Inputs
model checkpoints · hardware requirement
Agentic deployment infrastructure for Physical AI
From trained model to the hardware you ship.
AnyDevice turns the manual work of optimizing, validating and packaging AI models for physical devices into a repeatable deployment loop.
AnyDevice
recursively improving auto-optimization harness
Deliverables
cross-device model runtime
Private by design
Keep control of your models, data and deployment artifacts.
Hardware-aware
Optimize and validate for the silicon and constraints you actually ship.
Self-improving harness
The agent loop itself is rewritten between runs — roles, contracts and orchestration get sharper on your stack with every iteration.
The problem
Every new model, hardware or firmware update recreates the deployment problem.
Conversion, runtime integration, optimization and regression checks are typically fragmented across tools and specialists. That slows the move from an impressive model to an operational physical system.
The platform
A closed loop for the path from model to machine.
AnyDevice Agents work across the deployment stack, and the harness itself sharpens between runs — instead of treating optimization as a one-time export.
- 01
Define the target
Bring the model, hardware and the operational constraints that matter.
- 02
Search the deployment space
Explore compatible model, runtime and hardware-aware optimization paths.
- 03
Validate on target
Check the chosen path against the target device before it reaches the field.
- 04
Ship an artifact
Hand off a deployment-ready package and the context behind it.
Hardware-aware by default
Start from the real compute, memory, latency and runtime constraints.
Full-stack deployment path
Coordinate model, operator, runtime and artifact decisions in one workflow.
Evidence over promises
Keep validation tied to the workload and device being evaluated.
Where we start
Built for teams putting AI into the physical world.
Warehouse robotics
Put perception, navigation and action models onto the robot hardware that runs the operation.
- VLM / VLA
- Perception
- Manipulation
Industrial vision
Move inspection intelligence to the factory floor with a validated runtime for the target device.
- Vision
- Inspection
- Edge runtime
Physical AI systems
Extend the same deployment loop across cameras, machines and other on-device AI products.
- Cross-hardware
- On-device
- Production
Validation is part of deployment
Know what you are shipping.
The useful question is not whether a model runs somewhere. It is whether the selected path meets the operational requirements of the device in front of you.
Latency and responsiveness
Memory and compute envelope
Output quality against task requirements
Power and runtime compatibility
The AnyDevice difference
An MCP server that turns any LLM into a deployment expert.
LLM-agnostic and self-improving, it performs in-depth analysis, makes cross-hardware optimization, evaluation and deployment repeatable, and tackles unseen deployment challenges — with no data or model secrets ever leaving your device. Each deployment produces pairwise feedback that rewrites the agent's roles, contracts and orchestration, so the harness specializes to your hardware instead of staying generic.
Benchmark
Benchmark case studies across physical AI workloads.
Two workloads, one deployment harness: robotics models evaluated across Ada6000 and Jetson AGX Thor, plus CV workloads on RTX 6000 Ada.
Case study 01
Small VLA & Pi0.5 models
Pi0.5
- Ada6000PyTorch Eager (baseline)200 ms1.0× faster
- Ada6000Ours (stable pipeline)*78 ms2.6× faster
- Ada6000Ours (ultra-fast pipeline)*47 ms4.3× faster
- ThorPyTorch Eager (baseline)320 ms1.0× faster
- ThorOurs (stable pipeline)*71 ms4.5× faster
- ThorOurs (ultra-fast pipeline)*44 ms7.3× faster
SmolVLA
- Ada6000PyTorch Eager (baseline)249 ms1.0× faster
- Ada6000Ours (stable pipeline)*42 ms5.9× faster
- Ada6000Ours (ultra-fast pipeline)*30 ms8.3× faster
- ThorPyTorch Eager (baseline)355 ms1.0× faster
- ThorOurs (stable pipeline)*68 ms5.2× faster
- ThorOurs (ultra-fast pipeline)*54 ms6.6× faster
* Stable pipeline runs with the original model; no model changes required.* Ultra-fast pipeline requires LoRA fine-tuning for the target model.
Comparison with SoTA
AnyDevice vs SoTA Competitors
Pi0.5 · Ada6000 · PyTorch Eager baseline 200 ms
Pi0.5 · Thor · PyTorch Eager baseline 320 ms
SmolVLA is omitted — neither repo supports it. Bars show speedup over PyTorch Eager on the same hardware.
Case study 02 · CV workloads
Agentic Pipeline on CV workloads
microsoft/resnet-18
Classification- PyTorch Eager (baseline)1.185 ms1.0×
- Ours (stable)0.174 ms6.8× faster
facebook/dinov2-small-imagenet1k-1-layer
Feature Extraction- PyTorch Eager (baseline)2.871 ms1.0×
- Ours (stable)0.437 ms6.6× faster
facebook/sam-vit-base (vision encoder)
Vision encoder- PyTorch Eager (baseline)89.900 ms1.0×
- Ours (stable)26.6 ms3.4× faster
ultralytics/yolo26n
Object detection- PyTorch Eager (baseline)5.066 ms1.0×
- Ours (stable)0.898 ms5.6× faster
* Tested on RTX 6000 Ada with 50 warmup + 1000 iters average.
Blogs
Notes from the deployment frontier.
Engineering · 8 min read
How an AI agent adds new models to our ultra-fast inference pipeline
What changes when the work of porting a new VLA/WAM into a 30 ms robot stack goes from a 3–5 day engineering task to a 2–3 hour automated run with explicit correctness and latency gates.
Read article Deployment · 4 min readDeployment is a system problem, not a conversion step
Why a model that works in the lab can still fail on the device that has to ship it.
Read article Engineering · 3 min readValidation belongs on the target device
Latency, memory, accuracy and compatibility are deployment questions, not spreadsheet assumptions.
Read article