In this article
A40 pair qualification needs application-level distribution evidence as well as a supported server layout.
NVIDIA A40 is assessed here as an exact Ampere 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
A40 is a 48 GB GDDR6 ECC Ampere server board with 300 W passive cooling. NVLink connectivity does not make two boards one 96 GB allocation automatically.
| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| A40 | 48 GB GDDR6 ECC | 300 W | Passive; Full-height dual-slot PCIe |
Specifications: Official product/support documentation.
LLM backend and memory budget
For48 GB Ampere, validate engine, precision and KV-cache memory. If cards share a workload, retain the application-supported distribution mode. A bridge alone does not create pooled model memory.
Passing the architecture requirement does not certify every model, quantization kernel or prebuilt package.
Compatibility evidence: vLLM GPU requirements.
Cycles and batch rendering
A40 has graphics capabilities, but Cycles still requires scene and backend validation. Distributed scene memory and independent frame workers are different arrangements and must remain separate in the report.
Blender 5.2 LTS GPU rendering manual.
Integration into a server or workstation
The passive300 W board belongs in a server configuration with verified airflow. Preserve bridge installation and topology only when used by the workload. Keep display, virtualization and bare-metal execution settings identifiable so a configuration change does not inherit an unrelated result.
A concrete qualification scenario
An operator wants to place a larger model across two A40 cards. First establish a single-card control with a saved artifact. Then run the explicitly configured distributed case and retain per-device allocations and process mapping. Independently render a reference scene in its chosen mode. If both GPUs contain duplicate data, record that behavior rather than reporting a fictitious single96 GB device.
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 A40; 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 |
Observed duplication across two A40 workers must remain visible even when an NVLink bridge is installed and both cards report48 GB.
Further reading: RAM and VRAM: which memory matters for an AI server?, A GPU inference benchmark you can trust, NVIDIA and AMD GPU catalogue.