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Scale intelligenceand make breakthroughs
Meshia is a single workspace that autonomously scales and manages massive compute and intelligence towards solving hard problems
Built and used by my teams at
How Meshia works
Describe your research objective in natural language. Meshia interprets the goal, identifies the right models, and plans the work.
Which hidden biomarker explains the responder group across these assay exports, trace exports, and validation folds?
A single control pane
Plans, GPU allocation, live visualizations, and review artifacts stay in one inspectable surface.
Optimized for continuous iteration
Autoscaling compute
Boots the right GPUs for each phase, then winds them down when the work is done. You never manage infrastructure.
Model interplay
Frontier models collaborate, challenge, and reconcile work before results ship. No single point of failure in reasoning.
Data-aware workspace
Files, repos, logs, metrics, and prior outputs stay searchable as run context. The workspace remembers everything.
Review artifacts
Checkpoints, traces, run summaries, and memos are packaged with lineage so the work stays inspectable.
Pricing
- $10 in starter credits
- All GPU types, subject to availability
- 1 active session
- Full agent with 55+ tools
- 100 GB storage
- Up to 10 concurrent sessions*
- Priority GPU allocation
- Persistent volumes with 30-day retention
- Experiment tracking and scheduled runs
- Cross-model consensus
- Agent memory across sessions
- Everything in Pro
- Reserved H100 and H200 capacity
- Unlimited sessions
- SSO and team management
- Custom model deployment
- Data residency controls
Compute availability
Meshia connects to compute around the world and scales with your research and experiments
Extreme single-GPU memory for long-context inference, large verifier models, and frontier-scale experiments.
High-throughput Hopper runs with extra memory for large inference, RL, and model-parallel branches.
Blackwell memory headroom for high-resolution vision, large adapters, and heavy inference batches.
Largest training branches, long-context inference, and dense verifier passes.
High-memory training, retrieval-heavy review, and long document inference.
General training, RL, adapter sweeps, simulation, and batch inference.
Put discovery on autopilot
Direct massive compute towards a single objective
Stop wrangling infrastructure and intelligence. Start running experiments that converge on results.