Elastic Gang: Per-Token Membership Change for a Hard-Barriered LLM Inference Gang Co-Scheduled with OS Processes
arXiv:2607.04668v1 Announce Type: cross Abstract: On-device LLM decoding is a hard-barriered CPU-SIMD computation that wants every core for milliseconds per token, while the rest of the OS wants those same cores continuously. A barriered gang cannot simply be dropped into a preemptive scheduler: an unannounced departure deadlocks a barrier, and an unannounced arrival silently corrupts logits. I present the elastic gang of Anima OS, a bare-metal x86-64 Rust kernel in which the inference gang is a first-class schedulable entity whose core membership may change between any two tokens. The core me
The increasing demand for efficient on-device LLM inference and the recognition of OS scheduling challenges for such workloads necessitate novel architectural solutions.
This development addresses a fundamental technical bottleneck in deploying powerful AI models on constrained hardware, potentially accelerating the adoption of ubiquitous AI.
The proposed 'elastic gang' mechanism changes how LLM inference tasks interact with operating systems, enabling more dynamic resource allocation and reducing performance overheads.
- · AI hardware manufacturers
- · On-device AI developers
- · Operating system developers
- · Edge computing providers
- · Inefficient LLM deployment methods
- · Systems not optimized for concurrent AI/OS workloads
Improved performance and efficiency of on-device LLMs through dynamic core allocation.
Broader deployment of sophisticated AI capabilities on edge devices and personal computing hardware.
Reduced reliance on cloud-based inference for many AI tasks, contributing to data privacy and lower latency.
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Read at arXiv cs.AI