# NVIDIA RTX 4000 Ada Generation: 20GB in a full-height single slot

Specifications and qualification steps for NVIDIA RTX 4000 Ada Generation, covering memory variants, LLM backends, Cycles and system integration.

Author: Retrivium team
Updated: 2026-11-02
Canonical URL: https://retrivium.ai/journal/rtx-4000-ada-llm-rendering/
Language: en

NVIDIA RTX 4000 Ada Generation is assessed here as an exact Ada Lovelace 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

The full-height RTX 4000 Ada is a 130 W active single-slot20 GB card. The SFF sibling retains20 GB but has a 70 W low-profile dual-slot body.

| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| NVIDIA RTX 4000 Ada Generation | 20 GB GDDR6 ECC | 130 W | Active; single slot |

Specifications: [Official product/support documentation](https://www.nvidia.com/en-us/products/workstations/rtx-4000/).

## LLM backend and memory budget

For20 GB, define the actual model and KV-cache budget under the chosen concurrency. Matching SFF memory does not establish equal speed, although the same control artifact can test residency.

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

Qualify the full-height RTX board with a complete scene and selected backend. Single-slot placement is useful only with preserved airflow and repeatable neighboring-card conditions.

[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

A full-height single-slot worker can sit beside another expansion card, but that placement belongs in the qualification. Preserve bracket height and 9.5-inch board length in the installation record instead of substituting the shorter SFF dimensions.

## A concrete qualification scenario

An operator has one full-height server position and a small desktop carrying4000 SFF. Use the same model artifact and reference scene to establish compatibility on each system. Record each thermal and power envelope separately. A result that fits20 GB in both systems answers a capacity question; the run measurements remain tied to their own130 W and 70 W boards.

## 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 4000 Ada Generation; 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 |

The 9.5-inch full-height board requires its own installation drawing; the shorter low-profile sibling is not a mechanical template.

Further reading: [Can NVIDIA RTX cards be used for LLM inference?](/journal/rtx-for-inference/), [VRAM for LLMs: why model size is only part of the calculation](/journal/vram-and-models/), [NVIDIA and AMD GPU catalogue](/journal/nvidia-amd-gpu-catalog/).