Running multiple LLMs on your own hardware is a scaling nightmare
Handling a lone model instance is straightforward, but the real trouble begins when you have to coordinate a whole set of different models—each with its own memory demands and scaling requirements. With a single instance, you can manually assign VRAM and tweak environment variables, and it all works out. As your needs expand, though, you're suddenly dealing with GPU fragmentation, manual request routing, and the constant headache of keeping things reproducible across multiple nodes.
Why model resource footprints vary widely
The core problem is that models vary widely in their resource footprints. A compact 7B parameter model might sit comfortably on one consumer GPU, but once you add larger models or those with extensive context windows, memory usage becomes erratic. Trying to run these together often leaves you with plenty of total VRAM spread across a cluster, yet it's fragmented in a way that stops any single large model from loading—or you hit "out of memory" errors when one model's peak usage spikes and brings down the entire node.
Hand-routing requests adds yet another layer of difficulty. Without a robust orchestration layer, you're stuck building custom logic just to make sure a request for a specific model lands on the right node where that model is already loaded in memory. This manual overhead makes scaling nearly impossible; adding a new node means manual setup, and rolling out a model update across the fleet turns into a tedious, mistake-prone chore.
Why Shaide was open sourced by its creators
That's precisely why the team behind Shaide chose to open source their internal orchestration layer. They saw that the industry lacked a link between raw hardware management and the higher-level demands of AI deployment.
Shaide isn't just another wrapper—it's a K8s-native AI platform built specifically for distributed multi-model inference. By using Kubernetes, Shaide treats AI models as first-class citizens inside the cluster. It hides the underlying hardware complexity, letting users deploy and scale multiple models without micromanaging GPU workload placement. It takes on the heavy lifting of resource allocation, making sure memory requirements are met and models are spread efficiently across the available hardware.
Who benefits from running self-hosted inference
This platform is aimed at those who want to ditch pricey managed cloud services and run serious inference on their own gear. The cost of proprietary managed APIs can climb steeply at scale, and data privacy worries often push enterprises toward self-hosting. Shaide offers the tools to get "cloud-like" ease of use while keeping full control over the hardware.
Built for adaptability, Shaide runs across a range of environments. Whether it's a local RKE2 cluster for on-premises setups or a managed service like EKS or GKE for those who prefer cloud infrastructure without the lock-in of proprietary AI platforms, Shaide integrates smoothly. By offering a standardized orchestration layer, it removes the "scaling nightmare" and lets teams focus on real AI implementation rather than the grind of babysitting GPUs.
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vLLM and TGI configs are a disaster—especially when scaling across multiple models. The real pain isn’t just managing configs; it’s the sheer complexity of coordinating resource allocation, where a single model’s memory demands can fragment a cluster’s VRAM unpredictably. Without a proper orchestration layer, you’re left manually balancing requests across nodes, which quickly becomes error-prone as models grow in size or context window. That’s why tools like Shaide emerged: they simplify this by abstracting away the manual routing and memory management, letting you focus on the workflow rather than the infrastructure.
MIG is the only reliable way to handle a 70GB model without running into fragmented VRAM or unpredictable memory spikes, especially when scaling across multiple GPUs. For example, you might start by allocating a dedicated MIG instance per model to ensure consistent resource allocation—just like you’d manually assign VRAM for a single instance—before considering how to coordinate larger fleets. Which GPUs are you using for your cluster?
Scaling is a mess. Does k8s with custom resource definitions actually fix the memory issues? Handling a lone model instance is straightforward, but the real trouble begins when you have to coordinate a whole set of different models—each with its own memory demands and scaling requirements. With a single instance, you can manually assign VRAM and tweak environment variables, and it all works out. As your needs expand, though, you're suddenly dealing with GPU fragmentation, manual request routing, and the constant headache of keeping things reproducible across multiple nodes. The core problem is that models vary widely in their resource footprints. A compact 7B parameter model might sit comfortably on one consumer GPU, but once you add larger models or those with extensive context windows, memory usage becomes erratic. Trying to run these together often leaves you with plenty of total VRAM spread across a cluster, yet it's fragmented in a way that stops any single large model from loading—or you hit "out of memory" errors when one model's peak usage spikes and brings down the entire node. Hand-routing requests adds yet another layer of difficulty. Without a robust orchestration layer, you're stuck building custom logic just to make sure a request for a specific model lands on the right node where that model is already loaded in memory. This manual overhead makes scaling nearly impossible; adding a new node means manual setup, and rolling out a model update across the fleet turns into a tedious, mistake-prone chore. You could use a system that automatically provisions and manages resources based on the requirements of each model, ensuring that large models have the necessary memory available when needed.