What GPU is required to run the Qwen/QwQ-32B model from Hugging Face?

Vishnu Subramanian

Founder @JarvisLabs.ai

You'll need an A100 (80GB) for full precision, but can run it on consumer GPUs like RTX A5000 (24GB) with proper quantization.

GPU Memory Requirements by Precision

Best Options for Running QwQ-32B

Qwen's official response confirms you need "~80GB of memory for inference at 16bit. Half that for 8bit, and a quarter that for 4bit." Based on my experience running similar models at Javis Labs, here are your practical options:

1. Cloud GPU Option: Rent an A100-80GB (~$2.5-3.5/hr) for full precision inference
2. Consumer Hardware: With llama.cpp using Q4_K_M quantization, the model "fits on a single A5000 or RTX 3090 (24GB VRAM)" with minimal performance impact
3. Budget Option: There's a heavily quantized IQ2_XXS GGUF version that runs at ~10 tokens/sec on 8GB GPUs, though expect quality degradation

Quick Setup Code

For the fastest implementation with quantization:

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

# 4-bit quantization config
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="bfloat16"
)

model_id = "Qwen/QwQ-32B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    quantization_config=quantization_config,
    device_map="auto"  # Handles multi-GPU setups automatically
)

Key Takeaway

If you're bootstrapping and need to balance performance with cost, your best bet is an A5000 with 4-bit quantization. It offers the best price-to-performance ratio for running this model without significant quality loss. For production deployment, consider A100-40GB with 8-bit quantization.

Need any specific GPU rental options or have questions about optimizing inference for your specific use case?