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Radeon Vega Frontier Edition has official air-cooled and liquid-cooled versions with 16 GB HBM2.
Radeon Vega Frontier Edition has official air-cooled and liquid-cooled versions with 16 GB HBM2. Their board specifications differ at 300 W and 375 W; physical memory is not HBCC virtual address space.
Official variants and the memory boundary
The table follows AMD materials for this model. 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 |
|---|---|---|
| Air-cooled 16 GB | 16 GB HBM2 | 300 W |
| Liquid-cooled 16 GB | 16 GB HBM2 | 375 W |
Establish the LLM software path
Radeon Vega Frontier Edition is absent from the checked ROCm Core SDK 10.1.0 GPU list. Support for a neighboring model cannot be transferred through a shared generation or a similar name. For a separate experiment, the llama.cpp build documentation describes a Vulkan backend. That route requires a suitable driver, detection of the actual device, and validation of the selected GGUF; Vulkan in a specification does not establish that every operation works. Save tensor-placement logs and compare output with a reference run before concluding that the GPU is actually involved. This is not a claim of current official ROCm support for this card.
A practical scenario for this model
A practical example is inspecting an old card whose description mixes 16 GB with a much larger amount of addressable data. Record physical HBM2 from the specification first, then identify the virtual-addressing mechanism separately. Addressing data beyond local memory does not add the same amount of physical memory chips or guarantee equivalent access behavior. Cooling identity matters for this model as well: the air-cooled board and closed-loop liquid version require different chassis and component-condition checks. The shared Frontier Edition name cannot identify the implementation. Begin an LLM experiment with exact inventory and device detection; historical machine-learning positioning does not establish current library support. The software experiment should remain attached to the actual cooling and board configuration inspected.
Validate Blender Cycles separately
Vega predates the RDNA 1 HIP hardware floor in the Blender Cycles 5.2 LTS manual. It is therefore not a current Cycles HIP target; historical compute support or a ROCm LLM experiment cannot establish renderer eligibility.
Do not convert historical visualization advertising into a promise of current Cycles HIP. Preserve the original engine for an archived project and evaluate it separately from the new experimental LLM profile.
Preserve a machine-level acceptance record
The table gives AMD board-power specifications, not measured whole-computer consumption. Check the power supply, connectors, card dimensions, and airflow path for the actual system separately. Board power cannot establish inference speed.
Attach cooling type, 16 GB HBM2, and the corresponding board power. When the implementation is unknown, leave hardware acceptance incomplete instead of defaulting to 300 W.
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
RAM and VRAM: which memory matters for an AI server?; Idle render farms: assessing a second GPU workload; NVIDIA and AMD GPU catalogue for LLM inference and server rendering.