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IBM is staking its claim at the top of the open-source AI leaderboard with its new Granite 3.1 series out today.
The Granite 3.1 large language models (LLMs) offer enterprise users extended context length of 128K tokens, new embedding models, integrated hallucination detection and improved performance. According to IBM, the new Granite 8B Instruct model tops open source rivals of the same size including Meta Llama 3.1, Qwen 2.5 and Google Gemma 2. IBM ranked its models across a series of academic benchmarks included in the OpenLLM Leaderboard.
The new models are part of accelerated release cadence of IBM’s Granite open source models. Granite 3.0 was just released in October. At the time, IBM claimed that it has a $2 billion book of business related to generative AI. With the Granite 3.1 update, IBM is focusing on packing more capability into smaller models. The basic idea is that smaller models are easier for enterprises to run and are more cost efficient to operate.
“We’ve also just boosted all the numbers — all the performance of pretty much everything across the board has improved,” David Cox, VP for AI models at IBM Research, told VentureBeat. “We use Granite for many different use cases, we use it internally at IBM for our products, we use it for consulting, we make it available to our customers and we release it as open source, so we have to be kind of good at everything.”
Why performance and smaller models matter for enterprise AI
There are any number of ways an enterprise can evaluate the performance of an LLM with benchmarks.
The direction that IBM is taking is running models through the gamut of academic and real world tests. Cox emphasized that IBM tested and trained its models to be optimized for enterprise use cases. Performance isn’t just about some abstract measure of speed, either; rather, it is a somewhat more nuanced measure of efficiency.
One aspect of efficiency that IBM is aiming to push forward is helping users spend less time to get desired results.
“You should spend less time fiddling wi …