CHEVA LABSCHEVA LABSCHEVA LABS

Personalized for yourHardware

An independent educational hub to help you evaluate, size, and deploy open-weight AI models tailored to your physical hardware, latency budgets, and workflows.

Independent Research Open Weights Reproducible

Your AI stack, at a glance.

Request path
01Promptyour task
02Routerchooses path
03Modeldoes the work
04Checkverifies output
274
Open-Weight Models
11
AI Providers
7
Task Modalities
1
Lab Experiments

Choose Your Deployment Paradigm

From edge-device inference to frontier-scale cloud, pick the architecture that matches your constraints.

Air-Gapped

Small & Local

1B – 8B Parameters

Instant on-device execution with zero network dependency. Ideal for laptops, edge devices, and sensitive codebases.

4 – 16 GB
Deep Reasoning

Large & Local

32B – 70B+ (MoE)

Full-scale offline reasoning without recurring API fees or token throttle limits on dedicated workstations.

24 – 96+ GB
Infinite Scale

Open Cloud

Frontier Scale

Deploy unquantized open weights across high-concurrency cloud clusters and dedicated GPU instances.

Serverless
Cost & Privacy

Hybrid Routing

Local + Cloud

Filter PII locally, auto-delegate heavy reasoning to the cloud. Best of both worlds.

Dynamic

Open-Weight Model Directory

Verified open weights synchronized from 11 providers — DeepSeek, Meta, Qwen, Mistral, NVIDIA, and more. Filter by reasoning, coding, OCR, embeddings, and MoE architectures.

View Full Catalog
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Latest Lab Note

Reproducible experiments, measured results, and the decisions behind them.

LAB-103Ongoing

TamaBench — Small Models, Long Horizons

A lightweight benchmark that puts a small or local model in a persistent virtual-pet sandbox. The agent must plan across three simulated days, use structured tools, manage money and supplies, and recover when delayed consequences go wrong.

Lab Research NoteRead Note

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