In this article
GTX980 Ti is a manufacturer-specific6GB Maxwell experiment, with a retained software and cooling baseline.
NVIDIA GeForce GTX 980 Ti is assessed here as an exact Maxwell board configuration. Its published 6 GB capacity is the starting point for a workload qualification, not a measured serving or rendering result.
Exact model and memory variants
GTX 980 Ti is a 6 GB GDDR5 Maxwell desktop GPU. The cited GIGABYTE product specification confirms memory for that manufacturer board; physical cooling and power must match the actual card.
| Variant | Memory | Published board power | Cooling / format |
|---|---|---|---|
| GTX 980 Ti | 6 GB GDDR5 | Not specified | Board-vendor-specific; Desktop PCIe card; inspect actual board |
Specifications: Official product/support documentation.
LLM backend and memory budget
Maxwell fails the modern vLLM floor. For a tiny GGUF, evaluate only a controlled compatible CUDA12.x path and actual quantization kernels.6 GB with a display is not free memory for weights and context.
The current vLLM requirement is compute capability7.5 or higher; this architecture is below that floor.
Compatibility evidence: llama.cpp CUDA build configuration and the NVIDIA architecture matrix.
Cycles and batch rendering
Qualify Blender Cycles by backend, driver and complete scene. Maxwell has no RTX RT cores; GPU OIDN requires higher capability. Card age replaces neither device-health checks nor software qualification.
Blender 5.2 LTS GPU rendering manual.
Integration into a server or workstation
This is a desktop board, not automatically a server-qualified accelerator. Inspect the delivered manufacturer model, cooler intake and power connectors. Unknown board power in this profile stays unknown rather than borrowing a reference value from a different980 Ti implementation.
A concrete qualification scenario
An older home workstation becomes an educational local-model test. Keep normal monitors attached for the first baseline, choose a deliberately tiny quantized artifact and record complete or partial offload. Run a small independent scene only after inference exits. Archive the compatible build before maintenance. The output can establish a narrow6 GB experiment without describing the board as a modern professional serving resource or assuming another vendor cooler behaves identically.
The evidence to retain
Keep an ordinary qualification record with the actual test outputs.
Identify the tested variant, software build and execution mode before entering observations. Leave unavailable measurements explicitly unknown.
The fields below separate published specifications from observed allocation and job completion, so a later operator can repeat the same case.
| Evidence field | What this profile must retain |
|---|---|
| Hardware identity | NVIDIA GeForce GTX 980 Ti; physical variant and reported device identifier |
| Memory boundary | 6 GB published; observed free memory before and after loading |
| Inference artifact | Exact weights digest, quantization, context and offload configuration |
| Render artifact | Scene digest, renderer version, backend and denoising setting |
| System envelope | Driver, container/build digest, enclosure and cooling configuration |
| Completion evidence | Application exit status, output validity and observed memory failures |
The GIGABYTE 6 GB specification confirms that board example; another vendor cooler or a modified used980 Ti needs its own installation evidence.
Further reading: RAM and VRAM: which memory matters for an AI server?, A GPU inference benchmark you can trust, NVIDIA and AMD GPU catalogue.