Corvex Launches Secure Model Weights Solution
Written by Emily J. Thompson, Senior Investment Analyst
Updated: Mar 12 2026
0mins
Should l Buy MOVE?
Source: PRnewswire
- Secure Model Weights Launch: Corvex's patent-pending solution enables AI model builders to deploy inference workloads on third-party GPU infrastructure while safeguarding model weights, ensuring the security of enterprise intellectual property and addressing vulnerabilities in traditional cloud security models.
- Encryption Protection Mechanism: By leveraging NVIDIA's Confidential Computing instructions, Corvex ensures that model weights are decrypted only within the secure silicon boundary of the GPU, preventing structural access by the host and enhancing data protection reliability.
- Open Source Transparency: Unlike closed-source commercial alternatives, Corvex's solution is built on the open-source community, utilizing the Confidential Containers project under the Cloud Native Computing Foundation, allowing customers to independently verify security and enhancing market competitiveness.
- Market Demand Alignment: This solution is particularly suited for enterprises handling sensitive data, such as those in healthcare, finance, and defense, allowing them to securely deploy on external infrastructure without the need for on-premises isolation, thus meeting the growing market demand.
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Analyst Views on MOVE
About MOVE
Movano Inc., doing business as Movano Health, is developing a platform to deliver purpose-driven healthcare solutions to bring medical-grade data to the forefront of consumer health devices. Its commercial product Evie Ring is a wearable designed specifically for women. The Evie Ring combines health and wellness metrics to give a full picture of one’s health, which includes resting heart rate, heart rate variability, blood oxygen saturation, respiration rate, skin temperature variability, period and ovulation tracking, menstrual symptom tracking, activity profile, including steps, active minutes and calories burned, sleep stages and duration, and mood tracking. The Company is also developing a proprietary System-on-a-Chip (SoC) designed for blood pressure or continuous glucose monitoring systems. This data is delivered through a mobile app which simplifies how data is presented and turns biometric data into actionable insights that help women make manageable lifestyle changes.
About the author
Emily J. Thompson
Emily J. Thompson, a Chartered Financial Analyst (CFA) with 12 years in investment research, graduated with honors from the Wharton School. Specializing in industrial and technology stocks, she provides in-depth analysis for Intellectia’s earnings and market brief reports.
Medacta Group Reports Strong 2025 Earnings Growth
- Significant Revenue Growth: Medacta Group reported a revenue of EUR 684 million for 2025, reflecting an 18.5% growth in constant currency, particularly strong in Latin America and Asia Pacific with growth rates of 42% and 23%, respectively, indicating robust market performance.
- Margin Improvement: The company's EBITDA margin improved to 29% in constant currency, with a year-over-year increase of 19.1%, showcasing significant progress in cost control and operational efficiency, thereby enhancing overall profitability.
- Substantial Dividend Increase: The Board proposed a nearly 60% increase in the dividend to CHF 1.1 per share, reflecting confidence in future cash flows and aiming to attract more investor interest in the stock.
- Capital Expenditure and Market Challenges: Despite investing EUR 137 million in capital expenditures to support growth in 2025, the company faces challenges in expanding its sales force, with anticipated revenue growth slowing to 10%-14% in 2026, highlighting uncertainties in the market environment.
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Corvex Launches Secure Model Weights Solution
- IP Protection: Corvex's Secure Model Weights solution utilizes hardware-rooted security technologies to ensure that model weights exist in encrypted form within GPU memory, preventing exposure to infrastructure providers during inference, thereby safeguarding enterprises' core intellectual property.
- Addressing Trust Model Issues: This solution tackles the shortcomings of traditional cloud security models by employing NVIDIA's Confidential Computing instructions, ensuring that model weights are decrypted within the GPU's secure silicon boundary, eliminating the risk of data exposure during inference and enhancing corporate confidence in data security.
- Open Source Transparency: Built on an open-source foundation, Corvex's solution utilizes the Confidential Containers project under the Cloud Native Computing Foundation as its orchestration layer, providing verifiable security that allows enterprises to choose infrastructure partners based on security rather than price, enhancing market competitiveness.
- Wide Applicability: This solution is particularly suited for enterprises handling sensitive data, such as those in healthcare, finance, and defense, allowing them to securely deploy AI models on third-party infrastructure, reducing reliance on on-premises isolation and promoting business flexibility and scalability.
See More
Corvex Launches Secure Model Weights Solution for AI
- Secure Model Weights Launch: Corvex announced the early availability of its patent-pending Secure Model Weights solution on March 12, 2026, enabling AI model builders to deploy inference workloads on third-party GPU infrastructure while safeguarding their model weights, significantly enhancing enterprise data security.
- Three-Layer Security Architecture: The solution integrates Trusted Execution Environments, Remote Attestation, and Post-Quantum Key Exchange to ensure model weights exist in encrypted form within GPU memory, preventing access by the host during runtime and thereby reducing the risk of data exfiltration.
- Open Source Transparency: Built on an open-source foundation, Corvex's solution utilizes the Confidential Containers project under the Cloud Native Computing Foundation, providing auditable security that allows customers to choose infrastructure partners based on verifiable security rather than just price and availability.
- Market Demand Alignment: This technology is particularly suited for enterprises handling sensitive data, such as those in healthcare, finance, and defense, allowing them to securely deploy models on external infrastructure, meeting the growing demand for data privacy and security.
See More
Corvex Launches Secure Model Weights, Protecting AI Inference
- Secure Model Weights Launch: Corvex announced the early availability of its patent-pending Secure Model Weights on March 12, 2026, enabling AI model builders to deploy inference workloads on third-party GPU infrastructure without exposing their model weights, thus safeguarding their intellectual property.
- Addressing Trust Model Issues: Traditional cloud security models fail to protect data at runtime; Corvex leverages NVIDIA's Confidential Computing instructions to ensure model weights are decrypted within the GPU's secure silicon boundary, eliminating the need for trust in infrastructure providers.
- Three Layers of Hardware Security: The solution integrates Trusted Execution Environments, Remote Attestation, and Post-Quantum Key Exchange, ensuring model weights exist only in hardware-protected GPU memory during inference, effectively preventing data exfiltration.
- Open Source Foundation and Auditability: Built on an open-source foundation, Corvex's solution utilizes the Confidential Containers project under the Cloud Native Computing Foundation, providing verifiable security that allows enterprises to choose infrastructure partners based on security rather than just price.
See More
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- Secure Model Weights Launch: Corvex's patent-pending solution enables AI model builders to deploy inference workloads on third-party GPU infrastructure while safeguarding model weights, ensuring the security of enterprise intellectual property and addressing vulnerabilities in traditional cloud security models.
- Encryption Protection Mechanism: By leveraging NVIDIA's Confidential Computing instructions, Corvex ensures that model weights are decrypted only within the secure silicon boundary of the GPU, preventing structural access by the host and enhancing data protection reliability.
- Open Source Transparency: Unlike closed-source commercial alternatives, Corvex's solution is built on the open-source community, utilizing the Confidential Containers project under the Cloud Native Computing Foundation, allowing customers to independently verify security and enhancing market competitiveness.
- Market Demand Alignment: This solution is particularly suited for enterprises handling sensitive data, such as those in healthcare, finance, and defense, allowing them to securely deploy on external infrastructure without the need for on-premises isolation, thus meeting the growing market demand.
See More
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- Secure Computing Deployment: Corvex successfully deployed confidential computing on NVIDIA HGX B200 systems, validating encrypted GPU-to-GPU communication to ensure the security of AI workloads and meet customer needs for sensitive data protection.
- Runtime Verification Capability: Utilizing Intel Trust Domain Extensions and NVIDIA Confidential Computing, Corvex provides remote attestation for CPUs and GPUs, ensuring platform integrity at runtime, which enhances appeal to highly regulated industries.
- Performance and Security Balance: The system achieves near-native performance, eliminating the historical trade-off between security and speed, allowing enterprises to securely handle sensitive models and data without compromising performance.
- Multi-Tenant Support: The implementation of confidential computing enables enterprises to securely deploy multi-tenant AI environments, ensuring verifiable security and compliance at runtime, thereby increasing customer trust in AI solutions.
See More
Medacta Group Reports Strong 2025 Earnings Growth
- Significant Revenue Growth: Medacta Group reported a revenue of EUR 684 million for 2025, reflecting an 18.5% growth in constant currency, particularly strong in Latin America and Asia Pacific with growth rates of 42% and 23%, respectively, indicating robust market performance.
- Margin Improvement: The company's EBITDA margin improved to 29% in constant currency, with a year-over-year increase of 19.1%, showcasing significant progress in cost control and operational efficiency, thereby enhancing overall profitability.
- Substantial Dividend Increase: The Board proposed a nearly 60% increase in the dividend to CHF 1.1 per share, reflecting confidence in future cash flows and aiming to attract more investor interest in the stock.
- Capital Expenditure and Market Challenges: Despite investing EUR 137 million in capital expenditures to support growth in 2025, the company faces challenges in expanding its sales force, with anticipated revenue growth slowing to 10%-14% in 2026, highlighting uncertainties in the market environment.
See More
Corvex Launches Secure Model Weights Solution
- IP Protection: Corvex's Secure Model Weights solution utilizes hardware-rooted security technologies to ensure that model weights exist in encrypted form within GPU memory, preventing exposure to infrastructure providers during inference, thereby safeguarding enterprises' core intellectual property.
- Addressing Trust Model Issues: This solution tackles the shortcomings of traditional cloud security models by employing NVIDIA's Confidential Computing instructions, ensuring that model weights are decrypted within the GPU's secure silicon boundary, eliminating the risk of data exposure during inference and enhancing corporate confidence in data security.
- Open Source Transparency: Built on an open-source foundation, Corvex's solution utilizes the Confidential Containers project under the Cloud Native Computing Foundation as its orchestration layer, providing verifiable security that allows enterprises to choose infrastructure partners based on security rather than price, enhancing market competitiveness.
- Wide Applicability: This solution is particularly suited for enterprises handling sensitive data, such as those in healthcare, finance, and defense, allowing them to securely deploy AI models on third-party infrastructure, reducing reliance on on-premises isolation and promoting business flexibility and scalability.
See More
Corvex Launches Secure Model Weights Solution for AI
- Secure Model Weights Launch: Corvex announced the early availability of its patent-pending Secure Model Weights solution on March 12, 2026, enabling AI model builders to deploy inference workloads on third-party GPU infrastructure while safeguarding their model weights, significantly enhancing enterprise data security.
- Three-Layer Security Architecture: The solution integrates Trusted Execution Environments, Remote Attestation, and Post-Quantum Key Exchange to ensure model weights exist in encrypted form within GPU memory, preventing access by the host during runtime and thereby reducing the risk of data exfiltration.
- Open Source Transparency: Built on an open-source foundation, Corvex's solution utilizes the Confidential Containers project under the Cloud Native Computing Foundation, providing auditable security that allows customers to choose infrastructure partners based on verifiable security rather than just price and availability.
- Market Demand Alignment: This technology is particularly suited for enterprises handling sensitive data, such as those in healthcare, finance, and defense, allowing them to securely deploy models on external infrastructure, meeting the growing demand for data privacy and security.
See More
Corvex Launches Secure Model Weights, Protecting AI Inference
- Secure Model Weights Launch: Corvex announced the early availability of its patent-pending Secure Model Weights on March 12, 2026, enabling AI model builders to deploy inference workloads on third-party GPU infrastructure without exposing their model weights, thus safeguarding their intellectual property.
- Addressing Trust Model Issues: Traditional cloud security models fail to protect data at runtime; Corvex leverages NVIDIA's Confidential Computing instructions to ensure model weights are decrypted within the GPU's secure silicon boundary, eliminating the need for trust in infrastructure providers.
- Three Layers of Hardware Security: The solution integrates Trusted Execution Environments, Remote Attestation, and Post-Quantum Key Exchange, ensuring model weights exist only in hardware-protected GPU memory during inference, effectively preventing data exfiltration.
- Open Source Foundation and Auditability: Built on an open-source foundation, Corvex's solution utilizes the Confidential Containers project under the Cloud Native Computing Foundation, providing verifiable security that allows enterprises to choose infrastructure partners based on security rather than just price.
See More
Corvex Launches Secure Model Weights Solution
- Secure Model Weights Launch: Corvex's patent-pending solution enables AI model builders to deploy inference workloads on third-party GPU infrastructure while safeguarding model weights, ensuring the security of enterprise intellectual property and addressing vulnerabilities in traditional cloud security models.
- Encryption Protection Mechanism: By leveraging NVIDIA's Confidential Computing instructions, Corvex ensures that model weights are decrypted only within the secure silicon boundary of the GPU, preventing structural access by the host and enhancing data protection reliability.
- Open Source Transparency: Unlike closed-source commercial alternatives, Corvex's solution is built on the open-source community, utilizing the Confidential Containers project under the Cloud Native Computing Foundation, allowing customers to independently verify security and enhancing market competitiveness.
- Market Demand Alignment: This solution is particularly suited for enterprises handling sensitive data, such as those in healthcare, finance, and defense, allowing them to securely deploy on external infrastructure without the need for on-premises isolation, thus meeting the growing market demand.
See More
Corvex Launches Secure AI Computing Infrastructure
- Secure Computing Deployment: Corvex successfully deployed confidential computing on NVIDIA HGX B200 systems, validating encrypted GPU-to-GPU communication to ensure the security of AI workloads and meet customer needs for sensitive data protection.
- Runtime Verification Capability: Utilizing Intel Trust Domain Extensions and NVIDIA Confidential Computing, Corvex provides remote attestation for CPUs and GPUs, ensuring platform integrity at runtime, which enhances appeal to highly regulated industries.
- Performance and Security Balance: The system achieves near-native performance, eliminating the historical trade-off between security and speed, allowing enterprises to securely handle sensitive models and data without compromising performance.
- Multi-Tenant Support: The implementation of confidential computing enables enterprises to securely deploy multi-tenant AI environments, ensuring verifiable security and compliance at runtime, thereby increasing customer trust in AI solutions.
See More
