RahulSChand/gpu_poor

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1405 stars · 88 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A web-based calculator that estimates GPU memory requirements and inference/finetuning throughput (token/s) for any LLM. It supports quantization schemes like GGML, bitsandbytes, and QLoRA, and inference frameworks such as vLLM, llama.cpp, and HuggingFace.

Use cases

• check if my gpu can run this llm
• estimate vram needed for a language model
• calculate tokens per second for llm inference
• estimate finetuning time for llama model
• which quantization fits on my gpu
• max context length and batch size my gpu can handle
• breakdown of gpu memory usage during llm training

When to choose

• planning hardware purchases or cloud GPU selection for LLM inference or finetuning
• comparing quantization options (GGML, bnb, QLoRA) for memory fit
• estimating token/s and training iteration time before running experiments

When to avoid

• you need exact benchmarked performance numbers rather than estimates
• you need a CLI/API for automated pipelines - it is an interactive web tool
• models or frameworks outside its supported set (vLLM, llama.cpp, HF)

Facets

application · maturity active

gpu-computing llm-inference llm-training machine-learning developer-tools large-language-models gpu-computing machine-learning developer-tools browser gpu-memory-calculator vram-estimator tokens-per-second quantization llama-cpp qlora bitsandbytes vllm huggingface finetuning-estimator web javascript

Member repositories

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markdown · JSON · MCP: product_card(name="RahulSChand/gpu_poor")