What are the hardware requirements for Qwen3 8B Ep4 Julia Codeforces Extended With Thinksft 16bit Vllm?

Qwen3 8B Ep4 Julia Codeforces Extended With Thinksft 16bit Vllm requires approximately 16.4 GB of VRAM and supports a context window of 40K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Where can I download or evaluate Qwen3 8B Ep4 Julia Codeforces Extended With Thinksft 16bit Vllm?

Qwen3 8B Ep4 Julia Codeforces Extended With Thinksft 16bit Vllm is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3 8B Ep4 Julia Codeforces Extended With Thinksft 16bit Vllm by didula-wso2 — VRAM 16.4GB, 40K context

Name: Qwen3 8B Ep4 Julia Codeforces Extended With Thinksft 16bit Vllm
Author: didula-wso2

Qwen3 8B Ep4 Julia Codeforces Extended With Thinksft 16bit Vllm is an open-source language model by didula-wso2. Features: 8b LLM, VRAM: 16.4GB, Context: 40K, License: apache-2.0, Quantized, LLM Explorer Score: 0.23.

4bit Conversational En Endpoints compatible Quantized Qwen3 Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/didula-wso2/Qwen3-8B-ep4_julia_codeforces_extended_with_thinksft_16bit_vllm

Qwen3 8B Ep4 Julia Codeforces Extended With Thinksft 16bit Vllm Parameters and Internals

LLM Name	Qwen3 8B Ep4 Julia Codeforces Extended With Thinksft 16bit Vllm
Repository 🤗	https://huggingface.co/didula-wso2/Qwen3-8B-ep4_julia_codeforces_extended_with_thinksft_16bit_vllm
Base Model(s)	unsloth/qwen3-8b-unsloth-bnb-4bit unsloth/qwen3-8b-unsloth-bnb-4bit
Model Size	8b
Required VRAM	16.4 GB
Updated	2026-07-28
Maintainer	didula-wso2
Model Type	qwen3
Model Files	4.9 GB: 1-of-4 4.9 GB: 2-of-4 5.0 GB: 3-of-4 1.6 GB: 4-of-4
Supported Languages	en
Quantization Type	4bit
Model Architecture	Qwen3ForCausalLM
License	apache-2.0
Context Length	40960
Model Max Length	40960
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|PAD_TOKEN\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the Qwen3 8B Ep4 Julia Codeforces Extended With Thinksft 16bit Vllm capabilities

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Instruction Following and Task Automation
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Quantized Models of the Qwen3 8B Ep4 Julia Codeforces Extended With Thinksft 16bit Vllm

Model	Likes	Downloads	VRAM
...90 With Think Knowledge Merged	0	14	16 GB

Best Alternatives to Qwen3 8B Ep4 Julia Codeforces Extended With Thinksft 16bit Vllm

Best Alternatives	Context / RAM	Downloads	Likes
BehChat SFTv3 Ckpt1	128K / 16.4 GB	6	0
BehChat SFTv2 Mixed Ckpt 6	128K / 16.4 GB	11	0
...0528 Qwen3 8B Unsloth Bnb 4bit	128K / 7.5 GB	4457	13
DeepSeek R1 0528 Qwen3 8B 4bit	128K / 4.6 GB	1781	6
...Seek R1 0528 Qwen3 8B Bnb 4bit	128K / 6.1 GB	923	9
Qwen3 8B LQA 14e Full	128K / 16.4 GB	6	0
...Seek R1 0528 Qwen3 8B 4bit DWQ	128K / 4.6 GB	212	8
...0528 Qwen3 8B Unsloth Bnb 4bit	128K / 16.4 GB	7	0
Annie Lite V0.2.9 Qwen3 8B	80K / 16.4 GB	5	0
Bonsai 8B Mlx 1bit	64K / 1.3 GB	9396	221

Note: green Score (e.g. "73.2") means that the model is better than didula-wso2/Qwen3-8B-ep4_julia_codeforces_extended_with_thinksft_16bit_vllm.