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These GTX cards have 4 GB and do not support a broad LLM serving promise; validate only a specific small task with observed placement.
Why this is one limited-memory group
GTX 1650, GTX 1650 SUPER and GTX 1630 are different Turing GPUs grouped here only by 4 GB capacity and a limited experiment. The 1650 has GDDR5/GDDR6 variants. SUPER lists 100 W board power, while the other table rows list 75 W for the described designs. GTX 16 has no Tensor or RT cores. Memory belongs to one GPU rather than a sum of devices; verify actual AIB implementation separately.
| Official variant | Memory per GPU | Memory type | Board power | Evidence |
|---|---|---|---|---|
| GTX 1650 GDDR5 | 4 GB | GDDR5 | 75 W | Specification |
| GTX 1650 GDDR6 | 4 GB | GDDR6 | 75 W | Specification |
| GTX 1650 SUPER | 4 GB | GDDR6 | 100 W | Specification |
| GTX 1630 | 4 GB | GDDR6 | 75 W | Specification |
Judge a tiny workload by actual placement
General CC 7.5 clears the vLLM hardware minimum but promises neither useful 4 GB serving nor every kernel without Tensor hardware. For llama.cpp CUDA, choose a specific small GGUF with bounded context and output. Preserve GPU layers, CPU/RAM and answer quality. CPU-only or partial offload remains an explicit status. A file smaller than 4 GB does not guarantee room for runtime and KV cache. The experiment should retain its actual settings rather than a broad compatibility label.
Accept a small Cycles scene independently
Blender 5.2 specifies CUDA CC 5.0 and OptiX conditions including driver 575 or newer. A suitable build and actual detection are necessary. No RTX RT cores does not automatically prohibit every OptiX scenario. Preserve a small scene, assets and backend on the selected card, then check image and memory. Host RAM involvement differs from local fit. One project certifies neither another renderer nor an arbitrary texture-heavy scene. Keep the accepted project package available for reproduction.
Preserve baseline and host resources
Display state and background processes matter especially within 4 GB. The worker manifest records them with AIB identity, driver and build. Admission accounts for CPU and RAM during offload. Confirm previous ownership before another job. The 75/100 W specifications describe neither node supply nor universal slot or chassis compatibility. Check connectors and cooling against the actual board. Forced clocks or disabled protections cannot resolve software limitations. Normal operating conditions and preserved observations provide a clearer basis for deciding whether the small task is useful.
Record a limited-use result
The matrix associates exact card, GGUF and scene with observed local GPU, hybrid, CPU-only or unsupported outcomes. Unsupported operations point to build; resource pressure points to workload budget. Failure even in a small scenario cannot become a wider inference pool promise. The decision describes existing hardware for specified files and an environment release. It assigns no TPS, returns, maximum model fit or equality across GTX devices sharing 4 GB. Preserve both passing and failing cases so the practical boundary remains clear.
llama.cpp GPU layers: verify partial model offloading · Display-attached GPUs: the VRAM baseline before model loading · GPU catalog