From training-hub
Estimates GPU VRAM requirements for training configurations, checks model fit on GPUs, and suggests memory optimization. Use when planning GPU allocation.
How this skill is triggered — by the user, by Claude, or both
Slash command
/training-hub:memory-estimationThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Estimate GPU VRAM requirements before committing to a training run.
Estimate GPU VRAM requirements before committing to a training run.
"${CLAUDE_PLUGIN_ROOT}/scripts/th_detect.sh"
If library=missing, tell the user to install training_hub first via the setup-guide skill.
Execute the estimation script with user-provided parameters or config defaults:
"${CLAUDE_PLUGIN_ROOT}/scripts/th_estimate.sh" $ARGUMENTS
Parse the JSON output and present clearly:
max_seq_len (e.g., 4096 -> 2048)effective_batch_size| Method | For | Estimator |
|---|---|---|
basic | SFT, GRPO | BasicEstimator |
osft | OSFT | OSFTEstimator |
lora | LoRA-SFT, LoRA-GRPO | LoRAEstimator |
qlora | Quantized LoRA | QLoRAEstimator |
If no method is specified, the script infers it from the configured algorithm.
npx claudepluginhub red-hat-ai-innovation-team/training_hub --plugin training-hubEstimates VRAM/memory requirements for Hugging Face models (Safetensors or GGUF) without downloading weights. Useful for checking if a model fits on a GPU or instance.
Estimates VRAM/memory required to load Safetensors or GGUF model weights from Hugging Face Hub for inference, including optional KV cache calculation.
Guides users through setting up LLM training with training_hub: environment detection, installation, GPU checks, and configuration.