Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference

Researchers present LLMFI, a fault-injection framework that systematically studies how hardware errors propagate through large language model inference across multiple domains. The study identifies critical vulnerability patterns and proposes four software-only reliability improvements, providing practical guidance for deploying LLMs in high-performance computing environments.

As large language models become integral to scientific computing and HPC workflows, understanding their robustness against hardware failures becomes critical. This research addresses a significant gap by examining how soft errors—transient faults common in computing systems—cascade through LLM inference pipelines. The LLMFI framework enables deterministic, configurable fault injection across diverse models and tasks, moving beyond theoretical assumptions to empirical validation.

The study emerges amid growing recognition that LLMs, while powerful, operate as black boxes with unpredictable failure modes. Hardware errors in memory, processors, or interconnects can corrupt intermediate computations, yet their downstream effects on model outputs remain poorly understood. By testing three open-weighted models across thirteen tasks spanning reasoning, multilingual, mathematical, and coding domains, the researchers capture real-world complexity that single-task studies miss.

For organizations deploying LLMs in mission-critical scientific applications, this work has immediate practical value. The identification of vulnerability patterns enables targeted hardening strategies without full redundancy, reducing the computational overhead of error detection. The four proposed software-only modifications are particularly significant—they offer cost-effective reliability improvements without requiring specialized hardware, making resilient LLM deployment accessible to resource-constrained researchers.

Looking forward, this framework and its seventeen takeaways establish foundations for a new reliability discipline around LLM inference. Future work will likely expand to quantifying error rates across different hardware platforms and developing automated vulnerability scanning tools. As LLMs transition from research artifacts to production systems in scientific computing, systematic understanding of failure modes becomes as essential as performance optimization.

• →Soft errors propagate through LLM inference with variable impact depending on computational stage and error location, requiring domain-specific mitigation strategies.
• →LLMFI enables deterministic fault injection across diverse models and tasks, providing empirical data on error propagation previously unavailable to researchers.
• →Four software-only reliability improvements can enhance LLM robustness without hardware modifications, making resilience accessible for resource-constrained deployments.
• →Vulnerability patterns vary significantly across reasoning, multilingual, mathematical, and coding tasks, indicating task-specific error sensitivity.
• →Study yields 17 actionable takeaways advancing understanding of LLM reliability in high-performance computing environments.