Making an on-premise LLM highly available means removing every single point of failure between a user's request and a model's response, which typically requires at least two GPU nodes running the same model behind a load balancer, plus redundancy in every supporting service. On the inference side, this means running multiple replicas of the serving engine, such as vLLM or NVIDIA Triton, across separate physical GPU servers, orchestrated by Kubernetes with health checks that automatically reroute traffic away from a failed node. The load balancer or API gateway in front should support request queuing and graceful degradation, so a spike in traffic slows responses rather than dropping requests entirely. Supporting components need the same treatment, since the vector database behind retrieval-augmented generation, the authentication service, and any document store should run in a replicated configuration rather than as single instances, because a private LLM is only as available as its weakest dependency. Network redundancy, dual power feeds and monitoring with automated alerting round out a production-grade setup. Smaller deployments sometimes accept a warm-standby node instead of full active-active redundancy as a lower-cost middle ground. Nanobase AI designs high-availability architectures sized to how critical the deployment is to daily operations.
HA is only as strong as the weakest dependent service
Teams often focus high-availability effort entirely on running multiple replicas of the model-serving layer, then leave the vector database, identity provider, or logging service as a single unprotected instance, which quietly recreates the single point of failure the HA design was meant to eliminate. True high availability requires redundancy at every layer a user request passes through, model serving, retrieval, identity, and the load balancer itself, since a single unprotected dependency anywhere in that chain determines the system's actual uptime, not the layer that got the most attention.
HA components by layer
Uptime is set by the least redundant layer in this table, so a single unprotected row undoes the redundancy work done everywhere else.
| Layer | Single point of failure risk | HA approach |
|---|---|---|
| Model serving | One GPU server goes down, all requests fail | Multiple replicas across separate physical servers behind a load balancer |
| Load balancer | The balancer itself becomes the single point of failure | Redundant load balancer pair (active-passive or active-active) |
| Retrieval / vector database | Index unavailable, RAG-grounded answers fail | Replicated or clustered vector database deployment |
| Identity provider | Login fails company-wide | Redundant IdP deployment or a cloud IdP with its own SLA |
| Storage | Model weights or documents inaccessible | Redundant or networked storage, not single local disk |
Building HA step by step
- Deploy at least two GPU nodes running identical model replicas, physically separate enough that a single power or network failure does not take both down simultaneously.
- Place a load balancer, itself deployed redundantly, in front of the serving replicas with health checks that detect and route around a failed node automatically.
- Deploy the vector database in a replicated or clustered configuration rather than a single instance, matching the specific database's supported HA mode.
- Ensure the identity provider has its own redundancy, or rely on a cloud-hosted IdP's published availability guarantees if that fits the sovereignty requirements of the deployment.
- Test failover explicitly by deliberately taking down one node at a time and confirming the system continues serving requests within an acceptable degraded-capacity window.
- Define and monitor a specific uptime target, since "highly available" without a number is not actionable; many enterprise deployments target something in the range of 99.9 percent availability for internal tools, though the right target depends on how critical the deployment is to daily operations.
Failover has to be tested deliberately and regularly, not just designed on paper, since an untested failover path frequently has a configuration gap that only surfaces during an actual outage.
Capacity planning during failover
A common oversight is sizing each GPU node for exactly the normal expected load, which means that when one node fails, the remaining nodes are immediately overloaded instead of absorbing the extra traffic gracefully. Proper HA capacity planning sizes the surviving nodes to handle full expected load at reduced capacity, typically meaning N+1 redundancy at minimum, one more node than the number required to handle peak load alone.
Frequently asked questions
Does HA require Kubernetes, or can Docker Compose support it?
Meaningful HA generally requires Kubernetes or a similar orchestrator, since Docker Compose on a single server cannot survive that server's failure; Compose can support restart policies for process-level resilience but not node-level failover, which is covered further under Kubernetes vs Docker Compose for on-prem LLM.
How many GPU nodes are needed for basic HA?
A minimum of two nodes provides basic redundancy, but N+1 sizing relative to actual peak load, not just node count, determines whether the system performs acceptably during a failure, so the right number depends on the specific model's throughput per node and expected concurrent usage.
Is geographic redundancy across two datacenters necessary?
For most enterprise on-premise deployments, redundancy within a single well-managed datacenter across separate racks and power circuits is sufficient; geographic redundancy across sites adds significant cost and complexity and is typically reserved for the most business-critical deployments.
How Nanobase AI helps
Nanobase AI designs and implements high-availability on-premise LLM architecture across every layer, model serving, retrieval, identity, and load balancing, with explicit failover testing before go-live rather than an untested design. The team also sizes N+1 capacity correctly so a node failure degrades gracefully instead of causing an outage under load.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.