Gemma 2 9B IT, Granite 3.1 8B Instruct, and Llama 3.1 8B Instruct represent three different organizational philosophies at the sub-10B parameter tier — Google, IBM, and Meta — each tuned for a slightly different production use case while landing in similar inference cost ranges. Gemma 2 9B IT is Google DeepMind's 9-billion-parameter model released July 2024, distilled from Gemini Ultra training data. It performs comparably to Llama 3.1 8B on standard benchmarks, with Groq hosting delivering particularly low latency. The 8K context window is the significant constraint: workloads requiring document-length inputs or accumulated multi-turn context will push past it quickly. The Gemma license is permissive for commercial use but not OSI-approved, so legal review is warranted for regulated deployments. IBM's Granite 3.1 8B Instruct, released December 2024 under Apache 2.0, is the enterprise-oriented member of this group. Its 128K context window is the standout spec — four times what Gemma 2 9B offers and on par with Llama 3.1 8B, but with an IBM-tuned bias toward structured outputs, tool-use, and enterprise extraction tasks. If you are running RAG pipelines that need long-document ingestion at 8B-class cost, Granite 3.1 8B is the model to benchmark first. Meta's Llama 3.1 8B Instruct is released under the Llama 3 community license and carries the widest provider coverage of the three, appearing on virtually every major inference platform. Its 131K context window matches Granite 3.1 8B, and on general instruction-following it outpaces both alternatives on most public benchmarks. The Llama 3 community license is not fully open-source but permits commercial use at significant scale. Pick Gemma 2 9B for low-latency short-context inference on Groq. Pick Granite 3.1 8B for enterprise structured-output and RAG pipelines with Apache 2.0 licensing. Pick Llama 3.1 8B when maximum provider coverage and general-purpose quality are the priorities.