In this article
V100 needs an exact16/32GB PCIe/SXM2 identity and a retained Volta-compatible software path.
NVIDIA Tesla V100 is assessed here as an exact Volta board configuration. Its published 16/32 GB capacity is the starting point for a workload qualification, not a measured serving or rendering result.
Exact model and memory variants
V100 includes16 GB and 32 GB HBM2 variants in PCIe and SXM2 forms. These are different physical integrations, all based on Volta rather than newer Turing or Ampere.
| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| V100 PCIe 16 GB | 16 GB HBM2 | Not specified | Passive; PCIe server card |
| V100 PCIe 32 GB | 32 GB HBM2 | Not specified | Passive; PCIe server card |
| V100 SXM2 16 GB | 16 GB HBM2 | Not specified | Host-integrated cooling; SXM2 module; matching host required |
| V100 SXM2 32 GB | 32 GB HBM2 | Not specified | Host-integrated cooling; SXM2 module; matching host required |
Specifications: Official product/support documentation 1, Official product/support documentation 2.
LLM backend and memory budget
VoltaCC7.0 is below the current vLLM7.5 floor. Evaluate an explicitly compatible llama.cpp or framework stack with CUDA12.x and a validated sm_70 target. Tensor cores do not remove the current package architecture boundary.
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
For Cycles, separately validate compatible CUDA/OptiX and a complete frame. Volta has no RTX RT cores. A successful compute test or Tensor operation does not establish the selected renderer path.
Blender 5.2 LTS GPU rendering manual.
Integration into a server or workstation
Separate PCIe boards from SXM2 modules before installation. SXM2 requires its matching server baseboard and cooling system. Keep exact memory capacity and physical form in inventory; a 32 GB result does not qualify a 16 GB sibling or another module interface.
A concrete qualification scenario
A lab has a mixed archive of V100 accelerators. Create four inventory rows matching the table and retain the host pairing for each. Build one controlled sm_70 inference environment, then qualify a small common model/context case before exploring larger artifacts. Render a separate reference scene on each supported host. An architecture rejection from a current generic package should remain distinct from memory exhaustion and mechanical incompatibility.
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 V100; physical variant and reported device identifier |
| Memory boundary | 16/32 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 |
A PCIe V100 record does not qualify an SXM2 host; retain16 GB versus32 GB and the actual module interface separately.
Further reading: RAM and VRAM: which memory matters for an AI server?, A GPU inference benchmark you can trust, NVIDIA and AMD GPU catalogue.