Robust AI strategy requires trust and confidence in the AI being deployed. Businesses need to prioritize integrity and security of their models and not just cost.
While inference cost and performance are critical factors in enterprise AI, they are not the only considerations. Enterprises must also ensure their AI models can withstand stringent security, privacy, and regulatory compliance requirements. At the heart of a robust AI strategy lies the ability to develop and maintain trust and confidence in the AI systems being deployed.
DeepSeek took the world by storm upon its release of its latest model, DeepSeek R1, having demonstrated that it is possible to train high-performing models at a fraction of the cost traditionally associated with such endeavors. This cost-efficiency extends to its usage for inference, making it an economically attractive option for businesses looking to leverage AI.
Granite 3.2, is a family of models developed by IBM which consists of language models, time series models, geospatial models, embedding models, speculative decoding models, guardian models and with this latest 3.2 release, vision models.Similar to the DeepSeek R1-distilled Qwen 2.5 7B and Llama 3.1 8B, Granite 3.2, incorporates reasoning capabilities into its language models. However, unlike the DeepSeek-based models, IBM asserts that their approach developing Granite 3.
On the subject of costs, DeepSeek R1 is approximately $2.19 per million output tokens through its API, Granite models running on watsonx.ai range from $0.10 to $0.20 per million output tokens, an order of magnitude less while still delivering on the trusted nature of IBMs models. These costs are for models deployed through a cloud provider. When deployed locally, costs will vary based on the on-prem setup.
IBM Granite 3.2 AI AI Guardrails Trust Security Qwen Llama
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