Training and deploying massive language models requires thousands of GPUs working in concert, but most researchers lack access to such infrastructure. This creates a fundamental barrier: how do you optimize systems for extreme scale when you can't actually test at that scale?

The Infrastructure Gap

Systems researchers face a catch-22. To understand how LLMs behave on clusters of 10,000 or 32,000 GPUs—to optimize parallelization strategies, memory management, and communication patterns—they need to experiment at those scales. But access to such infrastructure is prohibitively expensive and extremely limited, even at well-resourced institutions.

Without the ability to experiment, optimizing distributed LLM systems becomes educated guesswork, leaving potentially massive performance gains on the table.

STAGE: Simulation at Scale

STAGE (Scalable Synthesis of Distributed LLM Workloads via Symbolic Tensor Graphs) offers an elegant solution: don't run the actual workload, simulate it symbolically.

The framework introduces a Symbolic Tensor Graph (STG) intermediate representation that abstracts the essential characteristics of distributed LLM execution—tensor shapes, operations, and how computations are distributed across devices—without requiring the actual hardware or data.

This symbolic approach enables STAGE to generate execution traces that preserve three critical dimensions of fidelity:

Compute: Accurately models the operations each device performs

Memory: Tracks memory allocation and access patterns across the system

Communication: Captures data movement between devices, a often-critical bottleneck in distributed training

Democratizing Large-Scale Research

The impact is substantial: STAGE can simulate workloads equivalent to approximately 32,000 GPUs, bringing formerly inaccessible research questions within reach of researchers with modest resources. A team with access to a single server can now explore how different parallelization strategies would perform at hyperscale, identify communication bottlenecks before committing to expensive infrastructure, and validate optimizations that would otherwise require millions of dollars in compute time.

Bridging Theory and Practice

By making large-scale LLM systems research accessible, STAGE helps close the gap between those with massive infrastructure and everyone else. The result is a more vibrant research ecosystem where good ideas can be validated before, rather than after, expensive deployment—accelerating the development of more efficient systems for training and serving the next generation of language models.