In this article
M60 is two8GB GPUs, with active240W and passive300W board editions kept distinct.
NVIDIA Tesla M60 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
M60 contains two independent8 GB GDDR5 GPUs per board, 16 GB in total. The passive300 W and active240 W editions have different cooling and power envelopes.
| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| M60 passive board | 8 GB GDDR5 per GPU; 2 GPUs; 16 GB board total | 300 W | Passive; Dual-slot PCIe board; two GPUs |
| M60 active board | 8 GB GDDR5 per GPU; 2 GPUs; 16 GB board total | 240 W | Active; Dual-slot PCIe board; two GPUs |
Specifications: Official product/support documentation.
LLM backend and memory budget
One GPU starts with 8 GB, not16 GB. Maxwell fails current vLLM requirements. Validate a tiny artifact on a retained CUDA12.x path and map workers to the two physical devices.
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 devices and a small scene with a compatible backend. Two frame workers do not imply one 16 GB scene memory domain. There are no RTX RT cores; choose denoising by actual compatibility.
Blender 5.2 LTS GPU rendering manual.
Integration into a server or workstation
Identify active versus passive hardware before installation; the fan arrangement changes the host requirements. The vGPU lifecycle documentation is separate from bare-metal CUDA qualification. Keep the board-level300 W/240 W field distinct from per-device observations.
A concrete qualification scenario
A used M60 is advertised as 16 GB. Rewrite the inventory as two 8 GB devices under one physical board, then inspect its cooling edition. Run a tiny control on each GPU using the retained legacy environment. Assign separate reference frames if rendering is evaluated. A larger single-device model cannot be justified from the board total. The useful evidence includes the exact edition, device mapping and independent output validation.
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 M60; 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 |
The active240 W and passive300 W M60 variants still expose two 8 GB domains; cooling edition does not merge those domains.
Further reading: RAM and VRAM: which memory matters for an AI server?, A GPU inference benchmark you can trust, NVIDIA and AMD GPU catalogue.