# Radeon PRO V710: 28 GB and server or guest visibility

Radeon PRO V710: official memory and board-power variants, ROCm and Blender Cycles support boundaries, and a practical workload validation scenario.

Author: Retrivium team
Updated: 2026-11-02
Canonical URL: https://retrivium.ai/journal/amd-radeon-pro-v710-llm-rendering/
Language: en

Radeon PRO V710 is an official RDNA 3 server card with 28 GB GDDR6 and a 158 W board specification. Physical board memory must be distinguished from resources assigned to a particular guest system.

## Official variants and the memory boundary

The table follows [AMD materials for this model](https://www.amd.com/en/products/accelerators/radeon-pro/amd-radeon-pro-v710.html). It describes dedicated device memory, not system RAM. A “not verified” entry preserves a source gap; a neighboring configuration cannot supply the missing specification.

| AMD variant | Dedicated memory | Board power |
|---|---|---|
| [28 GB board](https://www.amd.com/en/products/accelerators/radeon-pro/amd-radeon-pro-v710.html) | 28 GB GDDR6 | 158 W |

## Establish the LLM software path

The [ROCm Core SDK 10.1.0 device list](https://rocm.docs.amd.com/en/docs-10.1.0/about/release-notes.html) includes Radeon PRO V710. This is an exact hardware entry, not approval for every operating system, PyTorch build, or quantization format. Select the specific GPU and OS in the [compatibility matrix](https://rocm.docs.amd.com/en/latest/compatibility/compatibility-matrix.html), then record the driver, runtime, and framework release. A listed card does not establish that every kernel used by an inference engine works correctly. Establish device detection, loading of a saved model revision, and a reference request before exploring sustained operation or concurrent requests.

## A practical scenario for this model

The scenario is a model working on the host while a virtual machine reports another capacity or no compute device. Preserve V710 hardware identity and the assigned guest profile first, then inspect the driver inside the guest OS. A card installed in the server cannot establish that a model library sees it through the selected virtualization route. Repeat one fixed request separately in the original and guest profiles, retaining actual placement and releases. If dedicated access is required, document it explicitly instead of promising identical behavior for arbitrary virtual profiles. Nominal 28 GB board capacity is a starting hardware specification; the task available resources need evidence from the environment that actually runs the engine.

## Validate Blender Cycles separately

RDNA 3 meets the HIP hardware floor in the [Blender Cycles 5.2 LTS manual](https://docs.blender.org/manual/en/latest/render/cycles/gpu_rendering.html): RDNA 1 or newer. This renderer requirement is independent of ROCm LLM support; validate the actual driver, build, and scene on the intended machine.

For a virtualized graphics task, complete the frame inside the required guest rather than only on the host. HIP compatibility and device visibility also depend on the selected software route.

## Preserve a machine-level acceptance record

Physical board memory and guest-assigned memory are different quantities. Record the virtualization profile and resources visible to the device; owning the host board does not give every virtual machine all of its memory.

Preserve V710, 28 GB, passive implementation, and host documentation. The server card needs qualified cooling, and the guest-memory profile belongs beside the original board specification.

The acceptance artifact should connect board identity, software versions, model revision, and the workload actually exercised. After any of those changes, the old result describes its original run rather than establishing compatibility of the new configuration.

## Related reading

[GPU containers or virtual machines: what to check before deployment](/journal/gpu-containers-vs-vms/); [RAM and VRAM: which memory matters for an AI server?](/journal/ram-vs-vram/); [NVIDIA and AMD GPU catalogue for LLM inference and server rendering](/journal/nvidia-amd-gpu-catalog/).