AI deployment in private environments: Together AI has unveiled its Enterprise Platform, enabling organizations to deploy AI models in virtual private cloud and on-premises environments, addressing key concerns of performance, cost-efficiency, and data privacy.
- The platform extends AI deployment capabilities to customer-controlled cloud and on-premises environments, building upon Together AI’s existing full-stack platform for open-source LLMs.
- This new offering aims to meet the needs of businesses that have established privacy and compliance policies within their own cloud setups.
- Vipul Prakash, CEO of Together AI, emphasizes the importance of efficiency, cost, and data privacy as companies scale up their AI workloads.
Performance optimization and cost reduction: The Together Enterprise Platform promises significant improvements in AI inference workloads, potentially reducing hardware requirements and associated costs.
- The platform claims to enhance inference performance by two to three times while reducing hardware usage by 50%.
- These efficiency gains are achieved through optimized software and hardware utilization, including advanced scheduling and organization of GPU computations.
- Speculative decoding techniques are employed, using smaller models to predict larger model outputs, thereby reducing the computational burden on more intensive models.
Flexible model orchestration: The platform offers sophisticated capabilities for managing multiple AI models within a single application or workflow, catering to the diverse needs of enterprise AI deployments.
- Enterprises typically use a combination of open-source, custom, and third-party models in their AI applications.
- The Together platform allows for dynamic scaling of different models based on demand for specific features at any given time.
- This orchestration capability enables organizations to efficiently manage and utilize their AI resources across various use cases.
Mixture of Agents approach: Together AI introduces a novel method for combining multiple models to produce optimal outcomes, distinguishing itself from other approaches in the field.
- The Mixture of Agents technique uses “weaker” models as “proposers” to generate initial responses to prompts.
- An “aggregator” model then combines these responses to produce a more refined and accurate final answer.
- This approach differs from other methods like LangChain, model routing, or the Composition of Experts model used by competitors.
Future developments: Together AI hints at upcoming advancements in agentic AI workflows, signaling continued innovation in the field of enterprise AI platforms.
- The company’s focus on computational and inference platforms positions it to explore new frontiers in agentic AI.
- Prakash suggests that more developments in this area will be announced in the coming months, indicating an ongoing commitment to pushing the boundaries of AI technology.
Broader implications for enterprise AI adoption: Together AI’s Enterprise Platform represents a significant step towards addressing key barriers to AI adoption in enterprise environments, potentially accelerating the integration of AI technologies across various industries.
- By enabling deployment in private clouds and on-premises environments, the platform caters to organizations with strict data privacy and security requirements.
- The promise of improved performance and reduced costs could make AI more accessible to a wider range of businesses, potentially democratizing advanced AI capabilities.
- As companies continue to grapple with the challenges of integrating AI into their existing infrastructure, solutions like Together AI’s platform may play a crucial role in bridging the gap between cutting-edge AI technologies and practical enterprise applications.
Together AI promises faster inference and lower costs with enterprise AI platform for private cloud
