Sparse-Llama-3.1-8B-2of4-GGUF
Sparse-Llama-3.1-8B-2of4-GGUF is a 8 billion parameter chat model from QuantFactory, released November 27, 2024. Sparse-Llama-3.1-8B-2of4-GGUF is an open-weights chat model with roughly 8 billion parameters.
by QuantFactory · 8B parameters
Best for
Ways to use Sparse-Llama-3.1-8B-2of4-GGUF in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your QuantFactory API key. osFoundry discovers Sparse-Llama-3.1-8B-2of4-GGUF 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
Sparse-Llama-3.1-8B-2of4-GGUF 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 Sparse-Llama-3.1-8B-2of4-GGUF
Sparse-Llama-3.1-8B-2of4-GGUF 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 (~20 GB).
Sparse-Llama-3.1-8B-2of4-GGUF vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Sparse-Llama-3.1-8B-2of4-GGUF
Is Sparse-Llama-3.1-8B-2of4-GGUF free to use?
Sparse-Llama-3.1-8B-2of4-GGUF 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 Sparse-Llama-3.1-8B-2of4-GGUF 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 Sparse-Llama-3.1-8B-2of4-GGUF need?
Approximately 5 GB at Q4 quantisation, or 20 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run Sparse-Llama-3.1-8B-2of4-GGUF locally?
Yes. Sparse-Llama-3.1-8B-2of4-GGUF is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Sparse-Llama-3.1-8B-2of4-GGUF best at?
Sparse-Llama-3.1-8B-2of4-GGUF is well-suited to text generation.
How do I use Sparse-Llama-3.1-8B-2of4-GGUF in osFoundry?
Paste your QuantFactory API key in the key dialog (or deploy the open weights for self-hostable models), assign Sparse-Llama-3.1-8B-2of4-GGUF to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by QuantFactory on November 27, 2024. Source: https://huggingface.co/QuantFactory/Sparse-Llama-3.1-8B-2of4-GGUF