RahulSChand/gpu_poor

• highreadme#1

  Reposition README H1 and opening paragraph to clarify its estimation/prediction role

  原因：

  当前

  # Can my GPU run this LLM? & at what token/s?
  
  Calculates how much **GPU memory you need** and how much **token/s you can get** for any LLM & GPU/CPU.

  复制粘贴的修复

  复制

  # LLM GPU Poor: Predict & Estimate LLM Performance and Memory
  
  This tool accurately *predicts* and *estimates* the **GPU memory requirements** and **token/s throughput** for any Large Language Model (LLM) on your specific GPU/CPU hardware. It helps you determine if your GPU can run an LLM and what performance to expect, rather than being a runtime or optimization library.

  待办完成忽略
• highlicense#2

  Add a standard open-source license file

  原因：

  复制粘贴的修复

  复制

  Add a `LICENSE` file to the repository root containing the text of the MIT License.

  待办完成忽略
• mediumtopics#3

  Add more specific topics related to LLM performance estimation

  原因：

  当前

  ggml, gpu, huggingface, language-model, llama, llama2, llamacpp, llm, pytorch, quantization

  复制粘贴的修复

  复制

  ggml, gpu, huggingface, language-model, llama, llama2, llamacpp, llm, pytorch, quantization, llm-performance, gpu-memory-estimation, performance-prediction, llm-calculator

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  How to estimate GPU memory and inference speed for large language models?

  你：未被推荐
  AI 推荐顺序：

  1. Hugging Face Transformers (huggingface/transformers)
  2. DeepSpeed (microsoft/DeepSpeed)
  3. NVIDIA Nsight Systems
  4. PyTorch (pytorch/pytorch)
  5. NVIDIA TensorRT
  6. ONNX Runtime (microsoft/onnxruntime)
  7. DeepSpeed-Inference (microsoft/DeepSpeed)
  8. vLLM (vllm-project/vllm)
  9. Hugging Face Optimum (huggingface/optimum)
  10. bitsandbytes (TimDettmers/bitsandbytes)
  11. AWQ (mit-han-lab/awq)
  12. GPTQ (IST-DASLab/gptq)
  13. cProfile
  14. FlashAttention (Dao-AILab/flash-attention)
  15. xFormers (facebookresearch/xformers)

  AI 推荐了 15 个替代方案，却始终没点名 RahulSChand/gpu_poor。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Tool to predict LLM performance with different quantization methods on my hardware?

  你：未被推荐
  AI 推荐顺序：

  1. Hugging Face Optimum (huggingface/optimum)
  2. ONNX Runtime (microsoft/onnxruntime)
  3. NVIDIA TensorRT (NVIDIA/TensorRT)
  4. MLPerf Inference Benchmarks (mlcommons/inference)
  5. DeepSpeed (microsoft/DeepSpeed)
  6. Megatron-LM (NVIDIA/Megatron-LM)
  7. PyTorch Profiler
  8. TensorFlow Profiler
  9. NVIDIA Nsight Systems
  10. Nsight Compute
  11. Intel Neural Compressor (intel/neural-compressor)

  AI 推荐了 11 个替代方案，却始终没点名 RahulSChand/gpu_poor。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  warn

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of RahulSChand/gpu_poor?

  pass
  ✓AI 明确点名了 RahulSChand/gpu_poor

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts RahulSChand/gpu_poor in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 RahulSChand/gpu_poor

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo RahulSChand/gpu_poor solve, and who is the primary audience?

  pass
  ✓AI 明确点名了 RahulSChand/gpu_poor

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？