Give Meshia a hard problem. It assembles the agents, runs the experiments, and scales the compute on its own.
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Plans, GPUs, visualizations, and output artifacts stay under one roof, all in your browser.
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Meshia scales a problem from one agent up to swarms of thousands, all sharing memory and staying locked on the same objective.
The orchestrator indexes shared context, audits decisions, and enforces time and resource budgets so thousands of agents behave like one accountable research team.
Meshia binds scattered compute into one workspace that can expand around the run instead of forcing your team to rebuild the environment.
The provisioner optimizes placement around availability, region, and latency to maintain high-bandwidth communication between clusters across providers, turning fragmentation into one workspace.
Meshia scales a problem from one agent up to swarms of thousands, all sharing memory and staying locked on the same objective.
The orchestrator indexes shared context, audits decisions, and enforces time and resource budgets so thousands of agents behave like one accountable research team.
Keep weights, artifacts, and context together, so progress continues across workspaces and teammates.
Meshia meshes compute from 17 providers around the world. Every workspace gets unmatched compute availability and resilience.
Cost-efficient 24 GB workhorse for adapters, embeddings, and cheap parallel experiments.
Budget CUDA branch for small fine-tunes, package checks, and low-cost inference tests.
Many small experiments, smoke tests, and low-cost branches.
Cloud data-center GPU for inference, rendering-adjacent ML, and moderate CUDA training.
16 GB Ada GPU for CUDA development and small model workloads
Cost-effective legacy CUDA option for inference, embedding generation, and smoke tests.
Legacy Pascal-class GPU for robust CUDA training and older compute workloads.
Ultra-low cost legacy GPU for light inference and tiny CUDA validation tests.
Plans cover the Meshia workspace and collaboration layer. Compute, storage, and model usage are metered from real usage so small experiments stay light and serious runs can scale cleanly.
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