VLA Models Reshape Robotics as $94B Market Embraces AI Infrastructure

Iris Coleman Feb 12, 2026 04:41

Vision-Language-Action models are driving robotics teams to Ray and Anyscale for distributed training. Market projected to hit $94.38B by 2031.

The robotics industry's rapid adoption of Vision-Language-Action models is creating massive demand for distributed computing infrastructure, with Ray emerging as the framework of choice for teams racing to deploy next-generation AI systems.

Anyscale published technical guidance this week detailing how robotics teams can scale VLA pipelines using its Ray-based platform. The timing aligns with projections that the next-generation industrial robotics market will reach $94.38 billion by 2031, driven largely by the pivot toward VLA architectures.

What's Actually Happening

VLA models combine vision, language understanding, and physical action into unified systems—a departure from the modular approach that dominated robotics for decades. Google DeepMind's RT-2 proved the concept in mid-2023 by treating robot control as sequence prediction, essentially letting machines reason about tasks the way large language models reason about text.

Now the approach has gone mainstream. NVIDIA's Isaac GR00T, Physical Intelligence, and The Robotics and AI Institute all run their VLA training on Ray. NVIDIA's documentation explicitly states they "facilitate fault-tolerant multi-node training and data ingestion via a custom library built on top of the Ray distributed computing library."

The infrastructure demands are substantial. VLA models use transformer architectures similar to LLMs, requiring multi-node GPU clusters for training. Datasets combine video, robot trajectories, sensor data, and language annotations—all needing parallel processing. Simulation environments must run thousands of concurrent instances alongside training jobs.

Why Traditional Setups Break

Single-node workflows hit walls fast. VLA pipelines need different hardware for different tasks: CPU-heavy nodes for simulation, high-end H100s for training, cheaper RTX GPUs for evaluation. Running everything on identical infrastructure wastes money on expensive accelerators doing lightweight work.

Multi-node execution introduces failure modes that single-machine setups avoid entirely. Node crashes cause partial job failures. Dependencies drift between machines. Logs scatter across clusters. Teams end up spending engineering cycles on infrastructure instead of model development.

Market Momentum Building

Investment is flowing into the space. Galaxea, focused on embodied intelligence robotics, closed a 1 billion yuan Series B round last week at a valuation approaching 10 billion yuan. LimX Dynamics founder recently called 2026 "the first year of real deployment for humanoid robots," citing VLA models as essential for real-time motion generation.

The infrastructure layer matters because it determines who can iterate fast enough to capture the market. Anyscale's pitch centers on removing friction: automatic cluster provisioning, fault-tolerant checkpointing, unified observability across training and simulation workloads.

For teams evaluating their stack, the calculus is straightforward. VLA development requires distributed data processing, distributed training, and distributed simulation running in coordination. Build it yourself or buy a platform—but single-node scripts won't cut it anymore.

• robotics
• vla models
• anyscale
• ray
• ai infrastructure