🤖Beam Model Packs 501B Parameters, 23B Active Ones
Beam's efficiency makes it a game-changer for enterprise workloads
TL;DR
Beam, a sparse Mixture-of-Experts model with 501 billion parameters, outperforms GLM-5.2 in inference efficiency. It's a powerful workhorse for enterprise coding and agentic tasks, using less compute and delivering strong performance.
Beam, a sparse Mixture-of-Experts model with 501 billion total parameters and 23 billion active ones, has been trained to be highly efficient at inference time. If you're running complex AI workloads, this could save you a ton on compute costs. Beam was pretrained on 23.8 trillion tokens and trained for 4 weeks on 10.5K NVIDIA GPUs, achieving comparable scores to GLM-5.2 but using 3-4 times less inference compute. The model's efficiency gains translate into more intelligence per token, making it a powerful workhorse for enterprise coding and agentic workloads.
Key Points
Beam has 501 billion total parameters and 23 billion active parameters, making it highly efficient.
Trained on 23.8 trillion tokens, generating over 100 million rollouts in 4 weeks on 10.5K NVIDIA GPUs.
Achieves comparable scores to GLM-5.2 but uses 3-4 times less inference compute, making it cost-effective.
Beam's RL training was designed to develop reasoning and agentic capabilities that generalize beyond its training tasks.
The model's performance continued to improve as RL compute increased, with no sign of a plateau.
Why It Matters
If you're running complex AI workloads in an enterprise environment, Beam's 3-4x inference efficiency over GLM-5.2 means significant cost savings. It's a powerful workhorse for coding and agentic tasks, delivering strong performance at a lower cost. Anyone with large-scale AI needs should take a closer look.
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