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Local Idea Studio local AI benchmark methodology

How Local Idea Studio measures GGUF CPU, CUDA, and Vulkan performance with reproducible hardware, model, context, prompt, and tokens-per-second details.

No fake “fastest” claim

Local AI performance changes dramatically with GPU, CPU, memory speed, model architecture, quantization, context, prompt length, driver, and thermal limits. Local Idea Studio will not call itself the world’s fastest runner without a broad, reproducible comparison.

A benchmark is trustworthy only when another person can repeat it. Results without hardware, model, settings, prompt, and token counts are marketing—not evidence.

Required test record

  • Exact Local Idea Studio and runtime version
  • CPU, GPU, VRAM, system RAM, operating system, and driver
  • Exact GGUF repository, filename, quantization, and checksum
  • Backend and actual GPU layers offloaded
  • Context size, batch size, threads, and flash-attention state
  • Prompt-processing speed, generation speed, generated tokens, and elapsed time
  • Cold load time separately from warm generation

Current verified behavior

SettingExpected behaviorHow to verify
Auto + NVIDIAPrefer CUDA when supportedRuntime info shows cuda
Auto + other supported GPUUse Vulkan when availableRuntime info shows vulkan
CPU onlyNo model layers offloaded to GPURuntime info shows cpu
GPU layers: allRequest maximum GPU offloadLoaded model reports actual offloaded layers

Why results are not posted yet

The public release is new, and one developer computer is not a representative benchmark suite. Measured tables will be added only after the same test can be repeated across CPU-only, NVIDIA CUDA, and Vulkan hardware. Until then, Local Idea Studio displays live tokens per second in the app so users can measure their own exact system.

Run a useful comparison

  1. Use one model file and one fixed prompt.
  2. Start with the same context and generation settings.
  3. Generate at least 128 tokens after a warm-up run.
  4. Repeat three times on CPU, then CUDA or Vulkan.
  5. Report the median—not only the fastest run.
  6. Watch temperature and clock speed to identify throttling.

Share complete results through the GitHub issue tracker so future tables can link to evidence.