# NVIDIA Quadro M4000: 8GB in a legacy single slot

Specifications and qualification steps for NVIDIA Quadro M4000, covering memory variants, LLM backends, Cycles and system integration.

Author: Retrivium team
Updated: 2026-11-02
Canonical URL: https://retrivium.ai/journal/quadro-m4000-llm-rendering/
Language: en

NVIDIA Quadro M4000 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

M4000 places8 GB GDDR5 Maxwell in an active full-height single-slot120 W design. Its installation differs from the dual-slot150 W M5000 despite equal capacity.

| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| NVIDIA Quadro M4000 | 8 GB GDDR5 | 120 W | Active; Full-height single slot |

Specifications: [Official product/support documentation](https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/quadro-product-literature/12489_NV_DS_Quadro_M4000_US_NV_FNL_HR.pdf).

## LLM backend and memory budget

Maxwell is excluded by current vLLM. A retained llama.cpp CUDA12.x sm_52 build is a conditional small-model reuse path; verify the exact quantization kernels and do not describe it as current CUDA13 support.

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). The current [vLLM capability floor](https://docs.vllm.ai/en/stable/getting_started/installation/gpu/) excludes this architecture.

## Cycles and batch rendering

Blender5.2 lists the applicable minimum capability for CUDA/OptiX, but this GPU has no RT cores and cannot satisfy the newer GPU OIDN capability floor. Retain a compatible driver and verify complete scene execution.

[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

Single-slot reuse can suit an existing narrow host mechanically, but the active intake must remain clear. Keep the 120 W product boundary and final slot map. The retained CUDA12.x environment belongs beside those hardware conditions.

## A concrete qualification scenario

An administrator compares old M4000 and M5000 workstations for one bounded legacy task. Run the identical small artifact and reference scene using independently recorded compatible builds. Keep per-host results rather than ranking solely from 8 GB capacity. If a narrow enclosure forces a different cooling layout, preserve that difference. The experiment decides whether each existing configuration completes the task, without implying modern package support or an upgrade recommendation.

## 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 Quadro M4000; 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 120 W single-slot record cannot be substituted with the 150 W M5000 installation even when both complete the same 8 GB legacy control.

Further reading: [Build llama.cpp CUDA binaries for multiple GPU architectures](/journal/llama-cpp-portable-cuda-build/), [GPU drivers: a controlled comparison of two versions](/journal/gpu-driver-controlled-ab/), [NVIDIA and AMD GPU catalogue](/journal/nvidia-amd-gpu-catalog/).