In this article
V100S remains32GB PCIe Volta and does not bypass the current vLLM capability floor.
NVIDIA Tesla V100S is assessed here as an exact Volta board configuration. Its published 32 GB capacity is the starting point for a workload qualification, not a measured serving or rendering result.
Exact model and memory variants
V100S is the 32 GB HBM2 PCIe Volta variant rated250 W. Its distinct specification is not an SXM conversion or a new compute-capability generation.
| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| V100S PCIe | 32 GB HBM2 | 250 W | Passive; PCIe server card |
Specifications: Official product/support documentation.
LLM backend and memory budget
CC 7.0 remains below the current vLLM7.5 requirement. Validate the retained CUDA12.x runtime and sm_70 target, weights and context. An improved hardware parameter does not change that software floor.
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
Cycles needs its own validated backend and complete frame. Do not transfer V100S results to another SXM host. The absence of RTX RT cores and build requirements remain regardless of 32 GB capacity.
Blender 5.2 LTS GPU rendering manual.
Integration into a server or workstation
Use the 250 W passive PCIe server installation and retain its airflow conditions. Do not attach V100S results to an inventory row that simply saysV10032 GB: physical interface and exact suffix are needed to interpret driver, host and workload evidence.
A concrete qualification scenario
An operator compares an existing32 GB V100 PCIe host with a V100S system. Reuse one saved model/context control under independently recorded compatible builds. Keep the reference scene and validation rule unchanged. If a newer package rejects both devices, retain that compatibility result without interpreting it as a bandwidth comparison. Only actual measured runs can compare the two systems; their product labels do not supply a benchmark.
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 V100S; physical variant and reported device identifier |
| Memory boundary | 32 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 V100S suffix identifies the tested PCIe product; 32 GB alone cannot distinguish it from other Volta board or module variants.
Further reading: RAM and VRAM: which memory matters for an AI server?, A GPU inference benchmark you can trust, NVIDIA and AMD GPU catalogue.