When to Use AWQ vs GPTQ

JPEG and WebP both compress images. JPEG is universal but older. WebP offers better compression but less support. The right choice depends on your constraints.

AWQ and GPTQ both quantize LLMs to INT4. They use different algorithms with different tradeoffs. Understanding when each excels helps you choose correctly.

The Core Difference

class QuantizationApproaches:
    gptq = {
        "full_name": "GPTQ (Generative Pre-trained Transformer Quantization)",
        "approach": "Layer-by-layer quantization with error reconstruction",
        "calibration": "Processes entire calibration dataset per layer",
        "strengths": [
            "Mature, well-tested",
            "Often slightly better quality",
            "Good for custom calibration",
        ],
        "weaknesses": [
            "Slow to quantize (hours)",
            "Memory-intensive quantization",
            "Sequential layer processing",
        ],
    }

    awq = {
        "full_name": "AWQ (Activation-aware Weight Quantization)",
        "approach": "Identify important weights via activation patterns",
        "calibration": "Analyzes activation distributions",
        "strengths": [
            "Fast quantization (minutes)",
            "Lower memory during quantization",
            "Good quality preservation",
        ],
        "weaknesses": [
            "Newer, less battle-tested",
            "Fixed algorithm less flexible",
        ],
    }

Quantization Speed

def quantization_time_comparison(model_size_b: float) -> dict:
    """
    Approximate quantization time on single GPU
    """
    return {
        "model_size": f"{model_size_b}B parameters",
        "gptq": {
            "7b": "30-60 minutes",
            "13b": "1-2 hours",
            "70b": "4-8 hours",
            "notes": "Scales with model size × calibration size",
        },
        "awq": {
            "7b": "5-15 minutes",
            "13b": "15-30 minutes",
            "70b": "1-2 hours",
            "notes": "Faster due to activation-based approach",
        },
    }

Quality Comparison

def quality_comparison():
    """
    Typical quality results (vary by model and task)
    """
    return {
        "general_observation": """
        Both achieve similar quality in most cases.
        GPTQ has slight edge in some benchmarks.
        AWQ has slight edge in others.
        Difference is usually < 1%.
        """,

        "perplexity_typical": {
            "baseline_fp16": "5.00",
            "gptq_int4": "5.08 (+1.6%)",
            "awq_int4": "5.10 (+2.0%)",
            "difference": "Within noise for most applications",
        },

        "task_accuracy_typical": {
            "baseline": "82%",
            "gptq": "80-81%",
            "awq": "80-81%",
            "note": "Task-dependent, benchmark for your use case",
        },

        "recommendation": """
        Don't assume one is better. Benchmark both on your:
        1. Specific model
        2. Specific task
        3. Specific calibration data
        """,
    }

Decision Framework

def choose_quantization_method(context: dict) -> str:
    # Need fast iteration?
    if context.get("experimenting"):
        return "AWQ - faster quantization cycle"

    # Custom calibration critical?
    if context.get("domain_specific_calibration"):
        return "GPTQ - more control over calibration"

    # Memory constrained during quantization?
    if context.get("limited_gpu_for_quantization"):
        return "AWQ - lower memory requirement"

    # Using pre-quantized model?
    if context.get("using_existing_quantized"):
        return "Use what's available and well-tested"

    # Production deployment?
    if context.get("production"):
        return "Benchmark both, pick winner for your workload"

    return "AWQ - good default, faster to try"

Practical Usage

class QuantizationExamples:
    awq_example = """
    # Using transformers + autoawq
    from awq import AutoAWQForCausalLM

    model = AutoAWQForCausalLM.from_pretrained("model_name")
    tokenizer = AutoTokenizer.from_pretrained("model_name")

    # Quantize
    model.quantize(
        tokenizer,
        quant_config={"w_bit": 4, "q_group_size": 128}
    )

    # Save
    model.save_quantized("model_name-awq")
    """

    gptq_example = """
    # Using auto-gptq
    from auto_gptq import AutoGPTQForCausalLM

    model = AutoGPTQForCausalLM.from_pretrained("model_name")

    # Quantize with calibration data
    model.quantize(
        examples,  # Calibration dataset
        batch_size=1,
        use_triton=True,
    )

    # Save
    model.save_quantized("model_name-gptq")
    """

    vllm_usage = """
    # vLLM supports both seamlessly
    # AWQ model
    vllm serve model_name-awq --quantization awq

    # GPTQ model
    vllm serve model_name-gptq --quantization gptq
    """

Inference Speed

def inference_speed_comparison():
    """
    Both achieve similar inference speeds
    """
    return {
        "observation": """
        Inference speed is nearly identical.
        Both use INT4 weights → same memory bandwidth.
        Both use similar dequantization kernels.
        """,

        "typical_results": {
            "awq_tokens_per_second": 50,
            "gptq_tokens_per_second": 48,
            "difference": "Within measurement noise",
        },

        "factors_that_matter_more": [
            "Serving framework (vLLM, TGI, etc.)",
            "Batch size",
            "Hardware (GPU model)",
            "Context length",
        ],
    }

Best Practices

def quantization_best_practices():
    return [
        {
            "practice": "Use representative calibration data",
            "why": "Quality depends on calibration matching production",
            "how": "Sample from your actual prompts",
        },
        {
            "practice": "Benchmark both methods",
            "why": "Results vary by model and task",
            "how": "Run your eval suite on both",
        },
        {
            "practice": "Check for pre-quantized models",
            "why": "Save quantization time",
            "where": "HuggingFace has many AWQ/GPTQ models",
        },
        {
            "practice": "Version control quantized models",
            "why": "Quantization isn't deterministic",
            "how": "Tag with quantization method and calibration set",
        },
        {
            "practice": "Test edge cases",
            "why": "Quantization errors show up on unusual inputs",
            "how": "Include adversarial examples in testing",
        },
    ]

The Bottom Line

def summary():
    return {
        "awq": {
            "choose_when": "Fast iteration, good default choice",
            "time": "Minutes to quantize",
        },
        "gptq": {
            "choose_when": "Custom calibration, need every bit of quality",
            "time": "Hours to quantize",
        },
        "either": {
            "result": "INT4 weights, ~2x memory reduction, similar quality",
            "recommendation": "Start with AWQ, switch to GPTQ if quality matters",
        },
    }

Both AWQ and GPTQ get you to INT4. AWQ gets you there faster. GPTQ gives you more control. For most use cases, the quality difference is noise—pick based on your iteration speed needs.