FIG.00 / EST. 2026 / HRB 751537

Embodied AI
for the humanoid
era.

ALVPHA Tech develops an ARM-native, hardware-agnostic embodied AI framework for humanoid robotics — one coherent stack from NVIDIA Grace Blackwell training clusters to Jetson Thor edge deployment.

SYSARM-NATIVE
EDGEJETSON THOR
COREGRACE BLACKWELL
STACKISAAC / GR00T
HQGERMANY
FIG.01 / MISSION

One stack,
edge to data
center.

The next generation of robotics is no longer hypothetical. Figure, Boston Dynamics, Agility, Tesla Optimus, Unitree and 1X are scaling humanoid platforms toward commercial deployment between 2026 and 2028.

But the AI stack behind these robots remains fragmented. Training happens on x86 datacenter GPUs. Deployment runs on ARM edge silicon. Every transition costs engineering effort, introduces quantization drift, and burns energy that mobile humanoids cannot afford.

ALVPHA Tech eliminates this gap. Our framework runs natively on ARM throughout — from training on NVIDIA Grace Blackwell to inference on Jetson Thor, with energy efficiency as a first-class design constraint, not an afterthought.

≥ 70%
REDUCTION IN x86→ARM INTEGRATION OVERHEAD
130 W
TARGET EDGE COMPUTE ENVELOPE (JETSON THOR)
FIG.02 / TECHNOLOGY STACK

Built on NVIDIA. Extended by ALVPHA.

— COMPUTE LAYER

Hardware

  • NVIDIA Jetson AGX Thor — edge / deployment, 2,070 FP4 TFLOPS, 128 GB unified memory, ARM Neoverse V3AE.
  • NVIDIA DGX Station GB300 — R&D, 784 GB unified memory, 20 PFLOPS FP4, Grace Blackwell Ultra.
  • Grace Hopper cloud burst — Nebius EU, CoreWeave, for peak training workloads.
— SOFTWARE LAYER

Frameworks

  • NVIDIA Isaac Lab + Sim — simulation and synthetic data generation.
  • NVIDIA GR00T (N1.5) — foundation Vision-Language-Action models for humanoids.
  • TensorRT-LLM + NIM — optimized inference and microservice deployment.
  • LeRobot · ROS2 · MuJoCo — open-source integration layer.
— PROPRIETARY LAYER / ALVPHA

ALVPHA Memory Core + Energy Optimization Layer

MEMORY CORE

Long-term agentic memory and knowledge representation for embodied agents operating across extended timeframes.

ENERGY LAYER

Joules-per-inference profiling, policy compression, and runtime adaptation for mobile compute envelopes.

HW ABSTRACTION

Hardware-agnostic deployment target — Jetson Thor today, extensible to next-generation ARM edge silicon.

FIG.03 / EXECUTION ROADMAP

Three phases. From simulation to production.

PHASE 01
2026

Validation

Open-hardware integration on SO-ARM101 + Jetson AGX Thor. MuJoCo simulation pipeline. ACT and Diffusion Policy training for manipulation tasks.

FUNDING: ZIM · FORSCHUNGSZULAGE 2026

PHASE 02
2027

ADAM.

DEV PLATFORM

Proprietary humanoid development platform. GR00T-based VLA fine-tuning at scale on DGX GB300. First commercial pilots with German Mittelstand integrators.

FUNDING: EIC ACCELERATOR · INVEST BW · STRATEGIC PARTNERSHIPS

PHASE 03
2028

EVE.

PRODUCTION

Production-grade humanoid AI brain. Framework licensing to robotics OEMs. Multi-robot orchestration and fleet management.

FUNDING: SERIES A / STRATEGIC PARTNERSHIP TRACK

FIG.04 / COMPANY

German R&D. European ambition.

ALVPHA Tech GmbH is a German research and development company headquartered in Germany, focused on agentic AI, multimodal architectures, knowledge representation, embodied intelligence, and energy efficiency.

The company operates as a wholly-owned subsidiary of ALVPHA Holding GmbH (HRB 751537, AG Ulm). The corporate structure and Unternehmensgegenstand are notary-confirmed and optimized for German and EU research funding programs — ZIM, Forschungszulage, EIC Accelerator, and Invest BW.

The ALVPHA trademark is registered with the European Union Intellectual Property Office (EUIPO).

LEGAL ENTITY
ALVPHA Tech GmbH
PARENT
ALVPHA Holding GmbH
HRB 751537 · AG Ulm
TRADEMARK
EUIPO Registered
FOUNDED
2026
FIG.05 / CONTACT

Let's build the embodied future.

We work with robotics OEMs, industrial integrators, research institutions, and funding partners. If you build humanoids — or fund them — let's talk.

→ DIRECT
contact@alvpha.ai
GENERAL INQUIRIES
contact@alvpha.ai
PARTNERSHIPS
partners@alvpha.ai