ZAYA1-74B-preview
Built by Zyphra, ZAYA1-74B-preview is a 74 billion parameter chat model. ZAYA1-74B-preview is an open-weights chat model with roughly 74 billion parameters.
by Zyphra · 74B parameters
Best for
- complex multi-step reasoning
- agent orchestration with tool use
- long-document analysis and summarisation
Ways to use ZAYA1-74B-preview in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your Zyphra API key. osFoundry discovers ZAYA1-74B-preview 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
ZAYA1-74B-preview 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 ZAYA1-74B-preview
ZAYA1-74B-preview runs on a single A100 80GB or H100 80GB at Q4 quantisation (~45 GB VRAM with KV-cache headroom). Full-precision inference requires multiple H100/H200 GPUs at FP16 (~178 GB).
ZAYA1-74B-preview vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about ZAYA1-74B-preview
Is ZAYA1-74B-preview free to use?
ZAYA1-74B-preview 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 ZAYA1-74B-preview 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 ZAYA1-74B-preview need?
Approximately 45 GB at Q4 quantisation, or 178 GB at full FP16 precision. Fits on a single A100/H100 80GB.
Can I run ZAYA1-74B-preview locally?
Yes. ZAYA1-74B-preview is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is ZAYA1-74B-preview best at?
ZAYA1-74B-preview is well-suited to complex multi-step reasoning, agent orchestration with tool use, long-document analysis and summarisation.
How do I use ZAYA1-74B-preview in osFoundry?
Paste your Zyphra API key in the key dialog (or deploy the open weights for self-hostable models), assign ZAYA1-74B-preview to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by Zyphra on May 5, 2026. Source: https://huggingface.co/Zyphra/ZAYA1-74B-preview