# NVIDIA RTX A4500: 20GB and bounded context

Specifications and qualification steps for NVIDIA RTX A4500, covering memory variants, LLM backends, Cycles and system integration.

Author: Retrivium team
Updated: 2026-11-02
Canonical URL: https://retrivium.ai/journal/rtx-a4500-llm-rendering/
Language: en

NVIDIA RTX A4500 is assessed here as an exact Ampere board configuration. Its published 20 GB capacity is the starting point for a workload qualification, not a measured serving or rendering result.

## Exact model and memory variants

A4500 is the 20 GB GDDR6 ECC Ampere card with a 200 W active dual-slot body. RTX 4500 Ada has24 GB and a different architecture.

| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| NVIDIA RTX A4500 | 20 GB GDDR6 ECC | 200 W | Active; dual slot |

Specifications: [Official product/support documentation](https://www.nvidia.com/en-us/products/workstations/rtx-a4500/).

## LLM backend and memory budget

The 20 GB versus24 GB distinction cannot be hidden under 4500 shorthand. On A4500, validate model, context and concurrency using observed available memory, separately from engine startup.

Passing the architecture requirement does not certify every model, quantization kernel or prebuilt package.

Compatibility evidence: [vLLM GPU requirements](https://docs.vllm.ai/en/stable/getting_started/installation/gpu/).

## Cycles and batch rendering

For Cycles, test the applicable RTX path with a complete scene. A pair linked through NVLink needs explicit distributed-memory support; detecting two devices does not prove40 GB scene residency.

[Blender 5.2 LTS GPU rendering manual](https://docs.blender.org/manual/en/latest/render/cycles/gpu_rendering.html).

## Integration into a server or workstation

An inventory migration should preserve the leadingA and the exact20 GB capacity. Check the active dual-slot airflow with neighboring cards installed. Treat a later24 GB Ada replacement as a new hardware/software configuration.

## A concrete qualification scenario

An administrator imports workstation records into a scheduler and discovers labels reading4500 only. Reconcile each device before assigning a model artifact. Use a control that has been independently qualified for the 20 GB A4500, then repeat the reference scene. Retain actual device allocations if a paired configuration is evaluated. The scheduler description should represent the observed device boundary rather than the sum40 GB on an invoice.

## 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 A4500; physical variant and reported device identifier |
| Memory boundary | 20 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 |

Two20 GB devices can duplicate the same artifact; an invoice total of 40 GB does not identify application-visible memory organization.

Further reading: [VRAM for LLMs: why model size is only part of the calculation](/journal/vram-and-models/), [Idle render farms: assessing a second GPU workload](/journal/render-farm-idle-gpus/), [NVIDIA and AMD GPU catalogue](/journal/nvidia-amd-gpu-catalog/).