Do You Really Need a GPU to Guard Your LLM? CPU-Class Classifiers and Multi-Stage Pipelines for Safety Enforcement at Scale
arXiv:2512.19011v3 Announce Type: replace-cross Abstract: Safety classifiers that screen LLM inputs for jailbreak attempts have become standard deployment components, yet almost all production systems rely on GPU-based models: fine-tuned transformers and LLM-as-a-judge pipelines. These approaches impose significant per-query latency and infrastructure cost. Very little research has asked whether CPU-based classifiers, such as support vector machines and gradient-boosted trees trained on TF-IDF features, can match their accuracy across the conditions that production deployments encounter. We ev
The rapid deployment of LLMs and the recognition of their computational cost for safety layers are driving the search for more efficient solutions.
Reducing the computational overhead of LLM safety features can significantly lower deployment costs and increase accessibility, impacting the overall economics of AI.
The feasibility of deploying robust, cost-effective LLM safety mechanisms on less powerful hardware, potentially broadening the application and scalability of AI.
- · CPU manufacturers
- · AI startups with budget constraints
- · Edge AI developers
- · Large language model deployers
- · GPU manufacturers focused solely on high-end inference
- · Cloud providers reliant on GPU-centric billing for safety layers
Lower operational costs for deploying LLMs due to reduced GPU reliance for safety classifiers.
Increased adoption of LLMs in environments with limited power or budget, such as edge devices or smaller enterprises.
Democratization of advanced AI safety features, potentially accelerating broader LLM integration into everyday applications without prohibitive infrastructure costs.
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Read at arXiv cs.CL