What are the hardware requirements for Tess V2.5.2 Qwen2 72B 4 0bpw H6 EXL2 Pippa?

Tess V2.5.2 Qwen2 72B 4 0bpw H6 EXL2 Pippa requires approximately 38.6 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Tess V2.5.2 Qwen2 72B 4 0bpw H6 EXL2 Pippa and how large is it?

Tess V2.5.2 Qwen2 72B 4 0bpw H6 EXL2 Pippa is developed by denru, a model with 72b 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 Tess V2.5.2 Qwen2 72B 4 0bpw H6 EXL2 Pippa?

Tess V2.5.2 Qwen2 72B 4 0bpw H6 EXL2 Pippa is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Tess V2.5.2 Qwen2 72B 4 0bpw H6 EXL2 Pippa by denru — VRAM 38.6GB, 128K context

Name: Tess V2.5.2 Qwen2 72B 4 0bpw H6 EXL2 Pippa
Author: denru

Tess V2.5.2 Qwen2 72B 4 0bpw H6 EXL2 Pippa is an open-source language model by denru. Features: 72b LLM, VRAM: 38.6GB, Context: 128K, Quantized, LLM Explorer Score: 0.13.

4-bit Conversational Endpoints compatible Exl2 Quantized Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/denru/Tess-v2.5.2-Qwen2-72B-4_0bpw-h6-exl2-pippa

Tess V2.5.2 Qwen2 72B 4 0bpw H6 EXL2 Pippa Parameters and Internals

LLM Name	Tess V2.5.2 Qwen2 72B 4 0bpw H6 EXL2 Pippa
Repository 🤗	https://huggingface.co/denru/Tess-v2.5.2-Qwen2-72B-4_0bpw-h6-exl2-pippa
Model Size	72b
Required VRAM	38.6 GB
Updated	2026-08-07
Maintainer	denru
Model Type	qwen2
Model Files	8.5 GB: 1-of-5 8.5 GB: 2-of-5 8.6 GB: 3-of-5 8.5 GB: 4-of-5 4.5 GB: 5-of-5
Quantization Type	exl2
Model Architecture	Qwen2ForCausalLM
Context Length	131072
Model Max Length	131072
Transformers Version	4.41.2
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|end_of_text\|>
Vocabulary Size	152064
Torch Data Type	bfloat16
Errors	replace

Rank the Tess V2.5.2 Qwen2 72B 4 0bpw H6 EXL2 Pippa 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 Tess V2.5.2 Qwen2 72B 4 0bpw H6 EXL2 Pippa

Best Alternatives	Context / RAM	Downloads	Likes
Kimi Dev 72B 4bit DWQ	128K / 40.9 GB	4465	21
Kimi Dev 72B 8bit	128K / 77.1 GB	361	2
Kimi Dev 72B 5bit	128K / 50.1 GB	368	2
Qwen2.5 72B Bnb 4bit	128K / 41.4 GB	153	1
...in 2.9.2 Qwen2 72B 6.0bpw EXL2	128K / 56.1 GB	5	1
Qwen2 72B Bnb 4bit	128K / 41.2 GB	99	4
...phin 292 Qwen2 72b EXL2 4 0bpw	128K / 38.6 GB	4	1
Qwen2 72B 4bit	128K / 40.9 GB	51	0
...n 2.9.2 Qwen72b 8.0bpw H8 EXL2	128K / 66.8 GB	3	2
...B Instruct 2.0bpw H Novel EXL2	125K / 23 GB	4	1

Note: green Score (e.g. "73.2") means that the model is better than denru/Tess-v2.5.2-Qwen2-72B-4_0bpw-h6-exl2-pippa.