In this article
The RTX 2080 SUPER has 8 GB; backend changes and SUPER branding do not replace memory, feature and output validation.
A separate 8 GB Turing model
The RTX 2080 SUPER has 8 GB GDDR6. NVIDIA’s comparison lists 250 W for the design it describes. This is neither whole server power nor a universal value for every AIB board. The ordinary RTX 2080 and RTX 2080 Ti remain separate models. The table concerns one GPU. Record the actual SUPER board, idle VRAM and resident processes. Its name does not establish a particular modern engine’s support, and an 8 GB class does not prove equal outcomes across neighboring cards. This profile validates two software paths on an identified device.
| Official variant | Memory per GPU | Memory type | Board power | Evidence |
|---|---|---|---|---|
| NVIDIA GeForce RTX 2080 SUPER — 8 GB NVIDIA comparison reference/FE design | 8 GB | GDDR6 | 250 W | Specification |
Confirm the LLM engine path
vLLM requirements cover compute capability and installation. Turing clears the hardware minimum while dtypes, quantization and kernels depend on the chosen stack. Preserve checkpoint, tokenizer, context and output limit on the SUPER. A bounded test needs a correct response and normal termination rather than weight loading alone. Observe memory with KV cache and temporary allocations. Host offload is another execution mode. Do not guarantee a small weight file fits without runtime headroom or treat free VRAM as proof that a failing operation is supported. Preserve error classification alongside the configuration.
Compare CUDA and OptiX on one project
Blender Cycles documents both backends and conditions for RTX hardware tracing. Preserve one scene, quality and assets on the RTX 2080 SUPER, then perform bounded runs with explicitly selected backends. Verify the intended GPU actually executes them. Retain images and logs separately so quality changes or unsupported features do not vanish inside a duration comparison. RTX branding certifies no arbitrary renderer. Record host memory use as well: different residency paths no longer represent equal local scene allocation within 8 GB. Accept output behavior before drawing performance conclusions from those runs.
Keep the system unchanged
The server record contains driver, Blender release, image and AIB identity. Do not update the entire stack or scene assets while comparing CUDA with OptiX, or differences become unattributable. Check for another VRAM owner before work. Use actual board documentation for supply and cooling; source 250 W does not replace whole system measurement. Observe shared RAM and storage independently. Normal experiments require neither disabled protections nor forced clocks. If the card is unstable, preserve original errors and chronology instead of continuing endless retries. A reproducible failure is more useful than an unexplained passing attempt.
Preserve the rendering mode decision
The package contains LLM acceptance and two render records using one project, with selected backend, status, memory and images. If one path cannot detect the GPU, inspect release and driver. If only footprint differs, examine allocations; if output differs, inspect features and quality. The decision describes a chosen RTX 2080 SUPER mode for this project. It promises neither that OptiX always beats CUDA nor FPS, TPS or every external engine feature. Another scene or software release still requires its own bounded check. Keep those limits with the successful acceptance result.
Coding LLMs on 8GB VRAM: Start with a Bounded Task · Install vLLM: Check GPU, Driver and Package Compatibility · GPU catalog