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The RTX 2070 SUPER needs a bounded 8 GB workload; waiting in a queue is not automatically GPU execution time.
SUPER remains an 8 GB device
The RTX 2070 SUPER has 8 GB GDDR6 and a 215 W figure for NVIDIA’s comparison design. The ordinary RTX 2070 is a separate model; equal capacity does not make power and compute specifications identical. The table concerns one GPU. Record AIB identity, driver and baseline VRAM for an existing SUPER. Available memory differs from nominal capacity with displays and resident applications. Server assessment should select a workload and explain its behavior rather than deriving inference speed from SUPER branding or one specified power figure. Preserve actual task conditions with device identity.
| Official variant | Memory per GPU | Memory type | Board power | Evidence |
|---|---|---|---|---|
| NVIDIA GeForce RTX 2070 SUPER — 8 GB NVIDIA comparison reference/FE design | 8 GB | GDDR6 | 215 W | Specification |
Give the request clear timing boundaries
Match vLLM conditions to checkpoint, dtype and kernels. Turing meets the hardware minimum rather than every software path. Choose bounded prompt, context, output and initial concurrency on the RTX 2070 SUPER. Record acceptance, processing start and normal completion. If only waiting increases, inspect queueing and admission instead of declaring slower GPU computation. If memory grows, examine KV cache and runtime structures. llama.cpp CUDA needs another manifest with offload observations. A hybrid success proves neither full residency within 8 GB nor equivalence to another serving engine. Keep the timing definitions explicit.
Rendering has its own waiting phase
Test Blender Cycles CUDA or OptiX with a saved scene and appropriate driver. On the SUPER, record queue entry, project loading, GPU phase and completed image. Submission-to-result time is not pure rendering when the worker was occupied. RTX hardware tracing depends on backend support and certifies no arbitrary renderer. Report host RAM when local memory is insufficient. Preserve scene and assets together, or external file reading and missing textures can hide inside an overall delay number without explaining which service phase changed. The result should remain interpretable after the original run.
Bound the shared worker queue
The manifest contains accepted workload classes, maximum backlog and GPU selection rules. A resident engine does not leave an automatic slot for another process merely because no HTTP response is currently active. Confirm ownership before switching to rendering. Observe RAM, CPU and storage, especially with offloading. The 215 W figure is neither all component power nor request energy. Check supply and cooling for the AIB installation. Preserve original events after instability without disabling protections, and bound the experiment beforehand so diagnosis cannot create unlimited additional queued work. Admission behavior is part of the operating envelope.
Preserve phase timing evidence
The package contains task manifests and timestamps using consistent boundaries. If complete delay rises while the GPU phase stays stable, inspect admission and preparation. If longer contexts fail, examine memory budget; if only rendering fails, examine assets and backend. The decision describes an RTX 2070 SUPER server role in that mode. It establishes neither a general SLA nor constant TPS or speed relative to the ordinary RTX 2070. Another concurrency level requires separate validation because one passing request and completed frame cannot describe queue behavior under arbitrary task arrivals. Keep limits visible with the result.
Coding LLMs on 8GB VRAM: Start with a Bounded Task · A GPU inference benchmark you can trust · GPU catalog