https://github.com/rxdt/ai_deployment_calculator
Test app to simultaneously build frontend harness
https://github.com/rxdt/ai_deployment_calculator
agent-memory agent-orchestration agentic-ai agentic-workflow ai-agents ai-tools automation context-engineering context-management developer-experience developer-productivity harness-ci harness-engineering harness-execution-layer harness-framework loops session-management
Last synced: 18 days ago
JSON representation
Test app to simultaneously build frontend harness
- Host: GitHub
- URL: https://github.com/rxdt/ai_deployment_calculator
- Owner: rxdt
- License: mit
- Created: 2026-06-23T07:06:16.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2026-07-12T19:17:54.000Z (18 days ago)
- Last Synced: 2026-07-12T20:05:57.603Z (18 days ago)
- Topics: agent-memory, agent-orchestration, agentic-ai, agentic-workflow, ai-agents, ai-tools, automation, context-engineering, context-management, developer-experience, developer-productivity, harness-ci, harness-engineering, harness-execution-layer, harness-framework, loops, session-management
- Language: TypeScript
- Homepage:
- Size: 7.3 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Agents: AGENTS.md
Awesome Lists containing this project
README
LoopGate
frontend edition
# This is a sample app to demo LoopGate
### Built using [LoopGate for frontend](https://github.com/rxdt/loopgate_js) and **[LIVE HERE](https://aideploymentcalculator.vercel.app/)**
# [AI Deployment Calculator](https://aideploymentcalculator.vercel.app/)
A static Vite + TypeScript app for estimating GPU VRAM requirements and a
hardware memory tier for AI workloads. Calculations run in the browser; there is
no backend report service.
_[The Python loop harness is here](https://github.com/rxdt/py_ralph_frame)_
## Supports
- text generation, embeddings, encoder-decoder, vision, multimodal
- image diffusion, video, audio, tabular, and custom workloads
- inference, LoRA, QLoRA, and full-training estimates
- known model file size overrides, MoE, sharding, and runtime assumptions
## Calculates
- usable VRAM required
- recommended hardware memory tier
- minimum advertised GPU capacity
- usable target, headroom, rough speed, formula, and assumptions
Core equation:
```txt
Required_GB = (Weights + Working_Memory + Training_State + Runtime_Overhead) * Buffer
```
Estimates vary with model architecture, kernels, quantization, sequence packing,
batching, sharding, offload, framework overhead, and runtime configuration.
Validate against your target stack before buying hardware.
## Key Files
- UI: `frontend/index.html` and `frontend/src/app.ts`
- State: `frontend/src/state.ts`
- Calculation: `frontend/src/calculator-core.ts`, `frontend/src/workload-memory.ts`
- Hardware tiers: `frontend/src/hardware.ts`
- Report assembly: `frontend/src/report.ts`
- Specs: `specs/plan.md`, `specs/frontend.md`
## Requirements
- Node.js `^22.16.0 || >=24.8.0`
- pnpm `>=10` (`pnpm@11.9.0` declared)
## Local
```sh
pnpm install
pnpm setup
pnpm dev
```
## Build
```sh
pnpm build
pnpm preview
```
## Deploy
Build output is static and lives in `frontend/dist`. Root-domain static host
settings:
```txt
pnpm build # Build command
frontend/dist # Publish directory
npx vercel deploy --prod # redeploy vercel
```
If hosting under a subpath, set the Vite base path before building.
## Checks
```sh
pnpm preflight
pnpm gate
pnpm --prefix frontend run test:coverage
pnpm --prefix frontend run test:e2e
```