Paper
How Fast Can I Run My VLA? Demystifying VLA Inference Performance with VLA-Perf
Authors
Wenqi Jiang, Jason Clemons, Karu Sankaralingam, Christos Kozyrakis
Abstract
Vision-Language-Action (VLA) models have recently demonstrated impressive capabilities across various embodied AI tasks. While deploying VLA models on real-world robots imposes strict real-time inference constraints, the inference performance landscape of VLA remains poorly understood due to the large combinatorial space of model architectures and inference systems. In this paper, we ask a fundamental research question: How should we design future VLA models and systems to support real-time inference? To address this question, we first introduce VLA-Perf, an analytical performance model that can analyze inference performance for arbitrary combinations of VLA models and inference systems. Using VLA-Perf, we conduct the first systematic study of the VLA inference performance landscape. From a model-design perspective, we examine how inference performance is affected by model scaling, model architectural choices, long-context video inputs, asynchronous inference, and dual-system model pipelines. From the deployment perspective, we analyze where VLA inference should be executed -- on-device, on edge servers, or in the cloud -- and how hardware capability and network performance jointly determine end-to-end latency. By distilling 15 key takeaways from our comprehensive evaluation, we hope this work can provide practical guidance for the design of future VLA models and inference systems.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.18397v1</id>\n <title>How Fast Can I Run My VLA? Demystifying VLA Inference Performance with VLA-Perf</title>\n <updated>2026-02-20T18:02:28Z</updated>\n <link href='https://arxiv.org/abs/2602.18397v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.18397v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Vision-Language-Action (VLA) models have recently demonstrated impressive capabilities across various embodied AI tasks. While deploying VLA models on real-world robots imposes strict real-time inference constraints, the inference performance landscape of VLA remains poorly understood due to the large combinatorial space of model architectures and inference systems. In this paper, we ask a fundamental research question: How should we design future VLA models and systems to support real-time inference? To address this question, we first introduce VLA-Perf, an analytical performance model that can analyze inference performance for arbitrary combinations of VLA models and inference systems. Using VLA-Perf, we conduct the first systematic study of the VLA inference performance landscape. From a model-design perspective, we examine how inference performance is affected by model scaling, model architectural choices, long-context video inputs, asynchronous inference, and dual-system model pipelines. From the deployment perspective, we analyze where VLA inference should be executed -- on-device, on edge servers, or in the cloud -- and how hardware capability and network performance jointly determine end-to-end latency. By distilling 15 key takeaways from our comprehensive evaluation, we hope this work can provide practical guidance for the design of future VLA models and inference systems.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.RO'/>\n <published>2026-02-20T18:02:28Z</published>\n <arxiv:primary_category term='cs.RO'/>\n <author>\n <name>Wenqi Jiang</name>\n </author>\n <author>\n <name>Jason Clemons</name>\n </author>\n <author>\n <name>Karu Sankaralingam</name>\n </author>\n <author>\n <name>Christos Kozyrakis</name>\n </author>\n </entry>"
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