KaLM-Embedding-Gemma3-12B-2511
Released by tencent in 2025, KaLM-Embedding-Gemma3-12B-2511 is a 12 billion parameter embedding model. KaLM-Embedding-Gemma3-12B-2511 is an open-weights embed model with roughly 12 billion parameters.
by tencent · 12B parameters
Best for
Ways to use KaLM-Embedding-Gemma3-12B-2511 in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your tencent API key. osFoundry discovers KaLM-Embedding-Gemma3-12B-2511 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
KaLM-Embedding-Gemma3-12B-2511 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 KaLM-Embedding-Gemma3-12B-2511
KaLM-Embedding-Gemma3-12B-2511 runs on a single 16GB consumer GPU (~8 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~29 GB).
KaLM-Embedding-Gemma3-12B-2511 vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about KaLM-Embedding-Gemma3-12B-2511
Is KaLM-Embedding-Gemma3-12B-2511 free to use?
KaLM-Embedding-Gemma3-12B-2511 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 KaLM-Embedding-Gemma3-12B-2511 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 KaLM-Embedding-Gemma3-12B-2511 need?
Approximately 8 GB at Q4 quantisation, or 29 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run KaLM-Embedding-Gemma3-12B-2511 locally?
Yes. KaLM-Embedding-Gemma3-12B-2511 is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is KaLM-Embedding-Gemma3-12B-2511 best at?
KaLM-Embedding-Gemma3-12B-2511 is well-suited to sentence similarity.
How do I use KaLM-Embedding-Gemma3-12B-2511 in osFoundry?
Paste your tencent API key in the key dialog (or deploy the open weights for self-hostable models), assign KaLM-Embedding-Gemma3-12B-2511 to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by tencent on November 4, 2025. Source: https://huggingface.co/tencent/KaLM-Embedding-Gemma3-12B-2511