In this article
Qualify L40S workload transitions and any concurrency explicitly, within its passive server installation.
NVIDIA L40S is assessed here as an exact Ada Lovelace board configuration. Its published 48 GB capacity is the starting point for a workload qualification, not a measured serving or rendering result.
Exact model and memory variants
L40S is a 48 GB GDDR6 ECC Ada server accelerator with a 350 W passive-board envelope. Graphics and inference are separate workloads even when the hardware supports both.
| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| L40S | 48 GB GDDR6 ECC | 350 W | Passive; Full-height dual-slot PCIe |
Specifications: Official product/support documentation.
LLM backend and memory budget
For48 GB Ada, fix weights, quantization and context. Loading one model does not guarantee simultaneous renderer residency. Validate the selected package and actual working buffers before combining tasks.
Passing the architecture requirement does not certify every model, quantization kernel or prebuilt package.
Compatibility evidence: vLLM GPU requirements.
Cycles and batch rendering
L40S RTX capabilities matter only to a supported backend. For Cycles, validate scene, denoising and device selection. A marketing benchmark does not replace a complete local job qualification.
Blender 5.2 LTS GPU rendering manual.
Integration into a server or workstation
The passive350 W board requires a supported server airflow arrangement. Record whether compute and rendering are sequential, partitioned or concurrent. A concurrent experiment needs its own memory and output evidence; two successful isolated runs do not establish safe co-residency.
A concrete qualification scenario
A render server is being evaluated for off-hours LLM work. Run the reference scene to completion, release its worker, then launch the saved inference artifact. Preserve the before-load memory baseline at the workload handoff. Test simultaneous operation only as a separate explicitly configured case. If residual allocations remain, retain process and device observations rather than assuming the nominal48 GB is entirely available to the second application.
The evidence to retain
Keep an ordinary qualification record with the actual test outputs.
Identify the tested variant, software build and execution mode before entering observations. Leave unavailable measurements explicitly unknown.
The fields below separate published specifications from observed allocation and job completion, so a later operator can repeat the same case.
| Evidence field | What this profile must retain |
|---|---|
| Hardware identity | NVIDIA L40S; physical variant and reported device identifier |
| Memory boundary | 48 GB published; observed free memory before and after loading |
| Inference artifact | Exact weights digest, quantization, context and offload configuration |
| Render artifact | Scene digest, renderer version, backend and denoising setting |
| System envelope | Driver, container/build digest, enclosure and cooling configuration |
| Completion evidence | Application exit status, output validity and observed memory failures |
Stopping the render worker must be observed at the process and memory boundary before another workload assumes the full48 GB budget.
Further reading: RAM and VRAM: which memory matters for an AI server?, A GPU inference benchmark you can trust, NVIDIA and AMD GPU catalogue.