# NVIDIA Quadro P400, P600, P620, P1000, P2000 and P2200: 2GB to5GB legacy workload boundaries

Specifications and qualification steps for NVIDIA Quadro P400, P600, P620, P1000, P2000 and P2200, covering memory variants, LLM backends, Cycles and system integration.

Author: Retrivium team
Updated: 2026-11-02
Canonical URL: https://retrivium.ai/journal/compact-quadro-pascal-2gb-5gb-llm-rendering/
Language: en

NVIDIA Quadro P400, P600, P620, P1000, P2000 and P2200 is assessed here as an exact Pascal board configuration. Its published 2/2/2/4/5/5 GB capacity is the starting point for a workload qualification, not a measured serving or rendering result.

## Exact model and memory variants

This grouped profile covers six Pascal SKUs: P400/P600/P620 2 GB, P1000 4 GB and P2000/P2200 5 GB. P2200 uses GDDR5X; the other listed memory rows use GDDR5.

| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| NVIDIA Quadro P400 2 GB | 2 GB GDDR5 | 30 W | Active; Low-profile single slot |
| NVIDIA Quadro P600 2 GB | 2 GB GDDR5 | 40 W | Active; Low-profile single slot |
| NVIDIA Quadro P620 2 GB | 2 GB GDDR5 | 40 W | Active; Low-profile single slot |
| NVIDIA Quadro P1000 4 GB | 4 GB GDDR5 | 47 W | Active; Low-profile single slot |
| NVIDIA Quadro P2000 5 GB | 5 GB GDDR5 | 75 W | Active; Full-height single slot |
| NVIDIA Quadro P2200 5 GB | 5 GB GDDR5X | 75 W | Active; Full-height single slot |

Specifications: [NVIDIA product specification 1](https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/quadro-product-literature/302022-DS-NV-Quadro-Pascal-P400-A4-03Feb17-NV-fnl-WEB.pdf), [NVIDIA product specification 2](https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/quadro-product-literature/301995-DS-NV-Quadro-Pascal-P600-US-03Feb17-NV-fnl-WEB.pdf), [NVIDIA product specification 3](https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/quadro-product-literature/quadro-pascal-p620-data-sheet-593981-us-nv-r3-web.pdf), [NVIDIA product specification 4](https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/productspage/quadro/quadro-desktop/quadro-pascal-p1000-data-sheet-us-nv-704475-r1.pdf), [NVIDIA product specification 5](https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/documents/Quadro-P2000-US-03Feb17.pdf), [NVIDIA product specification 6](https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/productspage/quadro/quadro-desktop/quadro-p2200-datasheet-letter-974207-r4-web.pdf).

## LLM backend and memory budget

These PascalCC6.1 models are outside current vLLM requirements. A reproducible llama.cpp sm_61 CUDA12.x build can be evaluated for tiny or partially offloaded artifacts, but2–5 GB capacities must be tested individually and do not establish useful service throughput.

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

Current Cycles architecture eligibility does not guarantee scene fit. No RTX RT cores; GPU OIDN requires a higher capability. Keep scene/driver/backend limits and the power/form-factor table explicit rather than ranking these cards by memory alone.

[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

The table distinguishes low-profile2 GB/4 GB boards from full-height5 GB boards, with their own power fields. Keep each physical card capacity in the inventory. A shared Pascal build does not make all scene or model configurations interchangeable.

## A concrete qualification scenario

A teaching lab inventories several old display workstations. Run one intentionally tiny control artifact using the retained Pascal build, then record whether each variant initializes, offloads and completes. Test a small scene independently and preserve exact failure stages. Do not turn partial CPU/GPU execution into a claim that the entire model fits2 GB. The output is a capacity-and-backend matrix for all six documented SKUs.

## 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 P400, P600, P620, P1000, P2000 and P2200; physical variant and reported device identifier |
| Memory boundary | 2/2/2/4/5/5 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 5 GB P2200 GDDR5X row must remain distinct from 5 GB P2000 GDDR5; shared capacity does not identify the same physical SKU.

Further reading: [Build llama.cpp CUDA binaries for multiple GPU architectures](/journal/llama-cpp-portable-cuda-build/), [Used GPUs: which checks are needed before a pilot?](/journal/used-gpu-health-check/), [NVIDIA and AMD GPU catalogue](/journal/nvidia-amd-gpu-catalog/).