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The RTX 3090 provides 24 GB per GPU; acceptance needs output quality, memory and system condition rather than weight loading alone.
Profile an existing Ampere card fully
The RTX 3090 has 24 GB GDDR6X and reference board power of 350 W. The RTX 3090 Ti also has 24 GB but another power specification; these are different models. This profile covers both LLMs and Cycles, complementing the separate 3090 model selection guide. The table concerns one GPU. For an existing card, record AIB identity, available VRAM and physical placement. Its operating history cannot be reconstructed from its name: establish suitability through current observations rather than age or general comments about the family. Actual installation conditions remain part of the assessment.
| Official variant | Memory per GPU | Memory type | Board power | Evidence |
|---|---|---|---|---|
| NVIDIA GeForce RTX 3090 — 24 GB NVIDIA comparison design | 24 GB | GDDR6X | 350 W | Specification |
Validate one LLM configuration
Match vLLM conditions to the exact checkpoint, dtype and environment release. Ampere clears the hardware floor but does not certify every kernel. Pin tokenizer, context and output limit on the 3090, then retain response correctness, terminal state and memory. Record offload separately if required. llama.cpp CUDA is an independent path whose success does not establish another serving library works. Comparing quantizations requires quality evaluation. Having 24 GB promises neither every model with a similarly sized file fits nor unlimited context growth without KV cache consumption. Preserve the tested workload rather than a broad capability claim.
Accept a server Cycles scene
The Blender manual describes CUDA and OptiX, with RTX hardware acceleration in a suitable configuration. Preserve the 3090 project, assets and quality settings. Check selected GPU, errors and final image. Separate host RAM involvement from local residency. One material failing or a missing backend does not necessarily mean faulty hardware: inspect software requirements first. A passing scene certifies neither every external renderer nor an arbitrary frame duration. Comparison with another GPU needs the same workload package rather than only the same application name. Keep output validation alongside execution status.
Separate hardware state from stack state
Record driver, GPU identity, processes and available device events before testing an existing server. Run a short bounded suite under normal cooling without forced clocks. On error, preserve chronology and distinguish driver failures from application out-of-memory. The 350 W figure concerns a reference board; verify whole node supply and AIB connectors separately. A resident model can occupy memory between requests. Before handing the GPU to rendering, confirm resource release rather than requiring an unrealistic zero VRAM value that includes driver and display use. These observations make later failures easier to classify.
Produce an acceptance record
The artifact contains LLM and scene manifests, versions, original baseline, statuses and memory observations. If only long context fails, inspect KV budget; if only the scene fails, inspect assets and render backend. Repeated system failure needs independent diagnosis using preserved logs. The decision describes specific jobs that pass on this RTX 3090 now. It guarantees neither absence of rare defects nor continuous availability or equality with the RTX 4090. No revenue or mandatory token speed is assigned: such quantities would require separately defined workloads and appropriate measurement boundaries.
Choose a Local LLM for an RTX 3090 24GB · RTX 3090 vs RTX 4090 for LLMs: Compare the Same Workload · GPU catalog