What are the hardware requirements for ThinkingCap Qwen3.8 27B NVFP4A4 AWQ?

ThinkingCap Qwen3.8 27B NVFP4A4 AWQ requires approximately 22.5 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed ThinkingCap Qwen3.8 27B NVFP4A4 AWQ and how large is it?

ThinkingCap Qwen3.8 27B NVFP4A4 AWQ is developed by bottlecapai, a model with 27b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate ThinkingCap Qwen3.8 27B NVFP4A4 AWQ?

ThinkingCap Qwen3.8 27B NVFP4A4 AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

ThinkingCap Qwen3.8 27B NVFP4A4 AWQ by bottlecapai — VRAM 22.5GB

Name: ThinkingCap Qwen3.8 27B NVFP4A4 AWQ
Author: bottlecapai

ThinkingCap Qwen3.8 27B NVFP4A4 AWQ is an open-source language model by bottlecapai. Features: 27b LLM, VRAM: 22.5GB, License: other, Quantized, LLM Explorer Score: 0.36.

Awq Base model:bottlecapai/thinkin... Base model:quantized:bottlecap... Compressed-tensors Conversational Endpoints compatible Fp4 Image-text-to-text Nvfp4 Quantized Qwen3 5 Region:us Safetensors Sharded Tensorflow Vllm W4a4

Model Card on HF 🤗: https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4A4-AWQ

ThinkingCap Qwen3.8 27B NVFP4A4 AWQ Parameters and Internals

LLM Name	ThinkingCap Qwen3.8 27B NVFP4A4 AWQ
Repository 🤗	https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4A4-AWQ
Base Model(s)	bottlecapai/ThinkingCap-Qwen3.8-27B bottlecapai/ThinkingCap-Qwen3.8-27B
Model Size	27b
Required VRAM	22.5 GB
Updated	2026-09-26
Maintainer	bottlecapai
Model Type	qwen3_5
Model Files	10.0 GB: 1-of-3 9.9 GB: 2-of-3 2.6 GB: 3-of-3 0.8 GB
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	Qwen3_5ForConditionalGeneration
License	other
Model Max Length	262144
Transformers Version	5.10.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the ThinkingCap Qwen3.8 27B NVFP4A4 AWQ capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to ThinkingCap Qwen3.8 27B NVFP4A4 AWQ

Best Alternatives	Context / RAM	Downloads	Likes
Qwen3.6 27B AWQ INT4	0K / 20.4 GB	2563390	103
Qwen3.8 27B AWQ INT4	0K / 21 GB	21015	32
Qwen3.6 27B AWQ	0K / 22 GB	668924	24
Qwen3.6 27B AWQ BF16 INT4	0K / 28.3 GB	419171	41
...wen3.6 27B Abliterated AWQ MTP	0K / 18.4 GB	244978	12
Qwen3.5 27B AWQ	0K / 21.9 GB	370920	44
Qwen3.8 27B AWQ 4bit	0K / 27.7 GB	11874	6
Qwen3.5 27B AWQ 4bit	0K / 20 GB	279064	42
Qwen3.8 27B AWQ W4A16 ASYM	0K / 0.8 GB	10155	3
...wen3.8 27B Abliterated AWQ MTP	0K / 19.1 GB	4494	8

Note: green Score (e.g. "73.2") means that the model is better than bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4A4-AWQ.