In this article
P40 capacity is useful only with validated legacy kernels and chassis-directed cooling.
NVIDIA Tesla P40 is assessed here as an exact Pascal board configuration. Its published 24 GB capacity is the starting point for a workload qualification, not a measured serving or rendering result.
Exact model and memory variants
P40 is a 24 GB GDDR5 passive Pascal server card rated250 W. Large memory for its generation does not add Tensor cores, RT cores or modern engine eligibility.
| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| P40 | 24 GB GDDR5 | 250 W | Passive; Full-height PCIe server card |
Specifications: Official product/support documentation.
LLM backend and memory budget
PascalCC6.1 is below current vLLM requirements. For GGUF, validate a retained llama.cpp sm_61 CUDA12.x build and exact quantization kernels.24 GB does not guarantee arbitrary context or complete offload of every model.
The current vLLM requirement is compute capability7.5 or higher; this architecture is below that floor.
Compatibility evidence: llama.cpp CUDA build configuration and the NVIDIA architecture matrix.
Cycles and batch rendering
Validate Cycles CUDA/OptiX separately from inference. Pascal has no RTX RT cores, and GPU OIDN requires higher capability. Reference-scene completion and the chosen denoising path belong in the result.
Blender 5.2 LTS GPU rendering manual.
Integration into a server or workstation
The 250 W passive board depends on directed server airflow and its supported power connection. A desktop chassis with a free PCIe slot does not automatically supply those conditions. Preserve the exact cooling arrangement beside the retained legacy software image.
A concrete qualification scenario
A lab assesses an inherited P40 for a bounded local inference demonstration. Record the physical installation, build a controlled sm_61 artifact and run one fixed model/context control. Render a modest scene afterward with a compatible backend. If a larger model is tried, create another case instead of changing the reference. The useful output is a reproducible24 GB Pascal experiment rather than a claim of present-day package universality.
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 P40; physical variant and reported device identifier |
| Memory boundary | 24 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 |
A24 GB weight file is not a complete24 GB workload budget; leave measured room for cache and runtime on the retained Pascal path.
Further reading: RAM and VRAM: which memory matters for an AI server?, A GPU inference benchmark you can trust, NVIDIA and AMD GPU catalogue.