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ARXIV:2605.11678 · LLM OPTIMIZATION · SUBMITTED 13 MAY · 21:03 UTC · FRESHNESS STALE
ARXIV:2605.11678LLM OPTIMIZATIONSUBMITTED 13 MAY · 21:03 UTCFRESHNESS STALESeungwoo Roh · Huiyeong Kim · Jong-Chan Kim · arXiv
A framework for efficient Vision-Language-Action model inference on memory-constrained GPUs through system-level optimization.
Opportunity summary
Pain A framework for efficient Vision-Language-Action model inference on memory-constrained GPUs through system-level optimization.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A framework for efficient Vision-Language-Action model inference on memory-constrained GPUs through system-level optimization. We present a framework, which enables memory-efficient VLA inference on VRAM-constrained GPUs through system-level optimization alone, without model modification.
End-to-end Vision-Language-Action (VLA) models for autonomous driving unify perception, reasoning, and control in a single neural network, achieving strong driving performance but requiring 20-60GB of GPU memory-far exceeding the 12-16GB available on commodity GPUs.…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We present a framework, which enables memory-efficient VLA inference on VRAM-constrained GPUs through system-level optimization alone, without model modification.
LLM Optimization moved forward this cycle; last verified May 2026. Public score 4.0/10.
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A framework for efficient Vision-Language-Action model inference on memory-constrained GPUs through system-level optimization.
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Paper Pack
10.48550/arXiv.2605.11678A framework for efficient Vision-Language-Action model inference on memory-constrained GPUs through system-level optimization.
Abstract
End-to-end Vision-Language-Action (VLA) models for autonomous driving unify perception, reasoning, and control in a single neural network, achieving strong driving performance but requiring 20-60GB of GPU memory-far exceeding the 12-16GB available on commodity GPUs. We present a framework, which enables memory-efficient VLA inference on VRAM-constrained GPUs through system-level optimization alone, without model modification. Our work proceeds in three stages: (1) Sequential Demand Layering reduces VRAM usage from model-level to layer-level granularity; (2) Pipelined Demand Layering hides parameter transfer time within layer execution time via transfer--compute overlap; and (3) a GPU-Resident Layer Decision Policy, informed by per-module residency benefit analysis, eliminates the residual transfer overhead that pipelining cannot hide. We further propose a performance prediction model that determines the optimal configuration-both the number and placement of resident layers-from a single profiling run with less than 1.3% prediction error across all configurations. Applied to NVIDIA's Alpamayo-R1-10B (21.52GB) on an RTX 5070Ti (16GB), our work achieves up to 3.55x speedup over Accelerate offloading while maintaining full BF16 precision.
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Dimensions overall score 4.0
PROBLEM
A framework for efficient Vision-Language-Action model inference on memory-constrained GPUs through system-level optimization. We present a framework, which enables memory-efficient VLA inference on VRAM-constrained GPUs through system-level optimization alone, without model mod...
METHOD
End-to-end Vision-Language-Action (VLA) models for autonomous driving unify perception, reasoning, and control in a single neural network, achieving strong driving performance but requiring 20-60GB of GPU memory-far exceeding the 12-16GB available on commodity GPUs. We present a...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We present a framework, which enables memory-efficient VLA inference on VRAM-constrained GPUs through system-level optimization alone, without model modification.
WHY NOW
LLM Optimization moved forward this cycle; last verified May 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A framework for efficient Vision-Language-Action model inference on memory-constrained GPUs through system-level optimization. We present a framework, which enables memory-efficient VLA inference on VRAM-constrained GPUs through system-level optimization alone, without model modification.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
End-to-end Vision-Language-Action (VLA) models for autonomous driving unify perception, reasoning, and control in a single neural network, achieving strong driving performance but requiring 20-60GB of GPU memory-far exceeding the 12-16GB available on commodity GPUs. We present a framework, which enables memory-efficient VLA inference on VRAM-constrained GPUs through system-level optimization alone, without model modification.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. We present a framework, which enables memory-efficient VLA inference on VRAM-constrained GPUs through system-level optimization alone, without model modification.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Optimization moved forward this cycle; last verified May 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework for efficient Vision-Language-Action model inference on memory-constrained GPUs through system-level optimization.
Segment
LLM Optimization
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Commercial read
4.0/10 public viability
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reason
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proof status
unverified
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stale
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passport absent
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Technical feasibility
partial
Current read
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Gaps
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missing
Current read
No public implementation surface observed.
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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DEFENSIBILITY
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