Warning Num Samples Per Thread Reduced To 32768 Rendering Might Be Slower Upd Access

Modern GPUs process pixels in parallel using thousands of threads. Each thread requires a small amount of high-speed memory (registers) to store data for the path-tracing calculations.

emit configChanged();

int samplesPerThread() const return m_samples;

The warning " Num samples per thread reduced to 32768, rendering might be slower " is a critical notification typically found in or other CUDA/RTX-based rendering logs. It Modern GPUs process pixels in parallel using thousands

Why 32768? That number (2^15) is often a hardcoded safety limit imposed by the render engine or the underlying hardware/API (e.g., CUDA, OptiX, OpenCL). The warning appears when the renderer tries to allocate more than 32768 samples per thread, but something—memory constraints, driver limits, or device capabilities—forces it to that number to 32768.

To resolve this issue and speed up your rendering, you need to reduce the VRAM usage. A. Optimize Textures (The Biggest Memory Saver)

Only use the render elements necessary for compositing. C. Utilize Out-of-Core Rendering It Why 32768

) is often the lower limit the software sets before performance drops off significantly. SketchUp Community Immediate Solutions

Here is everything you need to know about why this happens and how to fix it. Why This Warning Appears

: Reducing these samples forces the GPU to communicate more frequently with the system RAM through the PCIe bus—a much slower process than using internal VRAM. This can lead to render times that are 10–20 times longer than normal. The "Magic" Number : 32,768 ( 2 to the 15th power To resolve this issue and speed up your

If you see the warning and want to maximize performance:

When you hit render, V-Ray divides your image into workloads distributed across thousands of parallel computing cores (CUDA or RTX threads).

: This issue often surfaces mid-way through an animation sequence. While the first few frames might clear out smoothly, incremental cached data, moving geometry, or motion blur calculations gradually accumulate until the graphics memory overflows.

Modern AI denoisers eliminate the need for tens of thousands of samples.