In this article
The RTX 2060 12 GB remains Turing; extra capacity does not automatically add kernels, dtypes or renderer features.
Two ordinary RTX 2060 capacities
NVIDIA lists ordinary RTX 2060 variants with 6 GB and 12 GB GDDR6, at 160 W and 185 W respectively for the described design. Capacity is per GPU; verify the AIB implementation separately. The 8 GB RTX 2060 SUPER is not a substitute. Both ordinary variants remain Turing. More memory must not be interpreted as a different architecture. Preserve full name, capacity, actual board and available baseline before applying another card’s manifest or assessing a large checkpoint. Nominal GB and supported software features are different questions.
| Official variant | Memory per GPU | Memory type | Board power | Evidence |
|---|---|---|---|---|
| NVIDIA GeForce RTX 2060 — 6 GB | 6 GB | GDDR6 | 160 W | Specification |
| NVIDIA GeForce RTX 2060 — 12 GB | 12 GB | GDDR6 | 185 W | Specification |
Check features separately from capacity
The vLLM hardware floor includes a CC that Turing meets, not a guarantee for every quantization or attention implementation. Preserve engine release, dtype, checkpoint and selected kernels on the 2060. If a configuration fails to fit in 6 GB but launches in 12 GB, that is capacity evidence rather than proof of new compute features. If an operation fails on both with free memory, inspect the software path. llama.cpp CUDA needs its own build/offload manifest. Hybrid CPU execution proves neither full local residency nor another engine’s support merely because sufficient GB appear available. Record the actual execution route.
Repeat the same Cycles scene
Blender Cycles describes CUDA/OptiX and RTX hardware tracing through a supporting backend. Preserve one project, assets and quality across the 2060 variants. Check selected device, image and host memory involvement separately. If 6 GB uses system RAM while 12 GB keeps the scene local, those are different modes rather than equivalent success. Another renderer and its materials require independent validation. Do not change geometry and textures together with backend selection, or extra capacity becomes a convenient but unsupported explanation for mismatched runs. Keep output verification and execution conditions associated with each result.
Do not inherit a server budget blindly
The registry distinguishes capacity variants and retains AIB documentation. Check actual GPU mapping and background processes when moving a task. Resident weights can occupy the card even with no new requests, leaving insufficient room for a scene. Account for RAM and CPU alongside the GPU when offloading. The 160/185 W values do not represent complete node consumption; verify supply, connectors and cooling for the installation. Run normal tests without changed clocks or disabled protections. After driver or image upgrades, old acceptance is not an unchanged guarantee: perform bounded checks of key scenarios in the new environment.
Preserve the capability distinction
The matrix contains both GPU records, matched manifests and separate compatibility, memory fit, offload and output validation fields. If only capacity constrains work, refine the envelope. If an operation fails, investigate backend independently of GB. Missing scene assets require fixing input packaging. This result assesses ordinary RTX 2060 6/12 GB without borrowing newer architecture properties. It promises neither constant TPS nor arbitrary file fit or superiority over SUPER. Preserved fields show which parts of the job were examined and which capabilities remain unknown for the chosen stack. Keep those uncertainties visible beside successful examples.
Coding LLMs on 12GB VRAM: Budget Context and Headroom · llama.cpp GPU layers: verify partial model offloading · GPU catalog