Fix: OOM Errors during Quantization

Quick Answer

If you encounter CUDA Out Of Memory (OOM) errors during AWQ or GPTQ quantization despite having sufficient raw VRAM, the issue is typically memory fragmentation or the PyTorch allocator caching. To fix this, set export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True before running your quantization script, and reduce your max_seq_len during the calibration phase.

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The Problem

Quantization (especially AWQ and EXL2) is highly memory-intensive because it loads the FP16 model weights and the calibration dataset simultaneously.

Even if an FP16 8B model requires only 16GB of VRAM, the quantization process can easily spike past 24GB (crashing consumer cards like the RTX 3090/4090) due to memory fragmentation within the PyTorch memory pool.

Common Error Signature

RuntimeError: CUDA error: out of memory
CUDA kernel errors might be asynchronously reported at some other API call...

The Solution

1. Optimize the PyTorch Allocator

PyTorch’s default memory allocator can struggle with the rapid allocations and deallocations during quantization.

Before starting the process, export the following environment variable to allow PyTorch to handle fragmented memory better:

export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True

2. Reduce Calibration Sequence Length

The calibration step calculates activations across a dataset (usually Wikitext or C4). A large seq_len exponentially increases VRAM usage.

If your script allows it, lower the sequence length:

# Instead of seq_len=4096
quantize_config = {
    "zero_point": True,
    "q_group_size": 128,
    "w_bit": 4,
    "version": "GEMM"
}
# Adjust in the run method:
model.quantize(tokenizer, quant_config=quantize_config, max_seq_len=2048) # Lower this!

3. Use CPU Offloading (Fallback)

If you still cannot fit the process in VRAM, use frameworks like AutoAWQ or llama.cpp that support partial CPU offloading.

For llama.cpp (GGUF format), quantization happens entirely on system RAM, which bypasses the VRAM limit entirely. If you only need GGUF, skip AWQ/GPTQ and use llama-quantize.

Technical Notes

• Batch Size: Ensure the calibration batch size is set to 1.
• Docker Environments: If running inside Docker, ensure the container has access to sufficient shared memory (--shm-size=8g) as PyTorch dataloaders may crash if shared memory is exhausted.