Model Detail
granite-speech-4.1-2b-nar
—granite-speech-4.1-2b-nar is a code generation model with 2B parameters released by ibm-granite. The model is registered under the feature-extraction pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.
granite-speech-4.1-2b-nar ships with 2B parameters. Total weight footprint is approximately 2.3 GB, which is the relevant figure when planning local-inference VRAM. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
granite-speech-4.1-2b-nar is best fit for code completion, repository-scale Q&A, and pair-programming integrations, and semantic search, retrieval, and clustering pipelines. It is a less obvious choice for one-shot generation of security-critical code without review. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Granite Switch 4.1: Chat, Decisions, and Speech from One Endpoint
MCP-GRANITE Benchmark: GRANularity Interface TEsting for MCP-Based LLM Agents
arXiv:2609.24161v1 Announce Type: cross Abstract: As LLM agents increasingly interact with external tools through standardized protocols such as MCP, tool-interface design becomes a critical yet underexplored factor. How fun{\psi}tionality is decomposed into tools affects whether an agent can select
Design of the IBM Granite 5.0 TurboCTC ASR Model
arXiv:2609.20104v1 Announce Type: new Abstract: We describe the architecture, training methodology and inference speedups of Granite 5.0 Turbo CTC, a 470 million parameter encoder-only model with an excellent speed-accuracy tradeoff. The architecture uses pyramidal temporal subsampling within Confor
IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license
Granite.Trust Policy Tools: Shareable, Actionable Policies for Generative AI Applications
arXiv:2608.23870v1 Announce Type: new Abstract: When it comes to safety policies for generative AI, one size does not fit all. Each organization and use case needs to mitigate different risks depending on the application context, regulatory environment, organizational values, and user personas. Yet,
