In this article
The RTX 2080 has 8 GB and Turing architecture; free memory does not prove a dtype or kernel is supported by a particular build.
Keep the original name distinct
The original RTX 2080 has 8 GB GDDR6. NVIDIA’s comparison lists its design at 225 W. That concerns the described implementation rather than every AIB board or a complete server. The RTX 2080 SUPER and RTX 2080 Ti are separate models. The table gives memory per GPU. Preserve actual board identity, available baseline and environment versions before testing. Architecture age alone cannot answer whether a specific task works; distinguish hardware/software path limitations from simple capacity pressure caused by selected weights and scene data. The operational record should retain this distinction.
| Official variant | Memory per GPU | Memory type | Board power | Evidence |
|---|---|---|---|---|
| NVIDIA GeForce RTX 2080 — 8 GB NVIDIA comparison reference/FE design | 8 GB | GDDR6 | 225 W | Specification |
Separate dtype failure from OOM
The vLLM requirements include a minimum compute capability that Turing meets. This does not certify every attention backend, dtype and quantization. Preserve settings and the original error on the RTX 2080. If an operation is unsupported despite free memory, reducing context may not address its cause. For out-of-memory, examine weights, KV cache and temporary allocations. Do not copy newer architecture options without checking them. Investigate llama.cpp CUDA independently, retaining build target and actual offload. Successful generation there proves neither another engine behaves equivalently nor full GPU residency for the task.
Check materials and render backend
Cycles CUDA and OptiX document software conditions and RTX hardware tracing. Run a small control project before the intended scene with saved assets on the RTX 2080. If the control passes but a production material fails, inspect renderer features and settings before declaring faulty hardware. For memory pressure, record VRAM/RAM and host involvement. Those causes can occur independently. Success in Cycles does not guarantee every other renderer. Preserve final image and backend logs because process startup alone does not establish that every job phase completed correctly. The control case helps locate the failing layer.
Pin an older worker environment
The server record contains AIB identity, driver, software image, launch options and selected GPU. Change a controlled factor during upgrades and retain the previous configuration for comparison. Check remaining processes: a quiet API can retain resident weights. Source 225 W does not describe complete pipeline energy, while supply and airflow depend on installation. Clock changes and disabled protections cannot establish software compatibility. A bounded minimal reproducible scenario with trustworthy device state is more useful after failure. Record attempts rather than repeating until one happens to pass and discarding the unsuccessful observations.
Classify diagnostic outcomes
Associate LLM and scene manifests with control and intended workload results. Keep unsupported operation, memory pressure, missing asset and system failure in separate columns instead of one failed flag. Preserve versions and resource observations for each outcome. This branch supports assigning a verified RTX 2080 role without confusing software incompatibility with its 8 GB capacity. It promises neither every modern model nor universal fit or constant TPS. Another checkpoint or renderer release requires its own acceptance, even when a previous small example passed on the same device and stack. Keep that boundary alongside the result.
Coding LLMs on 8GB VRAM: Start with a Bounded Task · GPU drivers: a controlled comparison of two versions · GPU catalog