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IBM Released new Granite 4.0 Models with a Novel Hybrid Mamba-2/Transformer Architecture: Drastically Reducing Memory Use without Sacrificing Performance

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IBM Released new Granite 4.0 Models with a Novel Hybrid Mamba-2/Transformer Architecture: Drastically Reducing Memory Use without Sacrificing Performance

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IBM’s new Granite 4.0 LLMs feature a groundbreaking hybrid Mamba-2/Transformer architecture, designed specifically for enterprise AI.
This novel design dramatically reduces memory usage by over 70% for long-context and multi-session inference, leading to significant savings in GPU costs.
Despite memory efficiency, Granite 4.0 models maintain or even surpass performance of larger, conventional Transformer models on critical enterprise benchmarks like instruction-following (IFEval), function calling (BFCLv3), and multi-turn RAG (MTRAG).
The models are open-source (Apache-2.0), cryptographically signed, and the first open models covered by an accredited ISO/IEC 42001:2023 AI management system certification, emphasizing trust and compliance.
Granite 4.0 is widely accessible through platforms such as IBM watsonx.ai, Hugging Face, NVIDIA NIM, Ollama, and more, offering variants from 3B dense to 32B hybrid MoE.

Revolutionary Hybrid Architecture: Mamba-2 Meets Transformer
Granite 4.0 Model Variants: Tailored for Diverse Needs
Unpacking Technical Specs and Enterprise Performance Signals
Accessing Granite 4.0: Your Gateway to Efficient AI
Real-World Impact: Enhancing Customer Support
Conclusion
Frequently Asked Questions (FAQ)

The landscape of Large Language Models (LLMs) is constantly evolving, with enterprises grappling with the dual challenges of performance demands and the substantial memory footprint required for effective deployment. High operational costs, particularly for GPU resources, often impede widespread adoption and innovation. Addressing these critical pain points, IBM has unveiled a significant advancement that promises to redefine efficiency in the LLM space.

In a move poised to accelerate enterprise AI adoption, IBM has introduced its latest family of open-source LLMs, engineered to deliver unparalleled memory efficiency without compromising on crucial performance metrics. This new generation of models leverages a cutting-edge architectural paradigm, setting a new benchmark for practical, cost-effective LLM deployment.

IBM just released Granite 4.0, an open-source LLM family that swaps monolithic Transformers for a hybrid Mamba-2/Transformer stack to cut serving memory while keeping quality. Sizes span a 3B dense “Micro,” a 3B hybrid “H-Micro,” a 7B hybrid MoE “H-Tiny” (~1B active), and a 32B hybrid MoE “H-Small” (~9B active). The models are Apache-2.0, cryptographically signed, and—per IBM—the first open models covered by an accredited ISO/IEC 42001:2023 AI management system certification. They are available on watsonx.ai and via Docker Hub, Hugging Face, LM Studio, NVIDIA NIM, Ollama, Replicate, Dell Pro AI Studio/Enterprise Hub, Kaggle, with Azure AI Foundry… This groundbreaking release signals a pivotal shift towards more sustainable and scalable AI solutions for businesses.

Revolutionary Hybrid Architecture: Mamba-2 Meets Transformer

So, what exactly makes Granite 4.0 a game-changer? The core innovation lies in its novel hybrid design. Unlike conventional LLMs that rely solely on monolithic Transformer architectures, Granite 4.0 introduces a paradigm that interleaves a small fraction of self-attention blocks with a majority of Mamba-2 state-space layers. Specifically, this architecture employs a 9:1 ratio, heavily favoring the highly efficient Mamba-2 layers.

This architectural blend is not merely an academic exercise; it yields substantial practical benefits. As per IBM’s technical blog, relative to conventional Transformer LLMs, Granite 4.0-H can reduce RAM by >70% for long-context and multi-session inference. For enterprises, this translates directly into significantly lower GPU costs for achieving a given throughput and latency target. This means organizations can run more sophisticated AI workloads with smaller, more cost-effective GPU fleets.

Beyond memory efficiency, IBM’s internal comparisons reveal impressive performance gains. The smallest Granite 4.0 models have demonstrated superior performance over Granite 3.3-8B, despite utilizing fewer parameters. This indicates a leap in efficiency, where more is achieved with less computational overhead.

Granite 4.0 Model Variants: Tailored for Diverse Needs

IBM is shipping both Base and Instruct variants across four initial models, offering flexibility for various enterprise applications:

Granite-4.0-H-Small: This is the largest hybrid model, boasting 32 billion total parameters with approximately 9 billion active parameters, leveraging a hybrid Mixture-of-Experts (MoE) architecture for scaled performance.
Granite-4.0-H-Tiny: A more compact hybrid MoE option with 7 billion total parameters and roughly 1 billion active parameters, ideal for efficient yet powerful applications.
Granite-4.0-H-Micro: A 3 billion parameter hybrid dense model, offering a balance of efficiency and capability for general use cases.
Granite-4.0-Micro: A 3 billion parameter dense Transformer model, provided for environments or stacks that do not yet fully support hybrid architectures, ensuring broader compatibility.

All these models are released under the permissive Apache-2.0 license and are cryptographically signed, enhancing trust and security for enterprise deployments. IBM proudly states that Granite is the first open model family with accredited ISO/IEC 42001 coverage for its AI management system (AIMS), underscoring their commitment to responsible AI. Furthermore, reasoning-optimized (“Thinking”) variants are planned for release later in 2025, promising even more advanced capabilities.

Unpacking Technical Specs and Enterprise Performance Signals

The engineering behind Granite 4.0 is robust, designed for high-performance enterprise scenarios. The models were trained on samples up to an extensive 512K tokens and rigorously evaluated up to 128K tokens, showcasing their exceptional long-context understanding capabilities. Public checkpoints available on Hugging Face are in BF16 format, with quantized and GGUF conversions also published to facilitate broader accessibility and deployment across different hardware setups. While FP8 is an execution option on supported hardware, it’s important to note that it’s not the format of the released weights.

IBM highlights Granite 4.0’s strong performance across several enterprise-relevant benchmarks, focusing on instruction following and tool-use:

IFEval (HELM): On this critical instruction following evaluation, Granite-4.0-H-Small demonstrates leadership among most open-weights models, trailing only Llama 4 Maverick, which operates at a significantly larger scale. This indicates its robust ability to interpret and execute complex instructions reliably.
BFCLv3 (Function Calling): The H-Small variant proves competitive with larger open and closed models, but at a more attractive price point. This is crucial for applications requiring precise function calling, enabling seamless integration with external tools and APIs.
MTRAG (Multi-Turn RAG): Granite 4.0 shows improved reliability on complex retrieval augmentation generation (RAG) workflows, particularly in multi-turn interactions. This enhancement is vital for applications like sophisticated chatbots or knowledge retrieval systems that need to maintain context over extended conversations.

Accessing Granite 4.0: Your Gateway to Efficient AI

Getting started with Granite 4.0 is designed to be straightforward, reflecting IBM’s commitment to broad accessibility and practical application. The models are available across a wide array of popular platforms, making integration into existing workflows easier than ever.

Actionable Step 1: Discover and Deploy Granite 4.0. You can access Granite 4.0 directly on IBM watsonx.ai. Additionally, the models are distributed via Dell Pro AI Studio/Enterprise Hub, Docker Hub, Hugging Face, Kaggle, LM Studio, NVIDIA NIM, Ollama, OPAQUE, and Replicate. This extensive availability ensures that developers and enterprises can pick the platform that best fits their infrastructure and operational preferences.

Actionable Step 2: Integrate into Your AI Stack. IBM notes ongoing enablement for various serving frameworks, including vLLM, llama.cpp, NexaML, and MLX for hybrid serving. This continuous development effort ensures that Granite 4.0 can be seamlessly integrated into a diverse range of AI deployment environments, allowing organizations to leverage their existing tooling and expertise.

Actionable Step 3: Dive Deeper with Resources. To maximize your understanding and implementation of Granite 4.0, make sure to check out the Hugging Face Model Card for detailed specifications and the Technical details blog post. For practical application, explore the GitHub Page which offers tutorials, code examples, and notebooks to help you get started quickly.

Real-World Impact: Enhancing Customer Support

Imagine a global e-commerce enterprise managing millions of customer inquiries daily. Their existing LLM-powered customer service chatbots often struggle with long, multi-session interactions, leading to slow response times and high GPU costs as context windows grow. By deploying IBM Granite 4.0-H-Tiny, this enterprise can drastically reduce the memory footprint required for each active conversation. The hybrid Mamba-2/Transformer architecture allows the chatbot to maintain extensive conversational history with >70% less RAM, ensuring quick, accurate, and context-aware responses without requiring a massive, expensive GPU cluster. This translates to lower operational costs, improved customer satisfaction, and the ability to scale their AI operations more efficiently than ever before.

Conclusion

IBM’s release of the Granite 4.0 models marks a pivotal moment in the evolution of enterprise AI. The novel hybrid Mamba-2/Transformer stack, combined with active-parameter MoE, presents a highly practical pathway to a lower Total Cost of Ownership (TCO) for LLM deployments. The significant memory reduction—exceeding 70% for long-context inference—and the resulting gains in long-context throughput translate directly into the ability to utilize smaller, more efficient GPU fleets.

Crucially, this efficiency does not come at the expense of performance. Granite 4.0 excels in critical enterprise metrics such as instruction-following (IFEval), tool-use accuracy (BFCLv3), and reliability in complex retrieval workflows (MTRAG). Furthermore, the BF16 checkpoints with readily available GGUF conversions simplify local evaluation pipelines, while the accredited ISO/IEC 42001 certification and cryptographically signed artifacts address key provenance and compliance gaps that frequently stall enterprise deployment.

The net result is a lean, auditable, and high-performing base model family (ranging from 1B to 9B active parameters) that is considerably easier and more cost-effective to productionize than prior 8B-class Transformer models. Granite 4.0 empowers enterprises to unlock the full potential of advanced AI, paving the way for more innovative and resource-efficient applications.

For more detailed information, check out the Hugging Face Model Card and Technical details. Feel free to check out our GitHub Page for Tutorials, Codes and Notebooks. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.

Frequently Asked Questions (FAQ)

Q1: What is the main innovation in IBM Granite 4.0 models?

A1: The primary innovation is a novel hybrid Mamba-2/Transformer architecture. This design interleaves a small fraction of self-attention (Transformer) blocks with a majority of Mamba-2 state-space layers (specifically, a 9:1 ratio), which allows for drastically reduced memory usage while maintaining high performance.

Q2: How much memory can Granite 4.0 save compared to traditional LLMs?

A2: According to IBM, Granite 4.0-H models can reduce RAM by over 70% for long-context and multi-session inference compared to conventional Transformer LLMs. This significant reduction translates directly into lower GPU operational costs for enterprises.

Q3: Are the Granite 4.0 models open-source and what are their licensing terms?

A3: Yes, all Granite 4.0 models are released under the permissive Apache-2.0 license. They are also cryptographically signed and are the first open models covered by an accredited ISO/IEC 42001:2023 AI management system certification, ensuring transparency and compliance for enterprise users.