Document conversion hardware requirements

README.md

@@ -107,7 +107,17 @@ The `scripts/` folder contains the full conversion pipeline:
 
 ### Reproduce a build
 
-Requirements: ~128 GB RAM, Python 3.12, `litert-torch==0.8.0`
+Requirements: 96 GB minimum system RAM, 128 GB preferred, Python 3.12, `litert-torch==0.8.0`.
+
+Observed multimodal export peak RSS was about 73.9 GiB, so 96 GB is the practical floor once Python, model cache, filesystem cache, and conversion overhead are included. Use 128 GB when running multiple quantization attempts or keeping extra build artifacts.
+
+Ideal Vast.ai image:
+
+- Vast.ai PyTorch image or an Ubuntu-based NVIDIA/PyTorch CUDA image, not a bare CUDA runtime image.
+- Python 3.12 with `uv`/pip, Git, Git LFS, Hugging Face CLI, and build tools available.
+- CUDA/PyTorch wheel support matching the rented GPU architecture; use CUDA 12.8+ PyTorch wheels on Blackwell GPUs.
+- 96+ GB RAM and at least 80 GB free disk; 150+ GB disk is safer for source checkpoint, caches, exported TFLite files, and `.litertlm` bundles.
+- Persistent `/workspace` volume if the instance may be stopped/recycled before upload.
 
 ```bash
 # Clone LiteRT-LM builder (needed by bundle_litertlm.py)
@@ -118,7 +128,7 @@ pip install litert-torch==0.8.0 mediapipe transformers huggingface-hub
 # Download model
 huggingface-cli download google/translategemma-4b-it --local-dir ./translategemma-4b-it
 
-# Convert to TFLite with KV cache (~30-60 min, needs ~128 GB RAM)
+# Convert to TFLite with KV cache (~30-60 min, needs 96 GB minimum RAM)
 python scripts/convert_translategemma_android.py \
   --model-dir ./translategemma-4b-it \
   --tflite-dir ./tflite_output/dynamic_int8 \

scripts/multi_quant_build_upload.py

@@ -196,7 +196,10 @@ def plan_quants(requested_quants, native_ok: bool):
 
 def main():
     ap = argparse.ArgumentParser(
-        description="Run multi-quant conversion+bundle and upload successful artifacts to HF."
+        description=(
+            "Run multi-quant conversion+bundle and upload successful artifacts to HF. "
+            "Requires 96 GB minimum system RAM; 128 GB is preferred for headroom."
+        )
     )
     ap.add_argument("--converter-script", default="/home/ubuntu/convert_translategemma_android.py")
     ap.add_argument("--model-id", default="google/translategemma-4b-it")
@@ -477,4 +480,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    main()