What are the hardware requirements for Deepmoney 34B 200K Base AWQ?

Deepmoney 34B 200K Base AWQ requires approximately 19.3 GB of VRAM and supports a context window of 195K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Deepmoney 34B 200K Base AWQ and how large is it?

Deepmoney 34B 200K Base AWQ is developed by TheBloke, a model with 34b 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 Deepmoney 34B 200K Base AWQ?

Deepmoney 34B 200K Base AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Deepmoney 34B 200K Base AWQ by TheBloke — VRAM 19.3GB, 195K context

Name: Deepmoney 34B 200K Base AWQ
Author: TheBloke

Deepmoney 34B 200K Base AWQ is an open-source language model by TheBloke. Features: 34b LLM, VRAM: 19.3GB, Context: 195K, License: apache-2.0, Quantized, LLM Explorer Score: 0.1.

4-bit Awq Base model:quantized:triadpart... Base model:triadparty/deepmone... En Finance Invest Llama Quantized Region:us Safetensors Sharded Tensorflow Zh

Model Card on HF 🤗: https://huggingface.co/TheBloke/deepmoney-34b-200k-base-AWQ

Deepmoney 34B 200K Base AWQ Parameters and Internals

Model Type

Supported Languages

en (expert), zh (expert)

Training Details

Data Sources:

college textbooks, professional books, research report data from 2019 to December 2023

Context Length:

200000

Model Architecture:

long context yi-34b-200k

Input Output

Input Format:

{prompt}

Accepted Modalities:

text

LLM Name	Deepmoney 34B 200K Base AWQ
Repository 🤗	https://huggingface.co/TheBloke/deepmoney-34b-200k-base-AWQ
Model Name	Deepmoney 34B 200K Base
Model Creator	triad party
Base Model(s)	Deepmoney 34B 200K Base TriadParty/deepmoney-34b-200k-base
Model Size	34b
Required VRAM	19.3 GB
Updated	2026-07-13
Maintainer	TheBloke
Model Type	llama
Model Files	10.0 GB: 1-of-2 9.3 GB: 2-of-2
Supported Languages	en zh
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	200000
Model Max Length	200000
Transformers Version	4.37.0.dev0
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	64000
Torch Data Type	float16

Rank the Deepmoney 34B 200K Base 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 Deepmoney 34B 200K Base AWQ

Best Alternatives	Context / RAM	Downloads	Likes
Opus V1 34B AWQ	195K / 19.2 GB	3	1
Smaug 34B V0.1 AWQ	195K / 19.2 GB	3	2
Yi 34B 200K RPMerge AWQ	195K / 19.2 GB	5	1
Tess 34B V1.5B AWQ	195K / 19.3 GB	7	3
...34B 200K DARE Megamerge V8 AWQ	195K / 19.3 GB	10	2
...ey 34B 200K Chat Evaluator AWQ	195K / 19.3 GB	5	5
Nous Capybara Limarpv3 34B AWQ	195K / 19.3 GB	0	1
Bagel DPO 34B V0.2 AWQ	195K / 19.3 GB	10	8
Nontoxic Bagel 34B V0.2 AWQ	195K / 19.3 GB	11	2
Bagel 34B V0.2 AWQ	195K / 19.3 GB	10	2

Note: green Score (e.g. "73.2") means that the model is better than TheBloke/deepmoney-34b-200k-base-AWQ.