# genpark-memory-efficient-lora-kernel-gradient-optimizer-skill

> genpark-memory-efficient-lora-kernel-gradient-optimizer-skill — alphaparkinc-genpark-memory-efficient-lora-kernel-gradient-optimizer-skill. Use this tool when you need to optimize LoRA kernel gradients while reducing memory usage, ideal for applications where VRAM is limited, such as training large models or running multiple tasks simultaneously. It saves up to 80% of VRAM, making it a valuable asset for efficient computing. Input your model and gradient data to receive optimized kernel gradients as output.

Canonical page: https://skillsregistry.net/skills/alphaparkinc-genpark-memory-efficient-lora-kernel-gradient-optimizer-skill  
JSON: https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-memory-efficient-lora-kernel-gradient-optimizer-skill

## Description

Memory-efficient LoRA kernel gradient optimizer saving 80% VRAM (Unsloth style)

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/alphaparkinc/genpark-memory-efficient-lora-kernel-gradient-optimizer-skill)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "alphaparkinc-genpark-memory-efficient-lora-kernel-gradient-optimizer-skill"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-memory-efficient-lora-kernel-gradient-optimizer-skill` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-memory-efficient-lora-kernel-gradient-optimizer-skill/pull`

---
SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt