Mixtral-8x7B-Instruct-v0.1continued
Released by AlSamCur123 in 2026, Mixtral-8x7B-Instruct-v0.1continued is a 7 billion parameter chat model. Mixtral-8x7B-Instruct-v0.1continued is an open-weights chat model with roughly 7 billion parameters.
by AlSamCur123 · 7B parameters
Best for
Ways to use Mixtral-8x7B-Instruct-v0.1continued in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your AlSamCur123 API key. osFoundry discovers Mixtral-8x7B-Instruct-v0.1continued 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
Mixtral-8x7B-Instruct-v0.1continued 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 Mixtral-8x7B-Instruct-v0.1continued
Mixtral-8x7B-Instruct-v0.1continued runs on a single 16GB consumer GPU (~5 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~17 GB).
Mixtral-8x7B-Instruct-v0.1continued vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Mixtral-8x7B-Instruct-v0.1continued
Is Mixtral-8x7B-Instruct-v0.1continued free to use?
Mixtral-8x7B-Instruct-v0.1continued 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 Mixtral-8x7B-Instruct-v0.1continued 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 Mixtral-8x7B-Instruct-v0.1continued need?
Approximately 5 GB at Q4 quantisation, or 17 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run Mixtral-8x7B-Instruct-v0.1continued locally?
Yes. Mixtral-8x7B-Instruct-v0.1continued is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Mixtral-8x7B-Instruct-v0.1continued best at?
Mixtral-8x7B-Instruct-v0.1continued is well-suited to text generation.
How do I use Mixtral-8x7B-Instruct-v0.1continued in osFoundry?
Paste your AlSamCur123 API key in the key dialog (or deploy the open weights for self-hostable models), assign Mixtral-8x7B-Instruct-v0.1continued to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by AlSamCur123 on May 5, 2026. Source: https://huggingface.co/AlSamCur123/Mixtral-8x7B-Instruct-v0.1continued