Intelligence, Distilled.
Intelligence infrastructure, forged in Blackwell-class systems. Sensitive workloads on our reference lab, elastic scale in the cloud — unified by a single rigorous pipeline.
About PureTensor
We approach AI from first principles of computation. PureTensor builds a durable substrate for intelligence, from Blackwell-class bare metal to cloud-scale delivery — portable, reproducible, and built to last. Standardized stacks, unified data planes, and consistent pipelines ensure that what we build is not ephemeral experimentation, but a stable foundation for mission-critical intelligence.
Capabilities
PureTensor is AI, engineered as infrastructure. Cloud-positive yet self-contained by design, we fine-tune and operate models with evaluation rigor and cost efficiency, always on standardized, reproducible systems.
- Model engineering (instruction/LoRA), multimodal pipelines, safety layers.
- Data systems to S3/Blob/GCS; versioned datasets and lineage.
- MLOps with automated evals, canary deploys, rollback playbooks.
Reference Lab
- Multiple RTX PRO 6000 Blackwell workstation GPUs.
- Per-dev VRAM quotas via SR-IOV for parallel R&D.
- Artifacts portable to cloud for elastic training & delivery.
Tensor // Core
PureTensor's flagship Blackwell-class GPU system, engineered as the compute foundation of our AI stack. Built on multiple RTX PRO 6000 Blackwell Workstation Edition GPUs with hundreds of gigabytes of unified VRAM, partitioned via SR-IOV, Tensor // Core multiplexes developers and workloads in parallel.
- Blackwell Architecture— multiple RTX PRO 6000 workstation GPUs
- Partitioned Compute— per-developer VRAM quotas enable parallel R&D
- Fleet Mentality— a class of systems, not a single node
- Portable Artifacts— distilled locally, deployed elastically to the cloud
- Hybrid Integration— aligned with Ark // Nexus for unified compute + data fabric
Infrastructure at a Glance
- GPU Memory— hundreds of gigabytes of unified GPU memory across multiple RTX PRO 6000 Blackwell workstations
- System Memory— terabytes of ECC system memory
- Storage— petascale erasure-coded distributed storage
- Compute Fabric— 200GbE linking inference, orchestration, and storage tiers
- Spine Switch— 400G with sub-microsecond switching latency
Owned infrastructure. Full operational control. Complete data sovereignty.
Ark // Nexus
The decentralized data plane bridging cloud object stores with PureTensor's Blackwell-class reference lab and edge inference. It enforces scheduled replication windows, versioned datasets, and low-latency inference paths for sensitive workloads — the backbone of PureTensor's distributed intelligence.
- Cloud object store alignment (S3/Blob/GCS).
- Scheduled replication windows, versioned datasets.
- Low-latency inference paths for sensitive workloads.
Deployment
- Elastic Cloud— scale-out training, managed MLOps, global delivery.
- Hybrid On-Prem— Blackwell inference on sensitive paths, cloud for scale.
- Edge Immediate— distilled, quantized models where milliseconds matter.
Security & Governance
Security is not an afterthought but a design principle. Per-developer isolation, lineage-tracked datasets, and strict minimization of data movement define PureTensor's operational standards.
- Per-dev isolation (fixed VRAM slices) and least-privilege by default.
- Dataset versioning, lineage, and retention policies.
- Minimize data movement: sensitive loops local; derived artifacts promoted to cloud.
The Team
Heimir Helgason — Founder & CTO
Heimir Helgason is the founder and CTO of PureTensor, and the principal architect of Tensor // Core, the flagship Blackwell-class GPU system anchoring PureTensor's compute fleet, engineered for next-generation training, inference, and HPC workloads at scale. He also designed Ark // Nexus, the decentralized data plane that unifies compute, storage, and recovery into a single operational fabric. With a background in distributed kernel engineering and CUDA-level performance tuning, his work focuses on low-latency architectures where bare-metal efficiency converges with cloud-native elasticity. His background spans finance and technology, including algorithmic trading systems where latency discipline and deterministic execution were non-negotiable — principles now embedded in PureTensor's infrastructure design.
Our Development Team
PureTensor operates with a distributed team model, drawing on engineering talent across time zones. Our focus: MLOps, distributed systems, and GPU-accelerated workloads. We scale capacity to match engagement requirements — lean on infrastructure, rigorous on delivery.
Contact
Send a short problem statement. If there's fit, we schedule a technical call.
Or email us directly: ops@puretensor.ai