{"slug": "running-multiple-llms-on-your-own-hardware-is-a-scaling-nightmare", "title": "Running multiple LLMs on your own hardware is a scaling nightmare", "summary": "Shaide, a Kubernetes-native AI platform for distributed multi-model inference, has been released under the Apache 2.0 license. It uses vLLM for inference, llm-d for orchestration, KV-cache-aware scheduling, and an internal OCI registry for model weights, enabling fully air-gapped deployment. The platform aims to simplify scaling multiple LLMs on private infrastructure, offering an OpenAI-compatible API and Infrastructure as Code management.", "body_md": "# Running multiple LLMs on your own hardware is a scaling nightmare\n\nInstead of just being another wrapper, Shaide is a K8s-native AI platform designed specifically for distributed multi-model inference. It’s built for people who want to move away from expensive managed cloud services and run heavy-duty inference on their own infrastructure, whether that is a local RKE2 cluster or a managed service like EKS or GKE.\n\n## The technical stack under the hood\n\nThe architecture isn't trying to reinvent the wheel; it's about gluing the right specialized tools together into a cohesive AI workflow. Here is the current breakdown of the stack:\n\n**Inference Engine:** Uses vLLM to handle the heavy lifting of model execution.**Orchestration:** Leverages`llm-d`\n\nfor multi-instance management.**Scheduling:** Implements KV-cache-aware scheduling to optimize how resources are allocated.**Storage:** Utilizes an internal OCI registry to manage both container images and model weights.**Interface:** Provides a fully OpenAI-compatible API so you don't have to rewrite your application logic.\n\n## Deployment and air-gapped capabilities\n\nOne of the biggest selling points here is the \"Infrastructure as Code\" approach. You aren't just running a bunch of loose scripts; the entire platform is managed as a single unit of infrastructure.\n\nIf you are working in a high-security environment, the setup is particularly interesting. It features an interactive installer that runs via Docker against an existing Kubernetes cluster. Because it handles its own model weight distribution through the OCI registry, the entire platform can operate in a fully air-gapped environment. This means zero cluster egress is required once the initial setup is complete, which is a massive win for enterprise-grade privacy.\n\n## Why this matters for local LLM deployment\n\nMost people struggle with the transition from \"I can run Llama-3 on my desktop\" to \"I can run a production-grade cluster of diverse models.\" The complexity of scaling replicas independently while managing GPU memory becomes a full-time job. By using a Kubernetes-native approach, Shaide treats LLMs as standard workloads that can be scheduled, monitored, and scaled using the same patterns we use for web microservices.\n\nThe project is released under the Apache 2.0 license. It is still in the early stages, so expect the API and orchestration logic to evolve as the community starts pushing it through real-world stress tests. If you are currently building a private AI stack and hitting a wall with manual scaling, this is definitely worth a deep dive.\n\n[Qwen 3.8 27B actually beats the larger 3.7 Plus in coding 16d ago](/en/news/6489/)\n\n[Since the provided source content is extremely minimal (\"4 hours 21d ago](/en/news/5819/)\n\n[Production AI Infrastructure 28d ago](/en/news/4926/)\n\n[Open-Weight Models Now Match Proprietary Titans 7/30/2026](/en/news/4392/)\n\n[Next AI-generated starvation videos are getting way too realistic for →](/en/news/8480/)", "url": "https://wpnews.pro/news/running-multiple-llms-on-your-own-hardware-is-a-scaling-nightmare", "canonical_source": "https://promptcube3.com/en/news/8482/", "published_at": "2026-09-01 16:13:18+00:00", "updated_at": "2026-09-01 16:24:41.894740+00:00", "lang": "en", "topics": ["ai-infrastructure", "ai-tools", "mlops"], "entities": ["Shaide", "vLLM", "llm-d", "OCI", "Apache 2.0", "Kubernetes", "RKE2", "EKS"], "alternates": {"html": "https://wpnews.pro/news/running-multiple-llms-on-your-own-hardware-is-a-scaling-nightmare", "markdown": "https://wpnews.pro/news/running-multiple-llms-on-your-own-hardware-is-a-scaling-nightmare.md", "text": "https://wpnews.pro/news/running-multiple-llms-on-your-own-hardware-is-a-scaling-nightmare.txt", "jsonld": "https://wpnews.pro/news/running-multiple-llms-on-your-own-hardware-is-a-scaling-nightmare.jsonld"}}