In this article
A4GB qualification must describe the tested artifact and actual offload precisely.
NVIDIA RTX A400 is assessed here as an exact Ampere board configuration. Its published 4 GB capacity is the starting point for a workload qualification, not a measured serving or rendering result.
Exact model and memory variants
RTX A400 is a 4 GB GDDR6 Ampere desktop board with 50 W active low-profile single-slot cooling. Tensor and RT hardware do not enlarge its memory budget.
| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| NVIDIA RTX A400 | 4 GB GDDR6 | 50 W | Active; low-profile single slot |
Specifications: Official product/support documentation.
LLM backend and memory budget
Start with a small quantized GGUF artifact and short context; measure full offload and remaining VRAM. Ampere passes the architectural vLLM floor, but4 GB can be impractical for package/runtime/model overhead.
Passing the architecture requirement does not certify every model, quantization kernel or prebuilt package.
Compatibility evidence: vLLM GPU requirements.
Cycles and batch rendering
RTX/Cycles candidate for small scenes with measured denoiser overhead; the compact cooler and board layout require a separate sustained-load test.
Blender 5.2 LTS GPU rendering manual.
Integration into a server or workstation
Identify the actual desktopA400 and its full-length PCIe Gen4 x8 interface. Low-profile single-slot fit is only the mechanical stage. Normal display use can consume a material portion of the 4 GB budget before either application starts.
A concrete qualification scenario
A maintenance workstation is available for an educational local-inference test. Choose a tiny artifact deliberately, preserve the display baseline and record partial offload if used. Stop inference before opening the small reference scene. If the runtime fails before weights load, retain that stage instead of guessing a model-size limit. The useful result may be an explicitly narrow experiment, not admission as a general worker.
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 RTX A400; physical variant and reported device identifier |
| Memory boundary | 4 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 successful tiny4 GB control is not evidence that a full inference runtime or denoised production scene has sufficient remaining memory.
Further reading: Build llama.cpp CUDA binaries for multiple GPU architectures, Display-attached GPUs: the VRAM baseline before model loading, NVIDIA and AMD GPU catalogue.