Nvidia GeForce 750 ti A.I. Benchmark Ollama & Stable Diffusion
6The Nvidia GeForce GTX 750 Ti, released in early 2014, is based on the Maxwell architecture and is quite dated compared to modern GPUs for AI workloads. However, it can still handle basic AI-related tasks with some limitations. Below is an overview and expected benchmark performance when running *Ollama* and *Stable Diffusion* on this card:
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*Key Specifications of GTX 750 Ti*
**CUDA Cores**: 640
**VRAM**: 2GB GDDR5 (some models with 4GB exist)
**Architecture**: Maxwell (1st generation)
**Memory Bandwidth**: 86.4 GB/s
**Compute Capability**: 5.0
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*Performance on AI Tasks*
#### *Ollama AI (Text-based AI models)*
**Compatibility**: Ollama is optimized for modern GPUs and leverages CUDA cores for processing. The 750 Ti supports CUDA but will struggle with large models due to its 2GB VRAM.
**Expectations**:
You can run small language models with limited token processing.
Models like LLaMA-2 (7B) might work with optimizations such as 4-bit quantization, but larger models (13B+) will likely exceed the VRAM capacity.
Inference speeds will be slow compared to newer GPUs, but it may suffice for basic experimentation.
#### *Stable Diffusion*
**Requirements**: Stable Diffusion typically needs at least 4GB VRAM, though optimizations can lower this to ~2GB using reduced precision (e.g., float16 or 8-bit quantization).
**Benchmark**:
**Rendering Time**: Expect very slow rendering speeds, with one 512x512 image taking several minutes to generate (depending on model and prompt complexity).
**Optimizations**: Use lightweight versions of Stable Diffusion, such as SD 1.4 or 1.5, and enable xformers or Torch 2.0 for memory efficiency.
**Tips**:
Reduce image resolution and batch size to fit within the 2GB VRAM limit.
Offload parts of the computation to the CPU using tools like `accelerate`.
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*Overall Benchmark Expectations*
1. **Ollama (Text AI)**:
Performance: Very limited; better suited for CPUs if the GPU's VRAM is a bottleneck.
Speed: Slow, but small models can still produce results with patience.
2. **Stable Diffusion (Image Generation)**:
Performance: Possible but extremely limited. Expect long rendering times and heavy reliance on optimizations.
Resolution: Stick to small resolutions (e.g., 256x256 or 512x512).
Models: Use older/lighter Stable Diffusion models for better compatibility.
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*Conclusion*
The GTX 750 Ti is not ideal for AI workloads due to its limited VRAM and dated architecture. While it can handle small-scale experiments in *Ollama* and *Stable Diffusion* with heavy optimizations, the user experience will be slow and constrained. For smoother AI performance, consider upgrading to a newer GPU with at least 6GB of VRAM, such as the GTX 1660, RTX 2060, or higher.