Qwen3 models available now on Gcore Everywhere Inference
- May 15, 2025
- 2 min read

We’ve expanded our model library for Gcore Everywhere Inference with three powerful additions from the Qwen3 series. These new models bring advanced reasoning, faster response times, and even better multilingual support, helping you power everything from chatbots and coding tools to complex R&D workloads.
With Gcore Everywhere Inference, you can deploy Qwen3 models in just three clicks. Read on to discover what makes Qwen3 special, which Qwen3 model best suits your needs, and how to deploy it with Gcore today.
Introducing the new Qwen3 models
Qwen3 is the latest evolution of the Qwen series, featuring both dense and Mixture-of-Experts (MoE) architectures. It introduces dual-mode reasoning, letting you toggle between “thinking” and “non-thinking” modes to balance depth and speed:
- Thinking mode (
enable_thinking=True): The model adds a<think>…</think>block to reason step-by-step before generating the final response. Ideal for tasks like code generation or math where accuracy and logic matter. - Non-thinking mode (
enable_thinking=False): Skips the reasoning phase to respond faster. Best for straightforward tasks where speed is a priority.
Model sizes and use cases
With three new sizes available, you can choose the level of performance required for your use case:
- Qwen3-14B: A 14B parameter model tuned for responsive, multilingual chat and instruction-following. Fast, versatile, and ready for real-time applications with lightning-fast responses.
- Qwen3-30B-A3B: Built on the Arch-3 backbone, this 30B model offers advanced reasoning and coding capabilities. It’s ideal for applications that demand deeper understanding and precision while balancing performance. It provides high-quality output with faster inference and better efficiency.
- Qwen3-32B: The largest Qwen3 model yet, designed for complex, high-performance tasks across reasoning, generation, and multilingual domains. It sets a new standard for what’s achievable with Gcore Everywhere Inference, delivering exceptional results with maximum reasoning power. Ideal for complex computation and generation tasks where every detail matters.
| Model | Architecture | Total parameters | Active parameters | Context length | Best suited for |
| Qwen3-14B | Dense | 14B | 14B | 128K | Multilingual chatbots, instruction-following tasks, and applications requiring strong reasoning capabilities with moderate resource consumption. |
| Qwen3-30B-A3B | MoE | 30B | 3B | 128K | Scenarios requiring advanced reasoning and coding capabilities with efficient resource usage; suitable for real-time applications due to faster inference times. |
| Qwen3-32B | Dense | 32B | 32B | 128K | High-performance tasks demanding maximum reasoning power and accuracy; ideal for complex R&D workloads and precision-critical applications. |
How to deploy Qwen3 models with Gcore in just a few clicks
Getting started with Qwen3 on Gcore Everywhere Inference is fast and frictionless. Simply log in to the Gcore Portal, navigate to the AI Inference section, and select your desired Qwen3 model. From there, deployment takes just three clicks—no setup scripts, no GPU wrangling, no DevOps overhead. Check out our docs to discover how it works.

Prefer to deploy programmatically? Use the Gcore API with your project credentials. We offer quick-start examples in Python and cURL to get you up and running fast.
Why choose Qwen3 + Gcore?
- Flexible performance: Choose from three models tailored to different workloads and cost-performance needs.
- Immediate availability: All models are live now and deployable via portal or API.
- Next-gen architecture: Dense and MoE options give you more control over reasoning, speed, and output quality.
- Scalable by design: Built for production-grade performance across industries and use cases.
With the latest Qwen3 additions, Gcore Everywhere Inference continues to deliver on performance, scalability, and choice. Ready to get started? Get a free account today to explore Qwen3 and deploy with Gcore in just a few clicks.
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