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IBM Introduces Granite 3.0: High Performing AI Models Built for Business

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2 years ago

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New Granite 3.0 8B & 2B models, released under the permissive Apache 2.0 license, show strong performance across many academic and enterprise benchmarks, able to outperform or match similar-sized modelsNew Granite Guardian 3.0 models deliver IBM’s most comprehensive guardrail capabilities to advance safe and trustworthy AINew Granite 3.0 Mixture-of-Experts models enable extremely efficient inference and low latency, suitable for CPU-based deployments and edge computingNew Granite Time Series model achieved state-of-the-art performance in zero/few-shot forecasting, outperforming models 10 times largerIBM unveils next generation of Granite-powered watsonx Code Assistant for general purpose coding; Debuts new tools in watsonx.ai for building and deploying AI applications and agentsAnnounces Granite will become the default model of Consulting Advantage, an AI-powered delivery platform used by IBM’s 160,000 consultants to bring new solutions to clients faster

ARMONK, N.Y., Oct. 21, 2024 /PRNewswire/ — Today, at IBM’s (NYSE: IBM) annual TechXchange event the company announced the release of its most advanced family of AI models to date, Granite 3.0. IBM’s third-generation Granite flagship language models can outperform or match similarly sized models from leading model providers on many academic and industry benchmarks, showcasing strong performance, transparency and safety.

Consistent with the company’s commitment to open-source AI, the Granite models are released under the permissive Apache 2.0 license, making them unique in the combination of performance, flexibility and autonomy they provide to enterprise clients and the community at large.

IBM’s Granite 3.0 family includes:

General Purpose/Language: Granite 3.0 8B Instruct, Granite 3.0 2B Instruct, Granite 3.0 8B Base, Granite 3.0 2B BaseGuardrails & Safety: Granite Guardian 3.0 8B, Granite Guardian 3.0 2BMixture-of-Experts: Granite 3.0 3B-A800M Instruct, Granite 3.0 1B-A400M Instruct, Granite 3.0 3B-A800M Base, Granite 3.0 1B-A400M Base

The new Granite 3.0 8B and 2B language models are designed as ‘workhorse’ models for enterprise AI, delivering strong performance for tasks such as Retrieval Augmented Geneneration (RAG), classification, summarization, entity extraction, and tool use. These compact, versatile models are designed to be fine-tuned with enterprise data and seamlessly integrated across diverse business environments or workflows.

While many large language models (LLMs) are trained on publicly available data, a vast majority of enterprise data remains untapped. By combining a small Granite model with enterprise data, especially using the revolutionary alignment technique InstructLab – introduced by IBM and RedHat in May – IBM believes businesses can achieve task-specific performance that rivals larger models at a fraction of the cost (based on an observed range of 3x-23x less cost than large frontier models in several early proofs-of-concept1).

The Granite 3.0 release reaffirms IBM’s commitment to building transparency, safety, and trust in AI products. The Granite 3.0 technical report and responsible use guide provide a description of the datasets used to train these models, details of the filtering, cleansing, and curation steps applied, along with comprehensive results of model performance across major academic and enterprise benchmarks.

Critically, IBM provides an IP indemnity for all Granite models on watsonx.ai so enterprise clients can be more confident in merging their data with the models.

Raising the bar: Granite 3.0 benchmarks

The Granite 3.0 language models also demonstrate promising results on raw performance.

On standard academic benchmarks defined by Hugging Face’s OpenLLM Leaderboard, the Granite 3.0 8B Instruct model’s overall performance leads on average against state-of-the-art-performance of similar-sized open source models from Meta and Mistral. On IBM’s state-of-the-art AttaQ safety benchmark, the Granite 3.0 8B Instruct model leads across all measured safety dimensions compared to models from Meta and Mistral.2 

Across the core enterprise tasks of RAG, tool use, and tasks in the Cybersecurity domain, the Granite 3.0 8B Instruct model shows leading performance on average compared to similar-sized open source models from Mistral and Meta.3

The Granite 3.0 models were trained on over 12 trillion tokens on data taken from 12 different natural languages and 116 different programming languages, using a novel two-stage training method, leveraging results from several thousand experiments designed to optimize data quality, data selection, and training parameters. By the end of the year, the 3.0 8B and 2B language models are expected to include support for an extended 128K context window and multi-modal document understanding capabilities.

Demonstrating an excellent balance of performance and inference cost, IBM offers its Granite Mixture of Experts (MoE) Architecture models, Granite 3.0 1B-A400M and Granite 3.0 3B-A800M, as smaller, lightweight models that could be deployed for low latency applications as well as CPU-based deployments.  

IBM is also announcing an updated release of its pre-trained Granite Time Series models, the first versions of which were released earlier this year. These new models are trained on 3 times more data and deliver strong performance on all three major time series benchmarks, outperforming 10 times larger models from Google, Alibaba, and others. The updated models also provide greater modeling flexibility with support for external variables and rolling forecasts.4

Introducing Granite Guardian 3.0: ushering the next era of responsible AI   

As part of this release, IBM is also introducing a new family of Granite Guardian models that permit application developers to implement safety guardrails by checking user prompts and LLM responses for a variety of risks. The Granite Guardian 3.0 8B and 2B models provide the most comprehensive set of risk and harm detection capabilities available in the market today.

In addition to harm dimensions such as social bias, hate, toxicity, profanity, violence, jailbreaking and more, these models also provide a range of unique RAG-specific checks such as groundedness, context relevance, and answer relevance.  In extensive testing across 19 safety and RAG benchmarks, the Granite Guardian 3.0 8B model has higher overall accuracy on harm detection on average than all three generations of Llama Guard models from Meta. It also showed on par overall performance in hallucination detection on average with specialized hallucination detection models WeCheck and MiniCheck.5

While the Granite Guardian models are derived from the corresponding Granite language models, they can be used to implement guardrails alongside any open or proprietary AI models.

Availability of Granite 3.0 models

The entire suite of Granite 3.0 models and the updated time series models are available for download on HuggingFace under the permissive Apache 2.0 license. The instruct variants of the new Granite 3.0 8B and 2B language models and the Granite Guardian 3.0 8B and 2Bmodels are available today for commercial use on IBM’s watsonx platform. A selection of the Granite 3.0 models will also be available as NVIDIA NIM microservices and through Google Cloud’s Vertex AI Model Garden integrations with HuggingFace.

To help provide developer choice and ease of use and support local, edge deployments, a curated set of the Granite 3.0 models are also available on Ollama and Replicate.

The latest generation of Granite models expand IBM’s robust open-source catalog of powerful LLMs. IBM has collaborated with ecosystem partners like AWS, Docker, Domo, Qualcomm Technologies, Inc. via its Qualcomm® AI Hub, Salesforce, SAP, and others to integrate a variety of Granite models into these partners’ offerings or make Granite models available on their platforms, offering greater choice to enterprises across the world. 

Assistants to Agents: realizing the future for enterprise AI 

IBM is advancing enterprise AI through a spectrum of technologies – from models and assistants, to the tools needed to tune and deploy AI specifically for companies’ unique data and use-cases. IBM is also paving the way for future AI agents that can self-direct, reflect, and perform complex tasks in dynamic business environments.

IBM continues to evolve its portfolio of AI assistant technologies – from watsonx Orchestrate to help companies build their own assistants via low-code tooling and automation, to a wide set of pre-built assistants for specific tasks and domains such as customer service, human resources, sales, and marketing. Organizations around the world have used watsonx Assistant to help them build AI assistants for tasks like answering routine questions from customers or employees, modernizing their mainframes and legacy IT applications, helping students explore potential career paths, or providing digital mortgage support for home buyers. 

Today IBM also unveiled the upcoming release of the next generation of watsonx Code Assistant, powered by Granite code models, to offer general-purpose coding assistance across languages like C, C++, Go, Java, and Python, with advanced application modernization capabilities for Enterprise Java Applications.6 Granite’s code capabilities are also now accessible through a Visual Studio Code extension, IBM Granite.Code.

IBM also plans to release new tools to help developers build, customize and deploy AI more efficiently via watsonx.ai – including agentic frameworks, integrations with existing environments and low-code automations for common use-cases like RAG and agents.7

IBM is focused on developing AI agent technologies which are capable of greater autonomy, sophisticated reasoning and multi-step problem solving. The initial release of the Granite 3.0 8B model features support for key agentic capabilities, such as advanced reasoning and a highly-structured chat template and prompting style for implementing tool use workflows.  IBM also plans to introduce a new AI agent chat feature to IBM watsonx Orchestrate, which uses agentic capabilities to orchestrate AI Assistants, skills, and automations that help users increase productivity across their teams.8  IBM plans to continue building agent capabilities across its portfolio in 2025, including pre-built agents for specific domains and use-cases.

Expanded AI-powered delivery platform to supercharge IBM consultants with AI 

IBM is also announcing a major expansion of its AI-powered delivery platform, IBM Consulting Advantage. The multi-model platform contains AI agents, applications, and methods like repeatable frameworks that can empower 160,000 IBM consultants to deliver better and faster client value at a lower cost.

As part of the expansion, Granite 3.0 language models will become the default model in Consulting Advantage. Leveraging Granite’s performance and efficiency, IBM Consulting will be able to help maximize the return-on-investment for the generative AI projects of IBM clients. 

Another key part of the expansion is the introduction of IBM Consulting Advantage for Cloud Transformation and Management and IBM Consulting Advantage for Business Operations. Each includes domain-specific AI agents, applications, and methods infused with IBM’s best practices so IBM consultants can help accelerate client cloud and AI transformations in tasks, like code modernization and quality engineering, or transform and execute operations across domains, like finance, HR and procurement.

To learn more about Granite and IBM’s AI for Business strategy, visit https://www.ibm.com/granite.

1 Cost calculations are based on API cost per million tokens pricing of IBM watsonx for open models and openAI for GPT4 models (assuming blend of 80% inout, 20% output) for customer proofs-of-concept.
2 IBM Research technical paper: Granite 3.0 Language Models
3 IBM Research technical paper: Granite 3.0 Language Models
4 The Tiny Time Mixer: Fast Pre-Trained Models for Enhanced Zero/Few Shot Forecasting on Multivariate Time Series
5 Evaluation results published in Granite Guardian GitHub Repo
6 Planned availability for Q4 2024
7 Planned availability for Q4 2024
8 Planned availability for Q1 2025

Media Contact:
Amy Angelini
alangeli@us.ibm.com

 

SOURCE IBM

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Annexus Health Unifies Patient Financial Assistance with Connected, End-to-End Platform

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59 minutes ago

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November 10, 2026

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Built on AssistPoint®, the Annexus Platform connects patient assessment and enrollment, claims management, and ongoing funding management to help patients start treatment sooner and enable healthcare providers to improve reimbursement performance

CRANBERRY TOWNSHIP, Pa., Nov. 10, 2026 /PRNewswire/ — Annexus Health today announced the launch of the Annexus Platform, designed to help healthcare providers identify, secure, and manage patient financial assistance throughout treatment. Built on the proven foundation of the AssistPoint software, the Platform uses AI and automation within existing provider workflows to match patients with assistance opportunities, enroll patients in financial assistance programs, manage claims, and maintain funding across the course of care.

Provider organizations can manage the Platform using their own teams, share workflow execution with Annexus, or outsource financial assistance operations through Annexus Managed Services. The Platform works alongside existing provider systems and requires no upfront capital investment.

Financial toxicity, or the financial distress caused by medical costs, can put needed treatment out of reach, especially in specialty care. Even with health insurance, patients with cancer can face out-of-pocket costs that disrupt access to care, according to the National Cancer Institute.

Financial assistance can help, but provider teams often manage program requirements, applications, claims, and renewals through a patchwork of spreadsheets, manual processes, and separate program portals. That fragmented administrative work leaves less time to help patients understand their options and secure support before costs become a barrier to care. Assistance efforts can also be reactive, beginning only when a patient asks for help. As a result, eligible patients can be overlooked and available funding can go unused. Delays in assistance can affect treatment starts and adherence, while unresolved claims can delay provider reimbursement.

Founded in 2017, Annexus pioneered software-as-a-service technology to connect provider workflows with financial assistance programs. As its network expanded beyond community oncology into infusion centers, specialty pharmacies, home infusion, and health systems, the company added operational services to support provider needs. The Platform and Annexus Managed Services build on that experience to help organizations manage the full financial assistance lifecycle at scale.

“Since partnering with Annexus Health, we’ve seen a significant evolution in how we support patients and manage financial assistance,” said Dr. Jeff Vacirca, CEO of New York Cancer & Blood Specialists. “The Annexus Platform helps us connect patients to manufacturer co-pay assistance and foundation funding, and with Annexus Managed Services, we’ve been able to extend that impact even further by strengthening our revenue cycle processes and achieving outcomes we hadn’t thought possible.”

Three Connected Workflows
The Platform brings treatment orders, assistance applications, claims, and funding information together across provider systems, including electronic health record, practice management, billing, infusion, and pharmacy systems. This connection supports three core workflows:

Initial Patient Assessment: Automatically screens every treatment order and uses AI to help match patients with thousands of financial assistance opportunities, including all manufacturer co-pay programs and charitable foundation programs across high-cost specialty therapeutics. The Platform advances likely matches through an organized enrollment processing workflow, helping teams manage applications and track status and outcomes.Claims Management: Uses automation and AI-supported matching to review medical benefit claims from provider systems and identify those that may qualify for reimbursement through assistance programs. The Platform then helps teams prepare, submit, track, and resolve eligible claims, with final documentation returned to the provider’s billing system.Ongoing Patient Management: Monitors assistance throughout treatment, including award balances and expiration dates. It alerts teams when funds run low, keeps assistance information accurate through reconciliation, maintains a watchlist for patients awaiting available funding, and supports re-enrollment.

For providers, these workflows help teams identify assistance earlier, reduce administrative work, and maintain funding throughout treatment while helping improve financial assistance collections and reimbursement performance.

Connecting Life Sciences Programs with Provider Workflows
For life sciences organizations, the Platform extends patient support programs into provider workflows where financial assistance is managed and works alongside existing hub and patient access infrastructure. When connectivity with a program is enabled, relevant enrollment, claims, and ongoing funding activity can be exchanged with the Platform, helping keep information synchronized across systems. Annexus’ dedicated life sciences team works with manufacturers to extend that connectivity across their brand portfolios.

“Patients should not have to know which assistance programs exist or when to ask for help,” said Brad Frazier, CEO and co-founder of Annexus Health. “Our goal is to help providers identify that support earlier and manage it throughout treatment. By connecting assessment, enrollment, claims management, and ongoing funding management, the Platform takes on much of the administrative work, giving teams more time to focus on and interact with patients. That can help with faster treatment starts and reduced financial toxicity while improving reimbursement performance for providers.”

Real-World Results Across the Annexus Network
Annexus has helped secure more than $11 billion in financial assistance for more than 400,000 patients. Its provider network spans more than 735 healthcare organizations, 32,000 sites of care, and 124,000 treating providers. Measured results include:

5.2-day reduction in time to treatment, on average75% increase in financial assistance collections, on average*55% lower cost per dollar of financial assistance collected, on average*78% reduction in days to payment on digitally integrated claims, from 19.5 days to 4.3 days

*Based on pre- and post-implementation comparisons among representative provider clients.

Annexus maintains HITRUST r2 Certification, an independent validation of its information security program.

Annexus will showcase the Platform at booth P10 during McKesson Accelerate, Nov. 16-18, at Wynn Las Vegas. For more information, visit annexushealth.com.

About Annexus Health
Annexus Health is a healthcare technology company providing the connected infrastructure behind patient financial assistance. Through automation, intelligence, and operational expertise, Annexus helps providers and life sciences organizations reduce complexity, improve organizational performance, and connect more eligible patients to available financial support throughout the patient journey.

Media contact:
AnnexusPR@supremecomms.ai

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SOURCE Annexus Health

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Typeform Brings Form Creation, Customer Insights, and Automated Workflows to ChatGPT and Claude

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59 minutes ago

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November 10, 2026

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New capability lets teams create forms and landing pages, analyze responses, and automate next steps within the AI tools they already use

SAN FRANCISCO, Nov. 10, 2026 /PRNewswire/ —Typeform, the AI research platform that turns conversations into qualitative and quantitative insights, today announced that it is now available directly within ChatGPT, Claude, and other MCP-compatible tools through a Model Context Protocol (MCP) server, giving businesses the power to use Typeform where and how they work.

AI tools are increasingly becoming a starting point for work, but the customer conversations and feedback that inform that work often live across separate applications and workflows. Many teams still move between platforms to manually transfer vital context, adding steps that prevent them from acting quickly. Typeform’s MCP server helps close that gap by allowing users to access Typeform capabilities from AI tools. Instead of navigating across tools to build a form or landing page or configure follow-up, users can describe what they want to accomplish in natural language, and those tasks will be completed directly in Typeform.

“AI makes it easier to act, but better decisions still depend on understanding your customers,” said Aleks Bass, Chief Product and Technology Officer at Typeform. “We’re bringing customer feedback into the tools where teams are already working, so they can ask a question, understand what they hear, and take the next step while that insight is still relevant.”

With Typeform’s MCP server, teams can do the following directly from ChatGPT, Claude, or other AI assistants, without switching to Typeform:

Create customer conversations faster: Describe the form, landing page, or study and use AI to create, edit, and publish it in Typeform.Turn feedback into insight: Ask questions about responses, identify recurring themes, and summarize what customers are saying without manually exporting the data for analysis.Work with customer context: Create and update contact profiles, ensuring the most recently collected data is synced.Act on what customers say: Build and edit automations for follow-up messages and next steps based on customer responses.

For example, a marketer could ask an AI assistant to create a lead-generation form, organize the contacts it generates, and set up follow-up. A customer experience team could use an assistant to analyze feedback responses and surface recurring themes, all without leaving the ChatGPT or Claude interface.

The connection also works in the other direction. With MCP connectors in Typeform AI, teams can bring business context from platforms like Canva, Notion, Slack, Shopify Storefront, and Stripe into Typeform to create more relevant experiences informed by existing customer information. A product team could turn a Slack thread into a customer feedback form, while an e-commerce team could use its Shopify product catalog to build a personalized product recommendation quiz.

This launch is part of Typeform’s broader investment in AI-powered customer understanding. Earlier this year, Typeform launched Research Flow, its AI-moderated research platform that combines quantitative scale with qualitative depth to help teams uncover not only what customers say, but why. Looking ahead to early 2027, Typeform plans to extend these same MCP-powered capabilities to Research Flow, enabling teams to bring AI-moderated research into the tools and workflows they already use.

Typeform’s MCP server is available on all Typeform plans. Users can connect Typeform directly to ChatGPT, Claude, or Cursor to get started. For more information, including how to connect other MCP-compatible AI tools, visit typeform.com/mcp.

About Typeform
Typeform helps businesses turn conversations into insight and action. Its platform brings together human-first forms, AI-moderated research that combines qualitative depth with quantitative scale, and AI-powered workflows to help teams engage customers, understand them more deeply, and make better decisions. Typeform serves more than 150,000 customers and has been used by 95% of the Fortune 500. For more information, visit typeform.com.

 

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SOURCE Typeform S.L.

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Rescale Expands Agentic Digital Engineering with Customizable AI Agents for R&D Teams

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59 minutes ago

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November 10, 2026

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Kairos Power and Oak Ridge National Laboratory use agentic workflows on the Rescale platform for advanced nuclear reactor design and manufacturing

SAN FRANCISCO, Nov. 10, 2026 /PRNewswire/ — Rescale, the digital engineering platform for the AI era, today introduced new agentic digital engineering capabilities that allow R&D and engineering teams across advanced industries to customize AI agents to their exacting standards, bringing agentic AI into engineering work where every result must be reviewable and traceable.

As engineering organizations continue to deploy more agents into production, the work is shifting from automating individual tasks to running end-to-end engineering processes. That shift requires an engineering-specific agentic harness: the structure and context that govern how agents operate, ensure they meet specific standards, and keep humans in the loop. 

New Agentic Projects on the Rescale platform provide the framework and structure, giving engineers a way to customize agents and build reusable agentic workflows. Teams can define each project with their own context, skills, tools, and guardrails, share projects across teams, and retain approved learnings through project memory. Expanded engineering intelligence capabilities supply the underlying context by connecting data across requirements, design, simulation, test, and process systems into foundational knowledge graphs. As agents execute workflows, the platform generates project-specific work graphs that provide traceability and capture critical learnings. Those learnings enrich agent context, build domain expertise, preserve knowledge, and drive process improvements that make engineering work more efficient and scalable. 

Rescale is also introducing Rescale Desktop, which extends Rescale directly to engineers’ local machines, along with expanded Model Context Protocol (MCP) connectors for interoperability across Rescale’s leading ecosystem of engineering applications and complementary agentic tools, including Rescale MCP availability on the Claude and Codex directories.

Rescale Edison, the platform’s AI assistant and agent orchestrator, now includes persistent memory so engineers can work faster by describing their intent in natural language. Together, these new capabilities work to provide an engineering-specific harness that ensures agents execute to the same standards as experienced engineers, helping organizations develop the trust in agentic outcomes needed to transform operations.

“Our customers build aircraft, vehicles, and reactors, so the agents supporting that work have to deliver reliable results. Agentic Projects provide the needed context that grows with every iteration. This is a material advancement toward an engineering harness built for how these teams actually work,” said Jacob Surber, vice president of product at Rescale.

Kairos Power and the Department of Energy’s Manufacturing Demonstration Facility (MDF) at Oak Ridge National Laboratory (ORNL), with support from Rescale AI developer engineers, have developed a digital agentic scaffolding on the Rescale platform as part of a collaboration to test advanced manufacturing and construction methods for Hermes 2, Kairos Power’s 50-megawatt fluoride-salt-cooled reactor. Working in a shared environment, agents have exercised thermal, flow, and shielding simulation and reporting workflows for the reactor cavity and bioshield plug designs, with engineers approving each agent action. Kairos Power projects that agentic workflows built from past simulations could increase end-to-end simulation analysis efficiency by up to 3x while preserving the data traceability required for regulatory compliance.

“Kairos Power depends on quick learning cycles: design it, build it, test it, and carry what we learn into the next round,” said Ed Blandford, chief technology officer and co-founder of Kairos Power. “Much of the difficulty in scaling nuclear energy comes down to manufacturing, from the smallest components to large structures. Rescale brings agentic engineering into the simulations that support that work, connecting our engineers with our ORNL collaborators in one environment built for context and traceability. With agents managing setup, execution, and reporting, our people can spend their time on the engineering judgment and rigor the work demands.”

ORNL is encoding its simulation and additive manufacturing expertise in agents on the platform so that engineers can adopt advanced methods to address industry challenges. 

“The Manufacturing Demonstration Facility works because industry engineers and lab researchers solve problems side by side,” said Ryan Dehoff, MDF director. “Rescale provides a digital environment for that collaboration. Nuclear innovation demands speed and rigor at the same time, and you can’t trade one for the other. Agents allow ORNL’s expertise to scale beyond our facility.”

The launch also highlights how agentic digital engineering works with Rescale’s AI physics solution to make surrogate models easier to build and more accessible to engineering teams. A new dataset creation agent automates the process of turning simulation data into training-ready datasets, reducing the specialized machine learning work involved. This is one of many new AI physics capabilities that help companies put valuable surrogate models into the hands of more engineers:

Auto Tune and guided pre-processing automate and simplify surrogate model training so organizations can develop better models with fewer specialized resources. Uncertainty quantification builds trust that engineers are using an appropriate surrogate model and flags predictions that should be confirmed with a full-fidelity simulation.Design explorer is a new local interface that allows design engineers to use surrogate models to evaluate thousands of design options from their local environment.

For high performance computing (HPC) administrators, new cluster performance metrics show where execution slows and why jobs are waiting. A new priority score considers wait time, job size, and resource usage to help teams allocate resources fairly.

These new and enhanced capabilities across agentic digital engineering, AI physics, and intelligent HPC work together to deliver compounding value for R&D and engineering organizations as they adopt AI-first operations across every stage of their product development lifecycles.

Organizations can learn more about the new capabilities at rescale.com.

About Rescale

Rescale is the digital engineering platform for the AI era. The R&D and engineering teams shaping the future across aerospace, automotive, defense, energy, public sector, life sciences, and manufacturing trust the Rescale platform to accelerate innovation. Rescale integrates agentic digital engineering, AI physics, advanced modeling and simulation, and intelligent HPC solutions on a single platform to unify product data, automate workflows, optimize resources, and bring breakthroughs to market faster.

Rescale is backed by leading investors such as NVIDIA, Sam Altman, Jeff Bezos, Paul Graham, Peter Thiel, Microsoft, Hitachi, and University of Michigan.

For more information, visit rescale.com.

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SOURCE Rescale Inc.

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