An on-premise LLM needs backup and disaster recovery coverage across three distinct layers: the model artifacts and configuration, the data feeding retrieval-augmented generation, and the infrastructure itself, since losing any one of them can take the system down even if the others are intact. Model weights and their exact configuration, quantization settings and fine-tuning checkpoints should be backed up to separate storage, ideally in a second physical location or a different building, since these can be large files that are easy to overlook until they are needed for a rebuild. The vector database and document indexes behind retrieval-augmented generation need their own backup schedule, since rebuilding an embedding index from scratch for a large document set can take significant time during an actual outage. For infrastructure, a disaster recovery plan should specify a recovery time objective, how quickly the system must be back online, and whether that requires a warm standby GPU server ready to take over or an acceptable window to reprovision hardware from scratch, since GPU procurement lead times make cold-start recovery slower than for typical IT systems. Regular recovery drills, not just backup jobs, confirm the plan actually works. Nanobase AI builds backup and disaster recovery planning into on-premise LLM architecture from the start rather than treating it as a later addition.
Three layers, three separate failure modes
An on-premise LLM can fail in three distinct ways, and a backup plan built for only one of them will leave the system down when a different layer fails. Model artifacts and configuration, the vector database and document indexes behind retrieval-augmented generation, and the physical infrastructure itself each need their own backup schedule and recovery plan, since losing any single one can take the whole system offline even if the other two are perfectly intact.
Recovery targets by layer
Each of the three layers has its own realistic recovery time and its own backup approach, and treating them identically is where most disaster recovery plans fall short.
| Layer | What can be lost | Recovery time objective consideration | Backup approach |
|---|---|---|---|
| Model artifacts | Weights, quantization config, fine-tuning checkpoints | Fast, once storage is accessible, since files just need to be reloaded | Backup to separate storage, ideally a second physical location |
| RAG vector database | Embedding index, document metadata | Slow to rebuild from scratch for large document sets | Regular index snapshots, not just source document backups |
| Infrastructure | GPU servers, networking, power | Depends on warm standby vs. cold-start reprovisioning | Warm standby hardware, or an accepted reprovisioning window |
Why the vector database needs its own explicit plan
It is easy to assume that backing up the source documents feeding a retrieval-augmented generation system is sufficient, but rebuilding an embedding index from scratch for a large document set can take significant time, hours or more depending on volume, which is exactly the wrong moment to discover that gap during an actual outage. A dedicated backup and recovery schedule for the vector database itself, separate from and in addition to source document backups, is what actually keeps recovery time reasonable.
Setting a realistic recovery time objective for infrastructure
GPU procurement lead times make cold-start infrastructure recovery meaningfully slower than for typical IT systems, so the disaster recovery plan needs to state explicitly whether the organization is maintaining a warm standby GPU server ready to take over immediately, or accepting a longer reprovisioning window as a deliberate trade-off against the cost of idle standby hardware.
- Back up model weights, quantization settings and fine-tuning checkpoints to a separate physical location on a regular schedule.
- Snapshot the vector database and document index on its own schedule, independent of source document backups.
- Decide explicitly between warm standby GPU hardware and an accepted cold-start reprovisioning window, documenting the choice and its recovery time impact.
- Define a recovery point objective for each layer, how much data loss is acceptable, not just a recovery time objective.
- Run a full recovery drill at least annually, actually restoring from backups rather than only verifying backup jobs completed.
Drills matter more than the backup schedule itself
A backup job that runs successfully every night proves nothing about whether the system can actually be restored when it matters; only a real recovery drill does that, and many organizations discover gaps, missing configuration, an outdated runbook, an assumption about hardware availability that no longer holds, only during an actual incident because they never tested the full recovery path beforehand. Scheduling a periodic, full drill that restores from backup rather than just checking that the backup files exist is the single highest-value step most on-premise LLM disaster recovery plans skip.
Frequently asked questions
How often should model weights be backed up?
Model weights change only when a new version is deployed, so backups should be triggered by each new version rather than on a fixed calendar schedule, ensuring every deployed version has a corresponding backup.
Can the vector database be rebuilt instead of backed up?
It can be rebuilt from source documents if time allows, but this can take significant time for large document sets, so a snapshot-based backup approach is strongly preferable for any deployment with a meaningful recovery time requirement.
What is a reasonable recovery time objective for GPU infrastructure?
This depends on whether warm standby hardware is maintained; with it, recovery can happen in minutes to hours, while cold-start reprovisioning without standby hardware can take days to weeks given GPU procurement lead times.
Should disaster recovery plans account for a partial outage, not just total loss?
Yes, partial failures, one layer down while others remain intact, are actually more common than total loss, and a good plan addresses graceful degradation, such as falling back to a smaller model or disabling retrieval temporarily, rather than only planning for full recovery.
How Nanobase AI helps
Nanobase AI, an NVIDIA Inception Program member, builds backup and disaster recovery planning into on-premise LLM architecture from the start, covering all three layers with explicit recovery objectives rather than treating it as a later addition. This connects to broader high-availability design for on-premise LLMs and infrastructure orchestration covered in Kubernetes GPU Operator vs Slurm. Check /faq for related operational questions.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.