gemma-4-e2b-nla-L23-av-v0_0_1
Released by Solshine in 2026, gemma-4-e2b-nla-L23-av-v0_0_1 is a 2 billion parameter chat model. gemma-4-e2b-nla-L23-av-v0_0_1 is an open-weights chat model with roughly 2 billion parameters.
by Solshine · 2B parameters
Best for
Ways to use gemma-4-e2b-nla-L23-av-v0_0_1 in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your Solshine API key. osFoundry discovers gemma-4-e2b-nla-L23-av-v0_0_1 automatically — assign it to a Maestro role (router, direct, orchestrator, or fallback) in the Pipeline tab and it is live in every chat. Your key, your provider account — no token markup.
Deploy a dedicated endpoint
gemma-4-e2b-nla-L23-av-v0_0_1 is open-weights — run it locally for free, or deploy a dedicated GPU endpoint in your workspace for reserved capacity with no rate limits.
Use it in a Room App
Room Apps declare AI features in their manifest, then call them with invokeAI:
import { invokeAI } from '@osfoundry/app-sdk'
// 'summarize' is an AI feature declared in your app manifest.
const result = await invokeAI('summarize', userText)
Call it from your own apps
Once a model is wired into your workspace you can host it as an API and reach it from your own services, scripts, or CI — outside osFoundry.
What hardware can run gemma-4-e2b-nla-L23-av-v0_0_1
gemma-4-e2b-nla-L23-av-v0_0_1 runs on a single 16GB consumer GPU (~2 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~5 GB).
gemma-4-e2b-nla-L23-av-v0_0_1 vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about gemma-4-e2b-nla-L23-av-v0_0_1
Is gemma-4-e2b-nla-L23-av-v0_0_1 free to use?
gemma-4-e2b-nla-L23-av-v0_0_1 is free to run locally on your own hardware. Hosted access through osFoundry is metered (input Free (local), output Free (local)). You can switch between local and hosted at any time.
Can I use gemma-4-e2b-nla-L23-av-v0_0_1 commercially?
Commercial use is allowed with conditions. Licence terms not specified — verify the upstream model card before commercial use. Check upstream documentation.
How much VRAM does gemma-4-e2b-nla-L23-av-v0_0_1 need?
Approximately 2 GB at Q4 quantisation, or 5 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run gemma-4-e2b-nla-L23-av-v0_0_1 locally?
Yes. gemma-4-e2b-nla-L23-av-v0_0_1 is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is gemma-4-e2b-nla-L23-av-v0_0_1 best at?
gemma-4-e2b-nla-L23-av-v0_0_1 is well-suited to text generation.
How do I use gemma-4-e2b-nla-L23-av-v0_0_1 in osFoundry?
Paste your Solshine API key in the key dialog (or deploy the open weights for self-hostable models), assign gemma-4-e2b-nla-L23-av-v0_0_1 to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by Solshine on May 12, 2026. Source: https://huggingface.co/Solshine/gemma-4-e2b-nla-L23-av-v0_0_1