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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 required to load Hugging Face model weights (Safetensors or GGUF) for inference without downloading. Answers whether a model fits on a given GPU.
Guides users through setting up LLM training with training_hub: environment detection, installation, GPU checks, and configuration.
Cost estimation scripts and tools for calculating GPU hours, training costs, and inference pricing across Modal, Lambda Labs, and RunPod platforms. Use when estimating ML training costs, comparing platform pricing, calculating GPU hours, budgeting for ML projects, or when user mentions cost estimation, pricing comparison, GPU budgeting, training cost analysis, or inference cost optimization.