In this article
M10 resource accounting is four8GB devices plus one225W physical board.
NVIDIA Tesla M10 is assessed here as an exact Maxwell board configuration. Its published 8 GB capacity is the starting point for a workload qualification, not a measured serving or rendering result.
Exact model and memory variants
M10 places four 8 GB GDDR5 Maxwell GPUs on one 225 W board.32 GB is the board total rather than the memory available to one ordinary device allocation.
| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| M10 board | 8 GB GDDR5 per GPU; 4 GPUs; 32 GB board total | 225 W | Passive; PCIe board; four GPUs |
Specifications: Official product/support documentation.
LLM backend and memory budget
Current vLLM excludes Maxwell through its architecture floor. For a retained compatible stack, qualify each8 GB GPU and actual build target. Virtual-desktop density does not establish one large LLM allocation.
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, validate separate devices and actual scenes on a compatible backend. Four independent frames can duplicate scene data in four domains; that is not one 32 GB budget.
Blender 5.2 LTS GPU rendering manual.
Integration into a server or workstation
Keep the passive server airflow arrangement and 225 W board boundary explicit. Enumerate the four devices after deployment, including whichever isolation mode is used. A virtual profile memory assignment must not be mistaken for the physical8 GB capacity or the 32 GB board total.
A concrete qualification scenario
A virtualization host is repurposed for controlled legacy workers. Correct any inventory row sayingone 32 GB GPU, then run a tiny control artifact on each visible physical device. Keep process mapping and output checks. For rendering, verify independent frame assignments rather than claiming pooled scene memory. If the old software image cannot reproduce the control, record that compatibility limitation instead of deriving eligibility from the original desktop-density product purpose.
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 M10; physical variant and reported device identifier |
| Memory boundary | 8 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 |
Four8 GB workers may load four copies of the same scene; 32 GB board total does not prove shared model or scene residency.
Further reading: RAM and VRAM: which memory matters for an AI server?, A GPU inference benchmark you can trust, NVIDIA and AMD GPU catalogue.