used by the student during training. The tokenizer, chat template, generation configuration and…
Open weights
apache-2.0
19.2B parameters
262,144 tokens
transformers
GLM-5.3-Flash with its routed experts quantized to NestQuant, a nested two-level format. Each expert has a 1.5-bit base plus a residual plane that lifts it to 4 bits. At serving time an expert moves from 1.5 bit to 4 bit by loading its residual plane on top of the base, and the base bytes stay the same. This build targets a 128 GB Mac with 96 GB usable for weights, KV cache and runtime. The native vision tower is included, so the model still takes images. Status: TODO. Encoding in progress. This repo cannot be loaded with stock transformers, vLLM or MLX. The serving kernel and loader will be published separately. - 67 of 288 experts per layer (23%) run at 4 bit. The rest run at 1.5 bit.…
Open weights
mit
16.9B parameters
1,048,576 tokens
