Built on openSUSE Linux, this product provides private AI using the Phi-4 model with 14 billion parameters. MultiCortex HPC (High-Performance Computing) allows you to boost your AI's response quality. This is a plug-and-play, low-cost product with no token fees.

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About Phi-4:

Phi-4 is a state-of-the-art language model developed by Microsoft, standing out for its ability to perform complex reasoning, especially in areas like mathematics. With 14 billion parameters, Phi-4 represents a significant advancement for Microsoft, delivering high-quality results in a relatively compact size.
 

Performance and Applications:
Phi-4 outperforms comparable models in tasks related to mathematical reasoning, thanks to advancements in its training process and the use of high-quality curated data. Additionally, its efficiency allows it to be deployed in environments with limited computational resources, enabling local execution with reduced latency and lower operational cost.
 

Key Strengths of Phi-4:

• High performance in mathematical reasoning: Outperforms larger models in math-related tasks due to its optimized training.

• Computational efficiency: Can run on local devices with lower operational costs and low latency.

• Optimized architecture: Decoder-only transformer model with 14 billion parameters and a 16,000 token context window.

• Advanced training: Uses high-quality synthetic data and a rigorous fine-tuning process for better instruction following.

• Enhanced safety and alignment: Incorporates modern techniques to ensure safer and more reliable responses.

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Buy now Phi-4 - 14b Boosted by MultiCortex HPC on AWS. 

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Built on openSUSE Linux, this product provides private AI using the Gemma 3 model with 1 billion parameters. MultiCortex HPC (High-Performance Computing) allows you to boost your AI's response quality. This is a plug-and-play, low-cost product with no token fees.

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About Gemma 3:

Gemma 3 is the latest generation of open language models developed by Google (April/2025). Designed to be efficient and versatile, Gemma 3 offers advanced capabilities that support a wide range of artificial intelligence applications.

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Key Features of Gemma 3:

• Multimodality: Gemma 3 can process text inputs, images, and short videos, enabling the creation of applications that analyze and interpret various types of media.

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• Multilingual Support: With support for over 140 languages, Gemma 3 facilitates global communication and adaptation of applications.

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• Extended Context Windows: The model supports context windows of up to 128,000 tokens, allowing for the processing of more complex information and better handling of tasks that require deep contextual understanding.

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• Computational Efficiency: Gemma 3 is designed to operate efficiently on devices with limited resources, and is considered the most powerful AI model capable of running on a single GPU.

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• Advanced Reasoning Capabilities: The model delivers advanced capabilities in mathematics, reasoning, and chat, including structured outputs and function calls— expanding the possibilities for interaction and automation.​​

 

Buy now Gemma 3 - 1b Boosted by MultiCortex HPC on AWS. ​​​​​​​

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Built on openSUSE Linux, this product provides private AI using the Gemma 3 model with 12 billion parameters. MultiCortex HPC (High-Performance Computing) allows you to boost your AI's response quality. This is a plug-and-play, low-cost product with no token fees.

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About Gemma 3:

Gemma 3 is the latest generation of open language models developed by Google (April/2025). Designed to be efficient and versatile, Gemma 3 offers advanced capabilities that support a wide range of artificial intelligence applications.

​

Key Features of Gemma 3:

• Multimodality: Gemma 3 can process text inputs, images, and short videos, enabling the creation of applications that analyze and interpret various types of media.

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• Multilingual Support: With support for over 140 languages, Gemma 3 facilitates global communication and adaptation of applications.

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• Extended Context Windows: The model supports context windows of up to 128,000 tokens, allowing for the processing of more complex information and better handling of tasks that require deep contextual understanding.

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• Computational Efficiency: Gemma 3 is designed to operate efficiently on devices with limited resources, and is considered the most powerful AI model capable of running on a single GPU.

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• Advanced Reasoning Capabilities: The model delivers advanced capabilities in mathematics, reasoning, and chat, including structured outputs and function calls— expanding the possibilities for interaction and automation.​​

 

Buy now Gemma 3 - 12b Boosted by MultiCortex HPC on AWS. ​​​​​​​

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Built on openSUSE Linux, this product provides private AI using the LLaMa 3.2 model with 1 billion parameters. MultiCortex HPC (High-Performance Computing) allows you to boost your AI's response quality. This is a plug-and-play, low-cost product with no token fees.

 

About LLaMa 3.2:

LLaMa Three 3.2 is a large language model developed by Meta AI and released in December twenty twenty four. This model represents a significant advancement in terms of efficiency and performance in the field of artificial intelligence.
 

Performance and Efficiency:
Llama 3.2 delivers strong performance with significantly lower computational requirements compared to previous models. This enables developers and companies to deploy advanced AI solutions without the need for expensive and specialized infrastructure.
 

Multilingual Capabilities:
Designed to serve a global audience, LLaMa 3.2 supports multiple languages and outperforms many existing conversational models on standard industry benchmarks. This makes it ideal for applications that require understanding and generating text in various languages.
 

Highlights:

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• Computational efficiency. Designed to run on more accessible infrastructure, reducing operational costs.

 

• Practical applications. Used by companies for automation, customer support, and other AI tasks.

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• Privacy and control. Companies can implement the model without compromising sensitive customer data.

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Buy now LLaMa 3.2 - 1b Boosted by MultiCortex HPC on AWS. 

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Built on openSUSE Linux, this product provides private AI using the LLaMa 3.2 model with 3 billion parameters. MultiCortex HPC (High-Performance Computing) allows you to boost your AI's response quality. This is a plug-and-play, low-cost product with no token fees.

 

About LLaMa 3.2:

LLaMa Three 3.2 is a large language model developed by Meta AI and released in December twenty twenty four. This model represents a significant advancement in terms of efficiency and performance in the field of artificial intelligence.
 

Performance and Efficiency:
Llama 3.2 delivers strong performance with significantly lower computational requirements compared to previous models. This enables developers and companies to deploy advanced AI solutions without the need for expensive and specialized infrastructure.
 

Multilingual Capabilities:
Designed to serve a global audience, LLaMa 3.2 supports multiple languages and outperforms many existing conversational models on standard industry benchmarks. This makes it ideal for applications that require understanding and generating text in various languages.
 

Highlights:

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• Computational efficiency. Designed to run on more accessible infrastructure, reducing operational costs.

 

• Practical applications. Used by companies for automation, customer support, and other AI tasks.

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• Privacy and control. Companies can implement the model without compromising sensitive customer data.

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Buy now LLaMa 3.2 - 3b Boosted by MultiCortex HPC on AWS. 

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Built on openSUSE Linux, this product provides private AI using the Deepseek R-1 model with 1.5 billion parameters. MultiCortex HPC (High-Performance Computing) allows you to boost your AI's response quality. This is a plug-and-play, low-cost product with no token fees.

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About Deepseek R1:
DeepSeek R1 is an advanced open source language model initiative focused on delivering high performance, efficiency, and versatility. The lineup includes models of different sizes and architectures, with a strong emphasis on its modern Mixture of Experts approach, supporting a large input context length.

Why is this architecture eficiente:

The Mixture of Experts approach is powerful because it allows only a fraction of the model to be used at a time. This results in:

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• Reduced inference time. Even with billions of parameters, partial activation keeps execution fast.

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• Computational efficiency. Selective use of experts reduces memory consumption and processing power.

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• Improved specialization. Each expert can focus on specific task types, enhancing overall accuracy.

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Ideal use cases for DeepSeek R1:

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• Interpreting large, structured reports or datasets

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• Literature reviews and meta-analyses

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• Advanced chatbots and conversational agents that handle long dialogues and rich contexts.

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• Code generation and understanding, thanks to its ability to process large blocks of text.

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Buy now Deepseek R-1 - 1.5b Boosted by MultiCortex HPC on AWS. ​​​

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Built on openSUSE Linux, this product provides private AI using the Deepseek R-1 model with 14 billion parameters. MultiCortex HPC (High-Performance Computing) allows you to boost your AI's response quality. This is a plug-and-play, low-cost product with no token fees.

​​​​​​

About Deepseek R1:
DeepSeek R1 is an advanced open source language model initiative focused on delivering high performance, efficiency, and versatility. The lineup includes models of different sizes and architectures, with a strong emphasis on its modern Mixture of Experts approach, supporting a large input context length.

Why is this architecture eficiente:

The Mixture of Experts approach is powerful because it allows only a fraction of the model to be used at a time. This results in:

​

• Reduced inference time. Even with billions of parameters, partial activation keeps execution fast.

​

• Computational efficiency. Selective use of experts reduces memory consumption and processing power.

​

• Improved specialization. Each expert can focus on specific task types, enhancing overall accuracy.

​

Ideal use cases for DeepSeek R1:

​

• Interpreting large, structured reports or datasets

​

• Literature reviews and meta-analyses

​​

• Advanced chatbots and conversational agents that handle long dialogues and rich contexts.

​​

• Code generation and understanding, thanks to its ability to process large blocks of text.

​

Buy now Deepseek R-1 - 14b Boosted by MultiCortex HPC on AWS. ​​​

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Built on openSUSE Linux, this product provides private AI using the Qwen 2.5 model with 0.5 billion parameters. MultiCortex HPC (High-Performance Computing) allows you to boost your AI's response quality. This is a plug-and-play, low-cost product with no token fees.

 

About Qwen:

Qwen is the large language model and large multimodal model series of the Qwen Team, Alibaba Group. Now the large language models have been upgraded to Qwen2.5. Both language models and multimodal models are pretrained on large-scale multilingual and multimodal data and post-trained on quality data for aligning to human preferences. Qwen is capable of natural language understanding, text generation, vision understanding, audio understanding, tool use, role play, playing as AI agent, etc.

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Qwen 2.5 has the following features:

• Pretrained on our latest large-scale dataset, encompassing up to 18T tokens.

 

• Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON.

 

• More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.

 

• Context length support up to 128K tokens and can generate up to 8K tokens.

 

• Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

 

Buy now Qwen 2.5 - 0.5b Boosted by MultiCortex HPC on AWS. 

 

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Built on openSUSE Linux, this product provides private AI using the Qwen 2.5  model with 14 billion parameters. MultiCortex HPC (High-Performance Computing) allows you to boost your AI's response quality. This is a plug-and-play, low-cost product with no token fees.

 

About Qwen:

Qwen is the large language model and large multimodal model series of the Qwen Team, Alibaba Group. Now the large language models have been upgraded to Qwen2.5. Both language models and multimodal models are pretrained on large-scale multilingual and multimodal data and post-trained on quality data for aligning to human preferences. Qwen is capable of natural language understanding, text generation, vision understanding, audio understanding, tool use, role play, playing as AI agent, etc.

​

Qwen 2.5 has the following features:

• Pretrained on our latest large-scale dataset, encompassing up to 18T tokens.

 

• Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON.

 

• More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.

 

• Context length support up to 128K tokens and can generate up to 8K tokens.

 

• Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

 

Buy now Qwen 2.5 - 14b Boosted by MultiCortex HPC on AWS. 

 

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Built on openSUSE Linux, this product provides private AI using the Mistral Small model with 24 billion parameters. MultiCortex HPC (High-Performance Computing) allows you to boost your AI's response quality. This is a plug-and-play, low-cost product with no token fees.

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About Mistral 0.3:

Mistral 0.3 is an open source language model developed by the company Mistral AI. It is designed to deliver high performance, versatility, and practical use across a wide range of natural language tasks.
 

Main Features:

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• Extended context of up to thirty two thousand tokens allowing it to handle long and complex inputs.

• Built using a decoder only architecture similar to GPT style models.

• High performance on tasks such as text understanding problem solving question answering and code interpretation.

• Trained with modern alignment techniques ensuring greater safety clarity and adherence to instructions.

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Highlights of Mistral 0.3:

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• Competitive performance surpassing many open source models of similar size in standard benchmarks.

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• Ready for instruction and chat use suitable for conversation tasks virtual assistants technical support and interactive applications.

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• Ideal for local or enterprise environments requiring less demanding infrastructure.

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• Easy integration into custom AI pipelines APIs and commercial applications.

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Buy now Mistral Small - 24b Boosted by MultiCortex HPC on AWS. ​​​

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