By name: memory-estimation description: "Use when the user wants to estimate GPU memory (VRAM) requirements for a training configuration, check if a model will fit on their GPUs, or plan GPU allocation for training." allowed-tools: ["Bash(${CLAUDE_PLUGIN_ROOT}/scripts/th_estimate.sh:)", "Bash(${CLAUDE_PLUGIN_ROOT}/scripts/th_detect.sh:)"]
GPU Memory Estimation
Estimate GPU VRAM requirements before committing to a training run.
Step 1: Check Environment
"${CLAUDE_PLUGIN_ROOT}/scripts/th_detect.sh"
If library=missing, tell the user to install training_hub first via the setup-guide skill.
Step 2: Run Estimation
Execute the estimation script with user-provided parameters or config defaults:
"${CLAUDE_PLUGIN_ROOT}/scripts/th_estimate.sh" $ARGUMENTS
Step 3: Present Results
Parse the JSON output and present clearly:
- Memory estimates — Show low/mid/high VRAM estimates in GB
- GPU fit — Report whether the configuration fits on the available GPU(s)
- Recommendations — If memory is tight, suggest:
- Reduce
max_seq_len(e.g., 4096 -> 2048) - Reduce
effective_batch_size - Switch to LoRA or QLoRA for lower memory
- Add more GPUs for data parallelism
- Reduce
Estimation Methods
| 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.