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Describe a mixed fleet as groups with separate limitations. Different GPUs can be discussed as independent resources rather than one large graphics card.
Company fleets often grow gradually: several workstation generations, a post-project server and separately purchased GPUs. This need not delay an inference discussion until everything is standardized. But “we have many different cards” should become a usable structure.
Inventory should first produce suitable resource groups and questions to check. You can then discuss part of the fleet without upgrading everything simultaneously.
Group by meaningful constraints
Record each GPU’s model, VRAM, host and machine role. Add driver version and available operating mode. Identically named cards at different sites may belong to different operational groups.
Use a table with “group, count, memory, environment, availability.” Do not replace it with total video memory. That sum does not show which model fits an individual node or which resources a specific request receives.
Separate shared service from shared execution
Independent nodes can serve their own model copies or different workloads. Distributing one model over several GPUs is a separate technical decision with compatibility and communication requirements.
The vLLM parallelism documentation describes scaling approaches. It does not imply an arbitrary set of remote cards can automatically become one fast resource. Comparing independent groups is often simpler for a first pilot before evaluating a complex design.
Do not standardize drivers blindly
Updating every machine for matching versions can affect existing applications. Check the selected stack’s requirements and save working-machine configurations first.
The NVIDIA CUDA guide shows compatibility has conditions and limitations. Decisions must concern specific versions and devices. Some groups may need separate environments or different workloads; discover that before mass deployment.
Pilot a representative of each group
Imagine eight cards divided into three homogeneous groups. Instead of connecting everything, choose one node from each. Test suitable models, recovery and constraints, then compare support costs.
One group may suit further operation, another need improvements and a third remain outside the pool. Partial success is useful: available resources can be used without requiring identical cards across the company.
Compare support effort separately. Two configurations may deliver useful performance but need different updates and recovery procedures. This is not an argument against heterogeneity; it helps choose the first segment and justify changes honestly.
Assessment notes
If the full specification is incomplete, provide known models and machine counts. Understanding limits matters more initially than a perfect register.
Next agree on check order and expansion criteria. Read about corporate inventory and multiple GPUs. A clearly segmented fleet makes useful roles for its suitable portion easier to find.