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T4 passes the architecture floor, while16GB residency, precision and passive cooling remain separate checks.
NVIDIA Tesla T4 is assessed here as an exact Turing board configuration. Its published 16 GB capacity is the starting point for a workload qualification, not a measured serving or rendering result.
Exact model and memory variants
T4 is a 16 GB GDDR6 Turing passive low-profile single-slot server accelerator rated70 W. Unlike older Pascal Tesla products, itsCC7.5 meets the current vLLM hardware floor.
| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| T4 | 16 GB GDDR6 | 70 W | Passive; Low-profile single-slot PCIe server card |
Specifications: Official product/support documentation.
LLM backend and memory budget
MeetingCC7.5 starts qualification rather than certifying every model. For16 GB, retain supported precision, exact kernels, weights and context. Do not transfer newer-generation requirements from theTensor Core name alone.
Passing the architecture requirement does not certify every model, quantization kernel or prebuilt package.
Compatibility evidence: vLLM GPU requirements.
Cycles and batch rendering
Turing includes RT capabilities, but Cycles needs validated OptiX or CUDA and a complete scene.16 GB does not guarantee simultaneous scene and inference-model residency.
Blender 5.2 LTS GPU rendering manual.
Integration into a server or workstation
The 70 W passive board still requires chassis-directed airflow. Retain the low-profile single-slot installation and power configuration. If virtualization is used, qualify its exact software and visible memory independently from the full physical GPU result.
A concrete qualification scenario
A small server is evaluated for a single T4 inference worker. Run one fixed-context model using a package that actually supports its precision path. Then release the model and execute a reference scene. Preserve free-memory observations at the workload transition. A package rejecting a particular numerical format is not proof that T4 fails the overall architecture floor; the evidence should identify the unsupported operation or memory boundary.
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 Tesla T4; physical variant and reported device identifier |
| Memory boundary | 16 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 |
An unsupported precision kernel on T4 is narrower evidence than failure of itsCC7.5 architecture eligibility; keep the actual operation identifiable.
Further reading: RAM and VRAM: which memory matters for an AI server?, A GPU inference benchmark you can trust, NVIDIA and AMD GPU catalogue.