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The RTX 2080 Ti has 11 GB per GPU; do not round it up to 12 GB or assume two cards automatically pool memory.
Turing with an 11 GB capacity
The RTX 2080 Ti has 11 GB GDDR6. NVIDIA’s comparison lists 260 W for its reference/FE design; that describes the listed implementation rather than a universal limit for every partner board. Capacity is per GPU. Do not round the less familiar value up to a 12 GB scheduler class. Record AIB identity, supply documentation and available VRAM baseline. The RTX 2080 and RTX 2080 SUPER are other models. Reusing a Ti requires exact information so a modern workload is not assigned resources that the actual device does not possess.
| Official variant | Memory per GPU | Memory type | Board power | Evidence |
|---|---|---|---|---|
| NVIDIA GeForce RTX 2080 Ti — 11 GB NVIDIA comparison reference/FE design | 11 GB | GDDR6 | 260 W | Specification |
Verify the Turing inference path
NVIDIA lists RTX 2080 Ti CC 7.5, matching the current vLLM hardware minimum. This promises neither every dtype nor attention backend or quantization kernel. Do not copy newer architecture settings without checking them. Preserve engine release, checkpoint, tokenizer and a bounded request on the Ti. After loading, measure memory and execution at selected context. Free VRAM with an unsupported operation can indicate a software issue. With llama.cpp CUDA, record offload separately: a successful hybrid run proves neither full residency nor compatibility with another serving engine. Keep the accepted path explicit.
Cycles has independent requirements
Blender Cycles documents CUDA/OptiX and RTX hardware tracing use. Run a packaged scene on the Turing Ti with known assets and settings. Check selected device, image and memory. System RAM involvement does not expand local 11 GB capacity. Another renderer needs its own capability list. One passing scene promises neither every material nor future projects work. When changing backend, preserve both configurations and results so preparation or image quality differences do not disappear behind one application duration. The saved project remains the reproducible unit of comparison.
Give scheduling actual capacity
Keep nominal 11 GB separate from observed available memory in the registry. Admission uses a tested manifest rather than assigning a rounded 12 GB class. Multiple Ti cards also do not automatically form one checkpoint memory pool. Establish a supported application distribution method first if one is needed. Observe CPU/RAM during offload, driver and remaining processes. Node supply concerns the whole system, while source 260 W concerns a particular board design. Normal diagnosis requires neither changed clocks nor disabled protections. Preserve evidence of instability rather than tuning several conditions simply to obtain success status.
Check rounding and rejection causes
The artifact contains inventory, worker classification rule and LLM and scene results. Verify that an unvalidated budget cannot pass admission solely through rounding. If assignment is correct, distinguish memory pressure from unsupported operations. A scene missing an asset concerns its file package rather than Ti capacity. The result defines a bounded RTX 2080 Ti 11 GB envelope. It promises neither automatic fit nor known TPS or pooled VRAM, and meeting vLLM’s hardware floor is not certification for the entire modern inference stack. Keep each accepted scenario and remaining limitation with the record.
VRAM for LLMs: why model size is only part of the calculation · Multiple GPUs in a pool: one service or one large model? · GPU catalog