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IBM Releases 97M-Parameter Granite Embedding Model With 60.3 MTEB Score — Highest Retrieval Quality Under 100M Parameter

IBM released two new multilingual embedding models under Apache 2.0: a 97M-parameter compact model scoring 60.3 on MTEB Multilingual Retrieval (highest in its size class) and a 311M full-size model scoring 65.2. Both support 200+ languages with enhanced retrieval for 52 languages, handle 32K-token context (64x increase over predecessors), and include code retrieval across 9 programming languages.

3 min read
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IBM Releases Granite Speech 4.1 2B: 2-Billion-Parameter Multilingual Speech Model with Non-Autoregressive Variant

IBM has released Granite Speech 4.1 2B, a 2-billion-parameter speech-language model trained on 174,000 hours of audio for automatic speech recognition and translation across English, French, German, Spanish, Portuguese, and Japanese. The model introduces a dual-head CTC encoder and includes variants for speaker attribution and a novel non-autoregressive architecture for higher throughput.

2 min read
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IBM's Granite 4.1: 8B Dense Model Matches 32B MoE Performance on 15T Tokens

IBM released Granite 4.1, a family of dense decoder-only LLMs (3B, 8B, 30B parameters) trained on approximately 15 trillion tokens using a five-phase pre-training pipeline. The 8B instruct model matches or surpasses the previous Granite 4.0-H-Small (32B-A9B MoE) despite using fewer parameters and a simpler dense architecture. All models support up to 512K context windows and are released under Apache 2.0 license.

3 min read