# NVIDIA Tesla M6 and P6: LLM inference and server rendering boundaries

Specifications and qualification steps for NVIDIA Tesla M6 and P6, covering memory variants, LLM backends, Cycles and system integration.

Author: Retrivium team
Updated: 2026-11-02
Canonical URL: https://retrivium.ai/journal/nvidia-tesla-m6-and-p6-llm-rendering/
Language: en

NVIDIA Tesla M6 and P6 is assessed here as an exact Maxwell / Pascal board configuration. Its published 8/16 GB capacity is the starting point for a workload qualification, not a measured serving or rendering result.

## Exact model and memory variants

M6 is an 8 GB Maxwell MXM module rated100 W; P6 is a 16 GB Pascal MXM module with 70/90 W settings. Neither is a conventional PCIe add-in card.

| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| M6 MXM | 8 GB GDDR5 | 100 W | Host-integrated cooling; MXM module; not a PCIe add-in card |
| P6 MXM | 16 GB GDDR5 | 70/90 W | Host-integrated cooling; MXM module; not a PCIe add-in card |

Specifications: [Official product/support documentation 1](https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/documents/161207-GRID-Packaging-and-Licensing-Guide.pdf), [Official product/support documentation 2](https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/solutions/resources/documents1/nvidia-p6-datasheet.pdf).

## LLM backend and memory budget

Maxwell and Pascal are below current vLLM requirements. A bounded experiment needs a retained CUDA12.x path matching the architecture. Do not transfer P6 capacity, power setting or build target to 8 GB M6.

The current vLLM requirement is compute capability7.5 or higher; this architecture is below that floor.

Compatibility evidence: [llama.cpp CUDA build configuration](https://github.com/ggml-org/llama.cpp/blob/master/ggml/src/ggml-cuda/CMakeLists.txt) and the [NVIDIA architecture matrix](https://docs.nvidia.com/datacenter/tesla/drivers/cuda-toolkit-driver-and-architecture-matrix.html).

## Cycles and batch rendering

For Cycles, validate a compatible backend and scene on the designed host. Successful MXM integration does not certify rendering. These architectures provide neither RTX RT cores nor automatic GPU OIDN eligibility.

[Blender 5.2 LTS GPU rendering manual](https://docs.blender.org/manual/en/latest/render/cycles/gpu_rendering.html).

## Integration into a server or workstation

MXM blade modules rely on their designed host, heat sink, firmware and power delivery. A module offered separately is not enough to establish a working server configuration. Preserve the exact blade and cooling assembly before evaluating software or model memory.

## A concrete qualification scenario

A lab sees inexpensive-looking MXM modules in an old equipment inventory and wants to reuse them. First identify the supported blade host and installed M6 or P6 capacity. Qualify a tiny architecture-matched inference control only after physical integration is established. Then evaluate a separate renderer reference scene. If no compatible host exists, keep that as an integration gap instead of inventing a PCIe installation procedure.

## 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 M6 and P6; physical variant and reported device identifier |
| Memory boundary | 8/16 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 separate MXM module cannot supply missing host firmware, heat sink or power delivery; those integration dependencies precede software acceptance.

Further reading: [RAM and VRAM: which memory matters for an AI server?](/journal/ram-vs-vram/), [A GPU inference benchmark you can trust](/journal/inference-benchmark-method/), [NVIDIA and AMD GPU catalogue](/journal/nvidia-amd-gpu-catalog/).