Qwen2-0.5B-Instruct

Qwen2-0.5B is an ultra-compact language model with 0.5 billion parameters, specifically designed for deployment on mobile and IoT devices. The model utilizes GQA (Grouped Query Attention) and tied embeddings to optimize performance, an architectural feature that significantly reduces energy consumption and memory usage during inference.  

Trained on a high-quality multilingual dataset of 12 trillion tokens, the model is capable of handling around 30 languages, including Russian and several relatively rare languages. Despite its compact size, it demonstrates strong performance in basic language tasks. However, the key advantage of Qwen2-0.5B is its ability to be efficiently deployed on smartphones, headphones, smart glasses, and other embedded systems.  

Its low memory and computational requirements make it ideal for edge computing applications. Qwen2-0.5B is particularly well-suited for developing personal assistants on mobile devices, simple chatbots, real-time text processing on IoT devices, and as a base model for specialized fine-tuning in resource-constrained environments.


Announce Date: 24.07.2024
Parameters: 500M
Context: 33K
Layers: 24
Attention Type: Full Attention
Developer: Qwen
Transformers Version: 4.40.1
License: Apache 2.0

Public endpoint

Use our pre-built public endpoints for free to test inference and explore Qwen2-0.5B-Instruct capabilities. You can obtain an API access token on the token management page after registration and verification.
Model Name Context Type GPU Status Link

API access to Qwen2-0.5B-Instruct endpoints

There are no public endpoints for this model yet.

Private endpoint

An endpoint is a server with pre-installed software that automatically downloads neural network weights and launches an OpenAI-compatible API to handle incoming requests.

A private endpoint is created within the user's project, belongs exclusively to the user, and is billed per server configuration in accordance with the infrastructure pricing.

We recommend creating private endpoints when you need to:

  • achieve maximum endpoint performance,
  • access the full context window for processing large requests and conversations,
  • ensure maximum security by processing data in a dedicated, isolated environment.

Recommended server configurations for creating a private inference endpoint Qwen2-0.5B-Instruct

Prices:
Name GPU Price, hour TPS Max Concurrency
teslaa2-1.16.32.160
32,768.0
1 $0.38 118.02 31.392 Launch
teslaa10-1.16.32.160
32,768.0
1 $0.53 227.66 50.773 Launch
rtx3080-1.16.32.160
32,768.0
1 $0.57 263.11 19.150 Launch
rtx3090-1.16.24.160
32,768.0
1 $0.83 282.86 53.374 Launch
rtx4090-1.16.32.160
32,768.0
1 $1.02 310.91 53.276 Launch
rtx3090-2.16.64.160.nvlink
32,768.0
tensor
2 $1.56 109.228 Launch
rtx5090-1.16.64.160
32,768.0
1 $1.59 72.485 Launch
teslaa100-1.16.64.160
32,768.0
1 $2.37 265.32 190.000 Launch
h100-1.16.64.160
32,768.0
1 $3.83 213.89 189.828 Launch
h100nvl-1.16.96.160
32,768.0
1 $4.11 223.954 Launch
teslaa100-2.24.96.160.nvlink
32,768.0
tensor
2 $4.61 382.480 Launch
h200-1.16.128.160
32,768.0
1 $4.74 338.549 Launch
h200-2.24.256.160.nvlink
32,768.0
tensor
2 $9.40 679.579 Launch

Related models

Need help?

Contact our dedicated neural networks support team at support@immers.cloud or send your request to the sales department at sale@immers.cloud.

We use cookies and web analytics services to ensure the proper functioning of the website, analyze web-site traffic, and improve the quality of our services.
By continuing to use the website, you consent to the Privacy Policy and consent to the processing of cookie files and technical data.