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Retain Ada as part of the SKU identity even when the previous card also had48GB.
NVIDIA RTX 6000 Ada Generation is assessed here as an exact Ada Lovelace 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
RTX 6000 Ada is a 48 GB GDDR6 ECC300 W board. It must be distinguished from 48 GB Ampere A6000 and 24 GB Quadro RTX 6000 Turing.
| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| NVIDIA RTX 6000 Ada Generation | 48 GB GDDR6 ECC | 300 W | Active; dual slot |
Specifications: Official product/support documentation.
LLM backend and memory budget
Ada is an eligible generation for current vLLM builds, but each quantization backend still requires validation. The 48 GB budget also contains KV cache and working buffers; the model file size alone is insufficient.
Passing the architecture requirement does not certify every model, quantization kernel or prebuilt package.
Compatibility evidence: vLLM GPU requirements.
Cycles and batch rendering
Hardware RT cores apply to supported Cycles OptiX rendering. Against A6000, preserve one scene, the same denoising settings and the same output-validation rule.
Blender 5.2 LTS GPU rendering manual.
Integration into a server or workstation
A replacement in an existing dual-slot tower still needs a documented power connection, fan intake and driver baseline. Keep the physical nameAda in inventory; a short label6000 is insufficient for reproducing a software result.
A concrete qualification scenario
A studio replaces an Ampere A6000 with RTX 6000 Ada while retaining48 GB capacity. Run the previously saved model artifact before changing precision or context. Render the existing reference scene with the same validation settings. Store the old and new device identities as separate records. A successful capacity-preserving replacement does not establish a general speed ratio between generations.
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 6000 Ada Generation; 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 |
The retained A6000 control remains a different hardware baseline; 48 GB in both generations does not make their engine binaries interchangeable.
Further reading: Can NVIDIA RTX cards be used for LLM inference?, VRAM for LLMs: why model size is only part of the calculation, NVIDIA and AMD GPU catalogue.