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The SUPER has 8 GB while the ordinary RTX 2060 has 6/12 GB variants; the suffix is not a universal capacity ranking.
SUPER is not the family’s capacity maximum
The RTX 2060 SUPER has 8 GB GDDR6 and a 175 W figure for NVIDIA’s comparison design. The ordinary RTX 2060 has 6 GB and 12 GB variants. SUPER therefore cannot automatically rank above every sibling in capacity. The table describes one GPU. Preserve exact name, AIB identity and specification source. Available memory after environment startup differs from nominal 8 GB. A catalog collapsing every 2060 into one hardware class loses important LLM and scene information before performance measurement or queue selection even begins. Keep identity and capacity as explicit independent fields.
| Official variant | Memory per GPU | Memory type | Board power | Evidence |
|---|---|---|---|---|
| NVIDIA GeForce RTX 2060 SUPER — 8 GB NVIDIA comparison reference/FE design | 8 GB | GDDR6 | 175 W | Specification |
Confirm the Turing inference configuration
The vLLM documentation defines hardware minimums and installation rather than every dtype, quantization and attention path. Preserve checkpoint, tokenizer, engine release and a bounded request on the SUPER. Examine context and concurrency in separate series. A task budget based on an ordinary 12 GB 2060 does not automatically receive permission on an 8 GB SUPER. Observe CPU and RAM alongside the GPU when offloading. llama.cpp CUDA is another backend path: its successful answer certifies neither vLLM nor full local runtime residency without host involvement. Keep the actual software route in the manifest.
Accept the scene independently
Blender Cycles documents CUDA/OptiX and RTX hardware tracing in a supporting backend. Select a packaged project and retain settings for the 2060 SUPER. Check device selection, output file and footprint. Host RAM involvement is not proof of local fit. Another renderer requires its own feature validation. Equal 8 GB on another Turing card does not guarantee equal preparation or rendering duration. A missing scene asset is a file error rather than evidence of insufficient VRAM. Preserve logs and image together so status explains the work actually completed. Acceptance concerns that saved project.
Include full identity in scheduling
Worker classification follows a tested manifest and actual capacity rather than only the number 2060. During inventory updates, verify that normalization preserves SUPER and jobs do not inherit another variant’s budget. Multiple GPUs do not automatically pool memory. Confirm the previous owner’s release, including resident weights, before switching queues. Check AIB supply and cooling separately. Source 175 W describes neither whole host power nor request energy. Normal validation needs no disabled protections or forced clocks: correct assignment and observed resources come first. Preserve the mapping rule with the hardware record for later review.
Preserve identity verification evidence
The artifact contains original GPU inventory, normalization rule, model and scene manifests and the selected worker log. Verify that a 12 GB envelope cannot pass on SUPER through imprecise name matching. If mapping is correct but execution fails, inspect backend and allocations. If only assets differ, correct project packaging. The conclusion concerns selected RTX 2060 SUPER tasks. It promises neither universal fit nor fixed TPS or superiority over every ordinary 2060. Its value is preserving the distinction between name and capacity instead of assuming that any suffix always means more available memory.
Coding LLMs on 8GB VRAM: Start with a Bounded Task · Build a GPU Inventory CSV with nvidia-smi · GPU catalog