Gravlyn connects AI workloads to distributed GPU infrastructure through deterministic scheduling, transparent scoring, and sovereign execution. No black boxes. No middlemen. Just compute that works.
The Problem
The infrastructure powering AI development is concentrated in a handful of providers, creating bottlenecks that slow research and inflate costs. Meanwhile, significant compute capacity sits idle worldwide.
Major cloud providers control GPU allocation. Researchers and startups compete for limited capacity with unpredictable availability, long queue times, and access that favors the highest bidder.
GPU compute pricing remains high and volatile. Spot instances vanish mid-training. Reserved capacity requires capital commitments most teams cannot justify at early stages of development.
Universities, enterprise data centers, and independent operators hold substantial GPU resources that remain underutilized. There is no efficient mechanism to coordinate this fragmented supply.
Current platforms offer no visibility into how jobs are scheduled, why certain hardware is selected, or what factors determine pricing. Users operate on trust with no means of verification.
The Gravlyn Approach
Gravlyn is a coordination network, not a marketplace. We deterministically match AI workloads to the most suitable GPU infrastructure based on transparent, explainable criteria.
01
Every job-to-GPU match is the result of a scored, reproducible selection process. Hardware fit, cost efficiency, reliability history, and data locality are evaluated explicitly. No opaque auctions.
02
Users can inspect why a particular node was selected. Scoring criteria are visible. This creates accountability across the network and allows informed decisions about compute tradeoffs.
03
Node operators maintain full control of their hardware. Agents self-register, verify capabilities, and execute jobs in isolated environments. No centralized access to operator infrastructure.
04
Every node builds a verifiable track record. Fault isolation, SLA adherence, and historical performance inform future scheduling. Trust is earned through execution, not declared.
System Flow
A streamlined four-stage process from job submission to verified result, designed for reliability and full auditability at every step.
A user submits a compute job with defined requirements: model type, VRAM needs, locality preferences, budget constraints, and SLA tier.
The coordination layer evaluates all eligible nodes against the job profile. Scores are computed deterministically across hardware fit, price, reliability, and locality.
The selected node receives the job in an isolated container. Execution is monitored for faults. Data remains within the specified sovereignty boundary.
Results are returned to the user. Node performance is recorded and feeds back into the reputation system, continuously refining future matching quality.
Technical Differentiation
Gravlyn's architecture is designed to solve specific, measurable problems in distributed compute coordination. These are the technical foundations that distinguish our approach.
matching
Job scheduling uses a multi-dimensional scoring function across hardware compatibility, cost, latency, and reliability. Every decision is reproducible and auditable. No black-box allocation.
hardware
Node capabilities are profiled at registration, including precise VRAM capacity, interconnect bandwidth, and thermal characteristics. Jobs are matched to hardware that meets exact memory and throughput requirements.
isolation
Each job executes in a sandboxed environment. Node-level failures do not cascade. Execution state is checkpointed to enable recovery without restarting entire training runs.
reputation
Nodes accumulate verifiable performance histories. SLA tiers allow users to select reliability guarantees appropriate to their workload. Reputation decay prevents stale trust signals.
sovereignty
Jobs can be constrained to specific geographic regions. This enables GDPR-compliant training, EU data residency requirements, and jurisdiction-aware execution for regulated workloads.
agents
Operators run lightweight agents that self-register, verify hardware specs, accept matched jobs, and report execution metrics. Operators retain full control of their infrastructure at all times.
Why It Matters
Gravlyn addresses structural problems in AI infrastructure that affect researchers, startups, and institutions across Europe and globally.
Access reliable GPU compute without enterprise procurement cycles. Run experiments on hardware matched to your exact model requirements, at costs that reflect actual market supply.
Eliminate the capital barrier to AI development. Train and iterate on models using distributed compute that scales with your needs, without long-term commitments or reserved instance contracts.
Execute AI workloads within EU jurisdiction. Gravlyn's locality-aware scheduling ensures compliance with GDPR and emerging AI regulation without sacrificing access to distributed compute capacity.
Gravlyn contributes to the development of open, sustainable AI infrastructure in Europe. We believe compute coordination should be a shared layer, not a proprietary advantage. Our work aligns with EU innovation frameworks and the broader goal of making AI experimentation accessible to any team with a meaningful problem to solve.
Roadmap
A phased approach focused on correctness before scale. Each phase is designed to validate core assumptions before expanding scope.
Node registration, hardware verification, deterministic matching engine, and basic job execution pipeline. Validating the coordination model with controlled workloads.
Container-based fault isolation, reputation system deployment, SLA tier enforcement, and node incentive mechanisms. Building the trust layer required for production workloads.
Multi-region scheduling, advanced orchestration for distributed training, operator onboarding at scale, and API access for programmatic job management.