Now that we've explored the various metrics and methodologies for evaluating quantized LLMs, let's put this knowledge into practice. This section provides a hands-on guide to setting up and executing a benchmark comparing a quantized LLM against its full-precision counterpart. We will measure performance characteristics like latency, throughput, and memory usage, alongside evaluating the impact on model quality using perplexity.

Our goal is to obtain concrete data points that illustrate the trade-offs involved in quantization, enabling informed decisions about model deployment.

Prerequisites and Setup

Before starting, ensure you have the necessary environment set up. This practical assumes you have access to a machine with a CUDA-enabled GPU and have installed the required Python libraries.

Python: Version 3.8 or higher.
PyTorch: Install with CUDA support.
Transformers: pip install transformers
Accelerate: pip install accelerate
Datasets: pip install datasets
Evaluate: pip install evaluate
bitsandbytes: (If benchmarking a bitsandbytes quantized model) pip install bitsandbytes
AutoGPTQ/AutoAWQ: (If benchmarking GPTQ/AWQ models) pip install auto-gptq or pip install autoawq

For this example, we will compare a baseline FP16 model (e.g., meta-llama/Llama-2-7b-hf) with a corresponding INT4 quantized version (e.g., using GPTQ). You will need to replace model identifiers with the specific models you are evaluating. Ensure the quantized model is compatible with your environment and libraries (e.g., requires auto-gptq for GPTQ).

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import time
import evaluate
from datasets import load_dataset
import numpy as np

# Configuration
baseline_model_id = "meta-llama/Llama-2-7b-hf" # Replace with your baseline model
quantized_model_id = "TheBloke/Llama-2-7B-GPTQ" # Replace with your quantized model
device = "cuda" if torch.cuda.is_available() else "cpu"
num_samples = 50 # Number of samples for benchmarking latency/throughput
max_new_tokens = 100 # Number of tokens to generate
perplexity_dataset = "wikitext"
perplexity_dataset_config = "wikitext-2-raw-v1"
perplexity_split = "test"
perplexity_max_samples = 50 # Reduce for faster evaluation

# Sample prompt for generation tasks
prompt = "The field of Large Language Models is "

print(f"Using device: {device}")
if device == "cpu":
    print("Warning: Benchmarking on CPU will be significantly slower and memory usage patterns differ.")

# --- Helper Functions ---

def load_model_and_tokenizer(model_id, is_quantized=False):
    print(f"Loading model: {model_id}...")
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token # Set pad token if missing

    model_kwargs = {"device_map": "auto"}
    if is_quantized:
        # Add quantization specific loading params if needed
        # Example for AutoGPTQ:
        # model_kwargs["use_safetensors"] = True
        # model_kwargs["trust_remote_code"] = True # Be cautious with trust_remote_code
        pass # Add specific loading arguments for your quantized model type here

    model = AutoModelForCausalLM.from_pretrained(model_id, **model_kwargs)
    model.eval() # Set model to evaluation mode
    print("Model loaded.")
    return model, tokenizer

def measure_latency(model, tokenizer, prompt, max_new_tokens, num_runs=10):
    print("Measuring latency...")
    latencies = []
    inputs = tokenizer(prompt, return_tensors="pt").to(device)

    # Warm-up run
    with torch.no_grad():
        _ = model.generate(**inputs, max_new_tokens=max_new_tokens, pad_token_id=tokenizer.pad_token_id)
        torch.cuda.synchronize() # Ensure GPU operation is complete

    for _ in range(num_runs):
        start_event = torch.cuda.Event(enable_timing=True)
        end_event = torch.cuda.Event(enable_timing=True)

        start_event.record()
        with torch.no_grad():
            _ = model.generate(**inputs, max_new_tokens=max_new_tokens, pad_token_id=tokenizer.pad_token_id)
        end_event.record()
        torch.cuda.synchronize() # Wait for the operation to complete

        latency_ms = start_event.elapsed_time(end_event)
        latencies.append(latency_ms)
        # print(f"Run latency: {latency_ms:.2f} ms") # Optional: print individual run latency

    avg_latency = np.mean(latencies)
    print(f"Average latency ({num_runs} runs): {avg_latency:.2f} ms")
    return avg_latency

def measure_throughput(model, tokenizer, prompt, max_new_tokens, num_samples=50):
    print("Measuring throughput...")
    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    total_tokens_generated = 0
    total_time_sec = 0

    # Warm-up
    with torch.no_grad():
        _ = model.generate(**inputs, max_new_tokens=max_new_tokens, pad_token_id=tokenizer.pad_token_id)
        torch.cuda.synchronize()

    start_time = time.time()
    start_event = torch.cuda.Event(enable_timing=True)
    end_event = torch.cuda.Event(enable_timing=True)

    start_event.record()
    with torch.no_grad():
        # Note: This simple loop doesn't parallelize requests.
        # Real-world throughput often involves batching or concurrent requests.
        # For simplicity, we measure sequential generation speed here.
        outputs = model.generate(**inputs, max_new_tokens=num_samples * max_new_tokens, pad_token_id=tokenizer.pad_token_id, do_sample=False) # Generate a long sequence
        generated_tokens = outputs[0][inputs.input_ids.shape[1]:].size(0) # Count generated tokens excluding prompt
        total_tokens_generated = generated_tokens

    end_event.record()
    torch.cuda.synchronize()
    total_time_sec = start_event.elapsed_time(end_event) / 1000.0 # Time in seconds

    # Alternative: CPU timing (less precise for GPU operations)
    # total_time_sec = time.time() - start_time

    throughput_tokens_per_sec = total_tokens_generated / total_time_sec if total_time_sec > 0 else 0
    print(f"Total tokens generated: {total_tokens_generated}")
    print(f"Total time: {total_time_sec:.2f} sec")
    print(f"Throughput: {throughput_tokens_per_sec:.2f} tokens/sec")
    return throughput_tokens_per_sec


def measure_memory_usage(model_load_fn, *args, **kwargs):
    print("Measuring peak memory usage...")
    torch.cuda.reset_peak_memory_stats(device)
    initial_memory = torch.cuda.max_memory_allocated(device)

    # Load the model within this function to capture its memory footprint
    model, tokenizer = model_load_fn(*args, **kwargs)
    memory_after_load = torch.cuda.max_memory_allocated(device)
    print(f"Memory after load: {memory_after_load / (1024**3):.2f} GB")

    # Perform a sample inference run to capture runtime memory
    inputs = tokenizer("Sample text for memory measurement.", return_tensors="pt").to(device)
    with torch.no_grad():
         _ = model.generate(**inputs, max_new_tokens=10, pad_token_id=tokenizer.pad_token_id)
    torch.cuda.synchronize()

    peak_memory = torch.cuda.max_memory_allocated(device)
    peak_memory_gb = peak_memory / (1024**3) # Convert bytes to GB
    print(f"Peak memory usage during inference: {peak_memory_gb:.2f} GB")

    # Clean up memory
    del model
    del tokenizer
    torch.cuda.empty_cache()
    return peak_memory_gb

def calculate_perplexity(model, tokenizer, dataset_name, dataset_config, split, max_samples=50):
    print(f"Calculating perplexity on {dataset_name} ({split} split)...")
    try:
        perplexity_metric = evaluate.load("perplexity", module_type="metric")
        data = load_dataset(dataset_name, dataset_config, split=f"{split}[:{max_samples}]") # Use a slice for faster eval
        data = data.map(lambda examples: tokenizer(examples["text"]), batched=True) # Use default settings

        results = perplexity_metric.compute(model=model,
                                            tokenizer=tokenizer,
                                            data=data["text"], # Pass raw text
                                            batch_size=1, # Adjust batch size based on GPU memory
                                            device=device)
        ppl = results["perplexity"]
        print(f"Perplexity: {ppl:.4f}")
        return ppl
    except Exception as e:
        print(f"Error calculating perplexity: {e}")
        print("Skipping perplexity calculation.")
        return None

# --- Benchmarking Execution ---

results = {}

# Benchmark Baseline Model
print("\n--- Benchmarking Baseline Model ---")
# Measure memory separately to capture peak usage during load
baseline_memory = measure_memory_usage(load_model_and_tokenizer, baseline_model_id, is_quantized=False)
# Load again for other benchmarks
baseline_model, baseline_tokenizer = load_model_and_tokenizer(baseline_model_id, is_quantized=False)
baseline_latency = measure_latency(baseline_model, baseline_tokenizer, prompt, max_new_tokens)
baseline_throughput = measure_throughput(baseline_model, baseline_tokenizer, prompt, max_new_tokens, num_samples)
baseline_perplexity = calculate_perplexity(baseline_model, baseline_tokenizer, perplexity_dataset, perplexity_dataset_config, perplexity_split, perplexity_max_samples)

results["baseline"] = {
    "latency_ms": baseline_latency,
    "throughput_tokens_sec": baseline_throughput,
    "peak_memory_gb": baseline_memory,
    "perplexity": baseline_perplexity
}

# Clean up baseline model memory before loading quantized model
print("Cleaning up baseline model...")
del baseline_model
del baseline_tokenizer
torch.cuda.empty_cache()
print("Baseline model cleanup complete.")


# Benchmark Quantized Model
print("\n--- Benchmarking Quantized Model ---")
# Measure memory separately
quantized_memory = measure_memory_usage(load_model_and_tokenizer, quantized_model_id, is_quantized=True)
# Load again for other benchmarks
quantized_model, quantized_tokenizer = load_model_and_tokenizer(quantized_model_id, is_quantized=True)
quantized_latency = measure_latency(quantized_model, quantized_tokenizer, prompt, max_new_tokens)
quantized_throughput = measure_throughput(quantized_model, quantized_tokenizer, prompt, max_new_tokens, num_samples)
quantized_perplexity = calculate_perplexity(quantized_model, quantized_tokenizer, perplexity_dataset, perplexity_dataset_config, perplexity_split, perplexity_max_samples)

results["quantized"] = {
    "latency_ms": quantized_latency,
    "throughput_tokens_sec": quantized_throughput,
    "peak_memory_gb": quantized_memory,
    "perplexity": quantized_perplexity
}

# Clean up quantized model memory
print("Cleaning up quantized model...")
del quantized_model
del quantized_tokenizer
torch.cuda.empty_cache()
print("Quantized model cleanup complete.")


# --- Results Analysis ---
print("\n--- Benchmark Results Summary ---")

print(f"{'Metric':<25} {'Baseline':<15} {'Quantized':<15} {'Change (%)':<15}")
print("-" * 70)

# Latency
base_lat = results["baseline"]["latency_ms"]
quant_lat = results["quantized"]["latency_ms"]
lat_change = ((quant_lat - base_lat) / base_lat) * 100 if base_lat else 0
print(f"{'Avg Latency (ms)':<25} {base_lat:<15.2f} {quant_lat:<15.2f} {lat_change:<15.2f}")

# Throughput
base_thr = results["baseline"]["throughput_tokens_sec"]
quant_thr = results["quantized"]["throughput_tokens_sec"]
thr_change = ((quant_thr - base_thr) / base_thr) * 100 if base_thr else 0
print(f"{'Throughput (tokens/sec)':<25} {base_thr:<15.2f} {quant_thr:<15.2f} {thr_change:<15.2f}")

# Memory
base_mem = results["baseline"]["peak_memory_gb"]
quant_mem = results["quantized"]["peak_memory_gb"]
mem_change = ((quant_mem - base_mem) / base_mem) * 100 if base_mem else 0
print(f"{'Peak Memory (GB)':<25} {base_mem:<15.2f} {quant_mem:<15.2f} {mem_change:<15.2f}")

# Perplexity
base_ppl = results["baseline"]["perplexity"]
quant_ppl = results["quantized"]["perplexity"]
if base_ppl is not None and quant_ppl is not None:
    ppl_change = ((quant_ppl - base_ppl) / base_ppl) * 100
    print(f"{'Perplexity':<25} {base_ppl:<15.4f} {quant_ppl:<15.4f} {ppl_change:<15.2f}")
else:
    print(f"{'Perplexity':<25} {'N/A':<15} {'N/A':<15} {'N/A':<15}")

print("-" * 70)

# Optional: Visualization
# Prepare data for plotting (replace with actual numbers from your run)
# These are illustrative placeholder values.
base_lat_val = results["baseline"]["latency_ms"] if results["baseline"]["latency_ms"] else 1000
quant_lat_val = results["quantized"]["latency_ms"] if results["quantized"]["latency_ms"] else 500
base_thr_val = results["baseline"]["throughput_tokens_sec"] if results["baseline"]["throughput_tokens_sec"] else 50
quant_thr_val = results["quantized"]["throughput_tokens_sec"] if results["quantized"]["throughput_tokens_sec"] else 100
base_mem_val = results["baseline"]["peak_memory_gb"] if results["baseline"]["peak_memory_gb"] else 15
quant_mem_val = results["quantized"]["peak_memory_gb"] if results["quantized"]["peak_memory_gb"] else 8
base_ppl_val = results["baseline"]["perplexity"] if results["baseline"]["perplexity"] else 5.0
quant_ppl_val = results["quantized"]["perplexity"] if results["quantized"]["perplexity"] else 5.5


```plotly
{"layout": {"title": "Baseline (FP16) vs. Quantized (INT4) LLM Performance", "barmode": "group", "xaxis": {"title": "Metric"}, "yaxis": {"title": "Value"}, "legend_title_text": "Model Type", "height": 400}, "data": [{"type": "bar", "name": "Baseline (FP16)", "x": ["Latency (ms)", "Throughput (tok/s)", "Memory (GB)", "Perplexity"], "y": [base_lat_val, base_thr_val, base_mem_val, base_ppl_val], "marker": {"color": "#4263eb"}}, {"type": "bar", "name": "Quantized (INT4)", "x": ["Latency (ms)", "Throughput (tok/s)", "Memory (GB)", "Perplexity"], "y": [quant_lat_val, quant_thr_val, quant_mem_val, quant_ppl_val], "marker": {"color": "#12b886"}}]}

Comparison of performance and quality metrics between the baseline FP16 model and its INT4 quantized version. Lower is better for Latency, Memory, and Perplexity. Higher is better for Throughput. (Note: Values are illustrative).

Interpreting the Results

The output table and chart provide a quantitative comparison. You should observe:

Latency: Quantized models typically exhibit lower latency (faster single inference) due to reduced computation and memory access times.
Throughput: Higher throughput is expected for the quantized model, meaning it can process more tokens or requests per second. Our throughput measurement here is basic (tokens per second in a single long generation); more sophisticated setups might involve batching or concurrent requests for a more realistic server throughput measure.
Memory Usage: Peak GPU memory usage should be significantly lower for the quantized model. This includes both the memory to load the model weights and the runtime memory for activations. Also, compare the model sizes on disk (du -sh model_directory).
Perplexity: Perplexity typically increases slightly with quantization, indicating a minor degradation in the model's ability to predict the next token based on the training distribution. The acceptable level of increase depends heavily on the specific application and the quantization method used. A small increase (e.g., < 1 point) might be acceptable for significant performance gains.

This practical benchmark provides essential data points. Remember that these results are specific to the hardware used, the chosen model, the quantization technique (e.g., GPTQ, AWQ, bitsandbytes), and the specific benchmarking setup (batch size, sequence length, dataset). For production scenarios, you would extend this by:

Benchmarking on target deployment hardware.
Evaluating on downstream tasks specific to your application (e.g., summarization ROUGE scores, question-answering accuracy).
Using more sophisticated throughput measurement tools or simulating production load.
Comparing different quantization methods and bit-precisions.

By systematically benchmarking, you can confidently select and deploy quantized models that meet your performance requirements while maintaining acceptable quality.