In this article
Physical24GB, MIG instance memory and renderer eligibility must stay separate.
NVIDIA A30 is assessed here as an exact Ampere board configuration. Its published 24 GB capacity is the starting point for a workload qualification, not a measured serving or rendering result.
Exact model and memory variants
A30 is a 24 GB HBM2 Ampere compute accelerator with a 165 W passive server envelope. It differs from RTX A-series graphics boards despite the shared letterA.
| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| A30 | 24 GB HBM2 | 165 W | Passive; Dual-slot PCIe |
Specifications: Official product/support documentation.
LLM backend and memory budget
A30 supports MIG, but physical24 GB is not the memory of every instance. Enumerate devices in the selected mode and validate engine operations. An isolated result does not replace the full-GPU baseline.
Passing the architecture requirement does not certify every model, quantization kernel or prebuilt package.
Compatibility evidence: vLLM GPU requirements.
Cycles and batch rendering
CUDA inference compatibility does not certify a selected renderer. For Cycles CUDA, separately verify A30 detection and scene completion. Do not assign RTX hardware capabilities to a compute product because its name resembles A-series graphics.
Blender 5.2 LTS GPU rendering manual.
Integration into a server or workstation
Use the supported passive server configuration and retain the actual isolation state. A30 can provide up to four MIG instances, but the assignment must be explicit. The observed instance memory and backend support belong in a different row from the physical24 GB specification.
A concrete qualification scenario
A server operator changes one A30 from full-device service to partitioned workers. Preserve the original model/context control, enumerate the new instances and give each worker a separate memory budget. Do not reuse a full-GPU model-fit decision for the partitions. If offline rendering is investigated, record its actual execution mode and device selection independently rather than assuming the same compute partition supports the graphics path.
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 A30; physical variant and reported device identifier |
| Memory boundary | 24 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 |
A MIG worker must report its assigned memory domain; a physical24 GB specification cannot stand in for that execution-mode observation.
Further reading: RAM and VRAM: which memory matters for an AI server?, A GPU inference benchmark you can trust, NVIDIA and AMD GPU catalogue.