INTRODUCTION: WHY MOST LLM BENCHMARKS MISS THE MARK
When you watch the typical YouTube video comparing LLM performance or read the average benchmark article, you encounter a fundamental disconnect from reality. The presenter loads a model, sends a single prompt, measures the tokens per second, and declares victory. This approach is roughly equivalent to benchmarking a commercial airliner by measuring how quickly it can taxi from one gate to another with a single passenger aboard. The measurement is technically accurate but practically useless.
In production environments, particularly on enterprise hardware like the NVIDIA DGX systems or multi-GPU clusters, LLMs serve dozens or hundreds of concurrent users. They process batches of requests, handle multi-turn conversations, power agentic workflows where AI systems make sequential decisions, and employ sophisticated optimization techniques like speculative decoding and continuous batching. The performance characteristics under these conditions differ dramatically from the single-prompt scenario.
This article explores how to properly benchmark LLMs for real-world applications. We will examine workload profiling, inference engine selection, optimization techniques, and the construction of meaningful benchmarks that actually predict production performance. By the end, you will understand why that simple tokens-per-second number tells you almost nothing about how an LLM will perform in your specific use case.
UNDERSTANDING WORKLOAD PROFILES: THE FOUNDATION OF MEANINGFUL BENCHMARKS
Before you can benchmark an LLM system effectively, you must understand your workload profile. A workload profile characterizes how your application actually uses the LLM. Different applications exhibit radically different patterns, and these patterns determine which metrics matter and which optimization strategies work.
Consider a customer service chatbot versus a code generation tool versus a batch document summarization system. The chatbot receives sporadic requests throughout the day with varying complexity, requires low latency for good user experience, and handles multi-turn conversations where context must be maintained. The code generation tool might receive bursts of requests during working hours, can tolerate slightly higher latency, and typically processes longer contexts with detailed technical specifications. The document summarization system processes large batches during off-peak hours, prioritizes throughput over latency, and works with relatively uniform input sizes.
Each of these scenarios demands different infrastructure configurations, different inference engines, and different benchmark methodologies. Measuring them with the same simple single-prompt test would be like using a thermometer to measure distance.
The key dimensions of a workload profile include request arrival patterns, prompt length distributions, output length requirements, concurrency levels, latency tolerance, and context management needs. Let us examine each dimension in detail.
Request arrival patterns describe when and how requests come into your system. Some applications receive requests at a steady rate throughout the day. Others experience pronounced peaks during business hours or specific events. Still others process requests in scheduled batches. This pattern affects whether you need to optimize for consistent low latency or maximum throughput during peak periods.
Prompt length distribution characterizes the typical input size your system processes. Some applications work primarily with short prompts of a few dozen tokens. Others regularly handle prompts containing thousands of tokens of context. The distribution matters because longer prompts consume more GPU memory for the key-value cache and require more computation during the prefill phase.
Output length requirements specify how much text the model must generate in response to each prompt. Generating a short answer differs fundamentally from generating a multi-paragraph essay or a complete code file. Longer outputs mean more decoding steps, which affects both latency and throughput.
Concurrency levels indicate how many requests your system must handle simultaneously. A single-user application has concurrency of one. A popular web service might handle hundreds of concurrent requests. Higher concurrency enables better GPU utilization through batching but requires more memory and sophisticated scheduling.
Latency tolerance defines how quickly your application must respond. Interactive chat applications need responses to start appearing within milliseconds. Batch processing jobs can wait seconds or even minutes. This tolerance determines whether you can use techniques that trade latency for throughput.
Context management needs describe whether your application requires maintaining conversation history, how long that history extends, and whether multiple requests share context. Multi-turn conversations require careful cache management. Agentic workflows might build up extensive context over many sequential operations.
PROFILING YOUR SPECIFIC APPLICATION: A METHODICAL APPROACH
To build meaningful benchmarks, you must first profile your actual or anticipated workload. This process involves collecting data, analyzing patterns, and quantifying the dimensions we discussed above. Let me walk you through a systematic profiling methodology.
The first step involves instrumenting your application or similar applications to collect request data. If you already have a production system, you can log every request with timestamps, prompt lengths, output lengths, and user identifiers. If you are building a new system, you can analyze similar applications, conduct user studies, or make informed estimates based on your application design.
Here is a simple Python script that demonstrates how to collect and analyze workload data from an application log:
import json
import numpy as np
from datetime import datetime
from collections import defaultdict
class WorkloadProfiler:
"""
Analyzes LLM request logs to extract workload characteristics.
This profiler processes request logs and computes statistical
distributions of key workload dimensions.
"""
def __init__(self):
self.requests = []
self.hourly_distribution = defaultdict(int)
def load_requests_from_log(self, log_file_path):
"""
Load request data from a JSON log file where each line
contains a request record with timestamp, prompt_tokens,
output_tokens, and user_id fields.
"""
with open(log_file_path, 'r') as f:
for line in f:
try:
request = json.loads(line.strip())
self.requests.append(request)
except json.JSONDecodeError:
continue
def analyze_temporal_patterns(self):
"""
Analyze when requests arrive to identify peak hours
and request rate patterns.
"""
for request in self.requests:
timestamp = datetime.fromisoformat(request['timestamp'])
hour = timestamp.hour
self.hourly_distribution[hour] += 1
peak_hour = max(self.hourly_distribution.items(),
key=lambda x: x[1])
avg_requests_per_hour = len(self.requests) / 24
return {
'peak_hour': peak_hour[0],
'peak_requests': peak_hour[1],
'average_per_hour': avg_requests_per_hour,
'peak_to_average_ratio': peak_hour[1] / avg_requests_per_hour
}
def analyze_prompt_distribution(self):
"""
Compute statistical distribution of prompt lengths
to understand input size characteristics.
"""
prompt_lengths = [r['prompt_tokens'] for r in self.requests]
return {
'mean': np.mean(prompt_lengths),
'median': np.median(prompt_lengths),
'p95': np.percentile(prompt_lengths, 95),
'p99': np.percentile(prompt_lengths, 99),
'std_dev': np.std(prompt_lengths)
}
def analyze_output_distribution(self):
"""
Compute statistical distribution of output lengths
to understand generation requirements.
"""
output_lengths = [r['output_tokens'] for r in self.requests]
return {
'mean': np.mean(output_lengths),
'median': np.median(output_lengths),
'p95': np.percentile(output_lengths, 95),
'p99': np.percentile(output_lengths, 99),
'std_dev': np.std(output_lengths)
}
def estimate_concurrency(self, time_window_seconds=1.0):
"""
Estimate concurrent request levels by counting requests
with overlapping processing times within time windows.
"""
sorted_requests = sorted(self.requests,
key=lambda x: x['timestamp'])
max_concurrent = 0
for i, request in enumerate(sorted_requests):
start_time = datetime.fromisoformat(request['timestamp'])
concurrent = 1
for j in range(i + 1, len(sorted_requests)):
other_start = datetime.fromisoformat(
sorted_requests[j]['timestamp'])
time_diff = (other_start - start_time).total_seconds()
if time_diff < time_window_seconds:
concurrent += 1
else:
break
max_concurrent = max(max_concurrent, concurrent)
return max_concurrent
This profiler provides the foundation for understanding your workload. The temporal analysis reveals whether you need to optimize for steady-state throughput or handle request bursts. The prompt and output distributions tell you what context sizes and generation lengths to test. The concurrency estimation indicates how many requests your system must handle simultaneously.
Once you have collected this data, you can create a workload profile document that summarizes the characteristics. This profile becomes the specification for your benchmark. For example, your profile might indicate that ninety-five percent of prompts contain between 200 and 1500 tokens, that peak concurrency reaches 50 requests, that the median output length is 300 tokens, and that requests arrive in bursts during business hours with a peak-to-average ratio of 4:1.
BATCH PROCESSING AND CONTINUOUS BATCHING: THE PERFORMANCE MULTIPLIER
Modern inference engines achieve their impressive performance through batching, which processes multiple requests simultaneously on the GPU. Understanding batching is essential for meaningful benchmarks because batch size dramatically affects throughput and latency.
In naive batching, the system waits until it accumulates a certain number of requests, processes them all together, and then waits for the next batch. This approach works well for offline batch processing but introduces unacceptable latency for interactive applications. If your batch size is 32 and requests arrive one at a time, the first request waits while 31 others accumulate.
Continuous batching, pioneered by systems like Orca and implemented in modern inference engines, solves this problem. Instead of waiting for a full batch, the system maintains a dynamic batch that changes as requests arrive and complete. When a request finishes generating its output, the system immediately removes it from the batch and adds a new waiting request. This approach maximizes GPU utilization while minimizing latency.
The performance impact of batching is substantial. Consider a scenario where processing a single request achieves 50 tokens per second. With a batch size of 8, you might achieve 300 tokens per second total throughput, or 37.5 tokens per second per request. With a batch size of 32, total throughput might reach 800 tokens per second, though per-request throughput drops to 25 tokens per second. The GPU utilization increases from perhaps 30 percent with a single request to over 90 percent with a large batch.
Here is a simulation that demonstrates the difference between naive batching and continuous batching:
import random
import heapq
from dataclasses import dataclass
from typing import List
@dataclass
class Request:
"""Represents a single LLM inference request."""
arrival_time: float
prompt_tokens: int
output_tokens: int
request_id: int
class NaiveBatchingSimulator:
"""
Simulates a naive batching system that waits for a full
batch before processing. This demonstrates the latency
problems with simple batching approaches.
"""
def __init__(self, batch_size, tokens_per_second_per_request):
self.batch_size = batch_size
self.tokens_per_second = tokens_per_second_per_request
self.waiting_requests = []
self.current_time = 0.0
self.completed_requests = []
def add_request(self, request):
"""Add a request to the waiting queue."""
self.waiting_requests.append(request)
def process_batch(self):
"""
Process a full batch of requests. All requests in the
batch complete at the same time based on the longest
output length.
"""
if len(self.waiting_requests) < self.batch_size:
return
batch = self.waiting_requests[:self.batch_size]
self.waiting_requests = self.waiting_requests[self.batch_size:]
max_output_tokens = max(r.output_tokens for r in batch)
processing_time = max_output_tokens / self.tokens_per_second
for request in batch:
completion_time = self.current_time + processing_time
latency = completion_time - request.arrival_time
self.completed_requests.append({
'request_id': request.request_id,
'latency': latency,
'completion_time': completion_time
})
self.current_time += processing_time
def get_average_latency(self):
"""Calculate average request latency."""
if not self.completed_requests:
return 0.0
return sum(r['latency'] for r in self.completed_requests) / len(
self.completed_requests)
class ContinuousBatchingSimulator:
"""
Simulates continuous batching where requests can join and
leave the batch dynamically. This shows the latency benefits
of modern inference engines.
"""
def __init__(self, max_batch_size, tokens_per_second_total):
self.max_batch_size = max_batch_size
self.tokens_per_second_total = tokens_per_second_total
self.event_queue = [] # Min-heap of (time, event_type, request)
self.active_batch = []
self.completed_requests = []
self.current_time = 0.0
def add_request(self, request):
"""Add a request arrival event to the event queue."""
heapq.heappush(self.event_queue,
(request.arrival_time, 'arrival', request))
def simulate(self):
"""
Process events in chronological order, maintaining a
dynamic batch that changes as requests arrive and complete.
"""
while self.event_queue:
event_time, event_type, request = heapq.heappop(
self.event_queue)
self.current_time = event_time
if event_type == 'arrival':
self._handle_arrival(request)
elif event_type == 'completion':
self._handle_completion(request)
def _handle_arrival(self, request):
"""
Handle a new request arrival. Add to batch if space
available, otherwise queue it.
"""
if len(self.active_batch) < self.max_batch_size:
self.active_batch.append(request)
# Calculate when this request will complete
# In continuous batching, throughput is shared
batch_size = len(self.active_batch)
tokens_per_second_per_request = (
self.tokens_per_second_total / batch_size)
completion_time = (self.current_time +
request.output_tokens /
tokens_per_second_per_request)
heapq.heappush(self.event_queue,
(completion_time, 'completion', request))
else:
# In a real system, this would queue the request
# For simplicity, we just delay it slightly
heapq.heappush(self.event_queue,
(self.current_time + 0.1, 'arrival', request))
def _handle_completion(self, request):
"""
Handle request completion. Remove from batch and
record latency.
"""
if request in self.active_batch:
self.active_batch.remove(request)
latency = self.current_time - request.arrival_time
self.completed_requests.append({
'request_id': request.request_id,
'latency': latency,
'completion_time': self.current_time
})
def get_average_latency(self):
"""Calculate average request latency."""
if not self.completed_requests:
return 0.0
return sum(r['latency'] for r in self.completed_requests) / len(
self.completed_requests)
This simulation demonstrates why continuous batching is crucial for production systems. With naive batching and a batch size of 32, early requests in each batch wait for all 32 requests to accumulate before processing begins. With continuous batching, requests begin processing immediately and leave the batch as soon as they complete, allowing new requests to enter.
When benchmarking, you must test with realistic concurrency levels and batch sizes. A single-request benchmark tells you nothing about how the system performs when handling 50 concurrent users. The throughput might scale linearly, sublinearly, or even superlinearly depending on the inference engine and hardware configuration.
INFERENCE ENGINE ARCHITECTURE: THE HIDDEN PERFORMANCE VARIABLE
The inference engine you choose has as much impact on performance as the hardware you run it on. Different engines make different architectural decisions, implement different optimizations, and excel at different workload profiles. Understanding these differences is essential for both selecting the right engine and designing appropriate benchmarks.
Let us examine several popular inference engines and their characteristics. vLLM pioneered the PagedAttention mechanism, which manages the key-value cache more efficiently by breaking it into blocks that can be allocated and deallocated dynamically. This approach reduces memory waste and enables higher batch sizes. vLLM excels at high-throughput scenarios with many concurrent requests.
SGLang takes a different approach, optimizing for complex, multi-turn interactions and agentic workflows. It implements RadixAttention, which automatically detects and reuses common prompt prefixes across requests. When multiple requests share the same system prompt or context, SGLang computes the attention only once and reuses the results. This makes it particularly effective for chatbots and agents that maintain conversation history.
llama.cpp focuses on efficiency and portability, enabling LLM inference on consumer hardware and edge devices. It implements aggressive quantization, supports a wide range of model formats, and runs on CPUs as well as GPUs. While it may not achieve the absolute highest throughput on high-end hardware, it excels at resource-constrained scenarios.
TensorRT-LLM from NVIDIA provides deep integration with NVIDIA hardware, implementing optimizations specific to their GPU architectures. It supports features like FP8 precision on Hopper GPUs, multi-GPU tensor parallelism, and custom CUDA kernels. For NVIDIA hardware, it often achieves the best performance, but it lacks the flexibility of framework-agnostic engines.
Each engine implements different optimization techniques. Understanding these techniques helps you select the right engine and benchmark it appropriately. Let us explore some key optimizations.
Speculative decoding attempts to generate multiple tokens per forward pass by using a smaller, faster draft model to propose tokens and a larger model to verify them. When the draft model proposes correct tokens, you get multiple tokens for little additional cost. This technique works well when the draft model accuracy is high, which depends on the specific task and models involved.
Here is a conceptual implementation showing how speculative decoding works:
class SpeculativeDecoder:
"""
Implements speculative decoding using a small draft model
to propose tokens and a large target model to verify them.
This can significantly increase tokens per second when the
draft model produces accurate predictions.
"""
def __init__(self, draft_model, target_model, num_speculative_tokens):
self.draft_model = draft_model
self.target_model = target_model
self.num_speculative_tokens = num_speculative_tokens
def generate_tokens(self, prompt_tokens, max_new_tokens):
"""
Generate tokens using speculative decoding. The draft model
proposes multiple tokens, and the target model verifies them
in a single forward pass.
"""
generated_tokens = []
current_tokens = prompt_tokens.copy()
while len(generated_tokens) < max_new_tokens:
# Draft model proposes several tokens quickly
draft_tokens = self._draft_tokens(
current_tokens,
self.num_speculative_tokens)
# Target model verifies all proposed tokens in one pass
# by computing probabilities for each position
verification_input = current_tokens + draft_tokens
target_probs = self.target_model.get_probabilities(
verification_input)
# Check which draft tokens the target model accepts
accepted_tokens = []
for i, draft_token in enumerate(draft_tokens):
position_probs = target_probs[len(current_tokens) + i]
# Accept if target model probability is high enough
if position_probs[draft_token] > 0.5:
accepted_tokens.append(draft_token)
else:
# Sample from target model distribution instead
corrected_token = self._sample_from_distribution(
position_probs)
accepted_tokens.append(corrected_token)
# Stop accepting after first rejection
break
# Add accepted tokens to output
generated_tokens.extend(accepted_tokens)
current_tokens.extend(accepted_tokens)
# If we accepted fewer tokens than proposed, we need
# to resample from the target model
if len(accepted_tokens) < len(draft_tokens):
# Continue from where we stopped accepting
continue
return generated_tokens[:max_new_tokens]
def _draft_tokens(self, context, num_tokens):
"""
Use the fast draft model to propose tokens. The draft
model is typically much smaller and faster than the
target model.
"""
draft_tokens = []
current_context = context.copy()
for _ in range(num_tokens):
token = self.draft_model.sample_next_token(current_context)
draft_tokens.append(token)
current_context.append(token)
return draft_tokens
def _sample_from_distribution(self, probabilities):
"""Sample a token from the probability distribution."""
# In practice, this would use temperature, top-p, etc.
return probabilities.argmax()
Speculative decoding illustrates an important benchmarking principle: the same model can perform very differently depending on the inference engine and optimization techniques employed. A benchmark that simply measures tokens per second with a single request will completely miss the benefits of speculative decoding, which only appears under generation workloads.
Continuous batching, which we discussed earlier, is another engine-specific optimization. Some engines implement sophisticated scheduling algorithms that maximize batch utilization while minimizing latency. Others use simpler approaches that may waste GPU cycles or introduce unnecessary delays.
Quantization reduces model precision from FP16 or BF16 to INT8, INT4, or even lower bit widths. This reduces memory bandwidth requirements and enables larger batch sizes, but may impact output quality. Different engines implement quantization differently, with varying impacts on accuracy and performance.
Kernel fusion combines multiple operations into single GPU kernels to reduce memory traffic. Engines with custom CUDA kernels often achieve better performance than those relying on standard PyTorch operations, but at the cost of reduced portability.
BUILDING PROFILE-SPECIFIC BENCHMARKS: FROM THEORY TO PRACTICE
Now that we understand workload profiles, batching, and inference engines, we can construct meaningful benchmarks. A good benchmark replicates your actual workload profile, measures the metrics that matter for your use case, and tests the system under realistic conditions.
Let us build a comprehensive benchmark framework that can test different profiles. The framework should generate requests matching your workload profile, submit them to the inference engine with realistic timing, measure relevant metrics, and report results in a useful format.
import time
import threading
import queue
from typing import Callable, Dict, List
import numpy as np
class WorkloadGenerator:
"""
Generates synthetic requests matching a specified workload
profile. This allows testing inference engines under realistic
conditions rather than artificial single-request scenarios.
"""
def __init__(self, profile: Dict):
"""
Initialize with a workload profile containing distributions
for prompt lengths, output lengths, and arrival patterns.
"""
self.profile = profile
self.request_counter = 0
def generate_request(self) -> Dict:
"""
Generate a single request with characteristics drawn from
the workload profile distributions.
"""
prompt_length = int(np.random.normal(
self.profile['prompt_mean'],
self.profile['prompt_std']))
prompt_length = max(10, min(prompt_length,
self.profile['prompt_max']))
output_length = int(np.random.normal(
self.profile['output_mean'],
self.profile['output_std']))
output_length = max(10, min(output_length,
self.profile['output_max']))
self.request_counter += 1
return {
'request_id': self.request_counter,
'prompt_length': prompt_length,
'output_length': output_length,
'timestamp': time.time()
}
def generate_arrival_schedule(self, duration_seconds: int) -> List:
"""
Generate a schedule of request arrival times based on the
profile's temporal pattern. This might include bursts during
peak hours or steady arrival rates.
"""
schedule = []
current_time = 0.0
while current_time < duration_seconds:
# Determine arrival rate based on time of day
hour = int(current_time / 3600) % 24
base_rate = self.profile['requests_per_second']
# Apply peak hour multiplier if defined
if 'peak_hours' in self.profile:
if hour in self.profile['peak_hours']:
rate = base_rate * self.profile['peak_multiplier']
else:
rate = base_rate
else:
rate = base_rate
# Use Poisson process for realistic arrival timing
inter_arrival_time = np.random.exponential(1.0 / rate)
current_time += inter_arrival_time
if current_time < duration_seconds:
schedule.append(current_time)
return schedule
class BenchmarkRunner:
"""
Executes benchmark runs against an inference engine, collecting
detailed performance metrics under realistic workload conditions.
"""
def __init__(self, inference_engine_callable: Callable):
"""
Initialize with a callable that represents the inference
engine. The callable should accept a request dictionary
and return timing and output information.
"""
self.inference_engine = inference_engine_callable
self.results = []
self.request_queue = queue.Queue()
self.active_requests = {}
self.lock = threading.Lock()
def run_benchmark(self, workload_generator: WorkloadGenerator,
duration_seconds: int,
max_concurrent: int) -> Dict:
"""
Run a benchmark for the specified duration with the given
concurrency limit. This simulates realistic load conditions
rather than artificial sequential processing.
"""
arrival_schedule = workload_generator.generate_arrival_schedule(
duration_seconds)
# Start worker threads to process requests
workers = []
for _ in range(max_concurrent):
worker = threading.Thread(target=self._process_requests)
worker.daemon = True
worker.start()
workers.append(worker)
# Schedule request arrivals
start_time = time.time()
for arrival_time in arrival_schedule:
# Wait until the scheduled arrival time
current_time = time.time() - start_time
sleep_time = arrival_time - current_time
if sleep_time > 0:
time.sleep(sleep_time)
# Generate and queue the request
request = workload_generator.generate_request()
request['scheduled_time'] = arrival_time
request['actual_arrival_time'] = time.time()
self.request_queue.put(request)
# Signal completion and wait for workers to finish
for _ in range(max_concurrent):
self.request_queue.put(None)
for worker in workers:
worker.join()
# Analyze and return results
return self._analyze_results()
def _process_requests(self):
"""
Worker thread that processes requests from the queue.
Each worker simulates a concurrent user or process
submitting requests to the inference engine.
"""
while True:
request = self.request_queue.get()
if request is None:
break
# Record when processing actually starts
processing_start = time.time()
# Call the inference engine
try:
result = self.inference_engine(request)
processing_end = time.time()
# Calculate metrics
queue_time = (processing_start -
request['actual_arrival_time'])
processing_time = processing_end - processing_start
total_latency = processing_end - request['actual_arrival_time']
tokens_per_second = (request['output_length'] /
processing_time if processing_time > 0
else 0)
with self.lock:
self.results.append({
'request_id': request['request_id'],
'queue_time': queue_time,
'processing_time': processing_time,
'total_latency': total_latency,
'tokens_per_second': tokens_per_second,
'prompt_length': request['prompt_length'],
'output_length': request['output_length']
})
except Exception as e:
print(f"Error processing request {request['request_id']}: {e}")
def _analyze_results(self) -> Dict:
"""
Analyze collected results to compute summary statistics
and performance metrics relevant to the workload profile.
"""
if not self.results:
return {'error': 'No results collected'}
latencies = [r['total_latency'] for r in self.results]
queue_times = [r['queue_time'] for r in self.results]
processing_times = [r['processing_time'] for r in self.results]
throughputs = [r['tokens_per_second'] for r in self.results]
total_tokens = sum(r['output_length'] for r in self.results)
total_time = max(r['total_latency'] for r in self.results)
return {
'total_requests': len(self.results),
'total_tokens_generated': total_tokens,
'overall_throughput_tokens_per_second': total_tokens / total_time,
'latency_mean': np.mean(latencies),
'latency_median': np.median(latencies),
'latency_p95': np.percentile(latencies, 95),
'latency_p99': np.percentile(latencies, 99),
'queue_time_mean': np.mean(queue_times),
'processing_time_mean': np.mean(processing_times),
'per_request_throughput_mean': np.mean(throughputs),
'per_request_throughput_median': np.median(throughputs)
}
This benchmark framework captures the essential elements of realistic testing. It generates requests with appropriate characteristics, submits them with realistic timing, processes them concurrently, and measures metrics that matter for production systems.
Notice how this differs from a simple single-request benchmark. We measure queue time separately from processing time because in production systems, requests often wait before processing begins. We track percentile latencies because mean latency can be misleading when some requests experience much longer delays. We compute both overall throughput and per-request throughput because they tell different stories about system performance.
To use this framework, you would define a workload profile based on your application analysis, implement a wrapper around your chosen inference engine, and run the benchmark:
# Define a workload profile for a customer service chatbot
chatbot_profile = {
'prompt_mean': 500,
'prompt_std': 200,
'prompt_max': 2000,
'output_mean': 300,
'output_std': 150,
'output_max': 1000,
'requests_per_second': 2.0,
'peak_hours': [9, 10, 11, 14, 15, 16],
'peak_multiplier': 3.0
}
# Create a mock inference engine for demonstration
# In practice, this would call vLLM, SGLang, etc.
def mock_inference_engine(request):
"""
Simulates an inference engine by sleeping for a time
proportional to the output length. Real implementation
would call actual inference engine APIs.
"""
# Simulate processing time based on output length
# Real engines have more complex performance characteristics
base_time_per_token = 0.01
processing_time = request['output_length'] * base_time_per_token
time.sleep(processing_time)
return {
'output_tokens': request['output_length'],
'processing_time': processing_time
}
# Run the benchmark
generator = WorkloadGenerator(chatbot_profile)
runner = BenchmarkRunner(mock_inference_engine)
results = runner.run_benchmark(
generator,
duration_seconds=300, # 5 minute benchmark
max_concurrent=20
)
# Results now contain realistic performance metrics
print(f"Overall throughput: {results['overall_throughput_tokens_per_second']:.2f} tokens/sec")
print(f"P95 latency: {results['latency_p95']:.3f} seconds")
print(f"Mean queue time: {results['queue_time_mean']:.3f} seconds")
This benchmark tells you how the system performs under conditions that match your actual application. The P95 latency indicates whether 95 percent of your users will receive acceptable response times. The queue time reveals whether your system has sufficient capacity or if requests pile up waiting for processing. The overall throughput shows whether you can handle your peak load.
HARDWARE CONSIDERATIONS AND MEMORY BANDWIDTH
The GPU hardware you benchmark on matters enormously, and not just because faster GPUs produce higher numbers. Different GPUs have different memory capacities, memory bandwidths, compute capabilities, and architectural features that interact with inference engines in complex ways.
Memory bandwidth often limits LLM inference performance more than compute capability. During the decoding phase, the model generates one token at a time, and each token generation requires loading the entire model weights from GPU memory. If your model has 70 billion parameters stored in 16-bit precision, that is 140 gigabytes of data. Generating a single token requires reading most of this data, but performs relatively little computation on it.
An NVIDIA A100 GPU has 1.5 terabytes per second of memory bandwidth. Reading 140 gigabytes takes about 0.093 seconds, limiting you to roughly 10 tokens per second for a single request. An H100 GPU has 3 terabytes per second of memory bandwidth, potentially doubling this to 20 tokens per second. The H100 also has much higher compute capability, but for single-request inference, memory bandwidth dominates.
Batching helps because you read the weights once but use them to generate tokens for multiple requests. With a batch size of 8, you might achieve 80 tokens per second total throughput on an A100, better utilizing the available memory bandwidth. However, the key-value cache for each request also consumes memory bandwidth, and as batch size increases, cache memory traffic eventually becomes the bottleneck.
Different model sizes and architectures stress hardware differently. A 7-billion parameter model fits comfortably in GPU memory and achieves high throughput. A 70-billion parameter model barely fits on a single A100 and may require tensor parallelism across multiple GPUs. A 405-billion parameter model definitely requires multiple GPUs and careful optimization.
When benchmarking, you must consider whether your test configuration matches your production deployment. Testing a 70B model on a single GPU might show poor performance, but deploying it across 4 GPUs with tensor parallelism could achieve acceptable throughput. The benchmark should test the actual deployment configuration.
Quantization trades some model quality for reduced memory footprint and bandwidth requirements. A model quantized to 4-bit precision uses one quarter the memory bandwidth of the 16-bit version, potentially quadrupling throughput. However, quantization impacts output quality, and the impact varies by model and task. Your benchmark should test whether quantized models meet your quality requirements while measuring the performance gains.
PUTTING IT ALL TOGETHER: A COMPLETE BENCHMARKING STRATEGY
Let me synthesize everything we have discussed into a complete benchmarking strategy. This strategy will guide you from initial application analysis through benchmark design, execution, and interpretation.
First, profile your application thoroughly. Collect real usage data if possible, or make informed estimates based on similar applications and user studies. Quantify the key dimensions: request arrival patterns, prompt length distribution, output length distribution, concurrency levels, latency requirements, and context management needs. Document these in a formal workload profile.
Second, select candidate inference engines based on your workload profile. For high-throughput batch processing, consider vLLM with its efficient PagedAttention. For interactive multi-turn conversations and agentic workflows, evaluate SGLang with its RadixAttention. For deployment on diverse hardware including CPUs and edge devices, test llama.cpp. For NVIDIA hardware with the latest features, benchmark TensorRT-LLM.
Third, design benchmark scenarios that replicate your workload profile. Do not just test single requests. Generate request streams with realistic arrival patterns, prompt lengths, and output requirements. Test at your expected peak concurrency level and beyond to understand system behavior under stress.
Fourth, measure the right metrics. For interactive applications, latency percentiles matter more than mean latency. For batch processing, overall throughput matters more than per-request latency. For agentic workflows, measure the total time to complete multi-step tasks, not just individual request latency.
Fifth, test multiple configurations. Vary batch sizes, quantization levels, and optimization techniques. For each configuration, measure performance and output quality. Some optimizations improve performance but degrade quality, and you need to find the right balance.
Sixth, validate output quality. Performance means nothing if the model produces garbage. For each configuration you test, evaluate output quality on a representative sample of requests. Compare outputs from different configurations to ensure optimizations do not unacceptably degrade quality.
Seventh, document everything. Record the exact model version, inference engine version, hardware configuration, software environment, benchmark parameters, and results. Benchmarks are only useful if they are reproducible and if you can compare results across different configurations.
Here is a complete example that demonstrates this strategy:
class ComprehensiveBenchmark:
"""
Implements a complete benchmarking strategy that tests multiple
inference engines and configurations against a workload profile,
measuring both performance and output quality.
"""
def __init__(self, workload_profile: Dict, quality_evaluator: Callable):
self.profile = workload_profile
self.quality_evaluator = quality_evaluator
self.results = []
def benchmark_configuration(self,
engine_name: str,
engine_callable: Callable,
config: Dict) -> Dict:
"""
Benchmark a specific inference engine configuration.
Returns both performance metrics and quality scores.
"""
print(f"Benchmarking {engine_name} with config: {config}")
# Run performance benchmark
generator = WorkloadGenerator(self.profile)
runner = BenchmarkRunner(engine_callable)
perf_results = runner.run_benchmark(
generator,
duration_seconds=self.profile.get('benchmark_duration', 300),
max_concurrent=self.profile.get('max_concurrent', 20)
)
# Evaluate output quality on a sample of requests
quality_sample_size = min(50, len(runner.results))
quality_samples = np.random.choice(
runner.results,
size=quality_sample_size,
replace=False
)
quality_scores = []
for sample in quality_samples:
# In practice, this would compare actual model outputs
# against reference outputs or human evaluations
score = self.quality_evaluator(sample)
quality_scores.append(score)
# Combine performance and quality metrics
result = {
'engine_name': engine_name,
'configuration': config,
'performance': perf_results,
'quality_mean': np.mean(quality_scores),
'quality_std': np.std(quality_scores),
'quality_min': np.min(quality_scores)
}
self.results.append(result)
return result
def compare_configurations(self) -> str:
"""
Generate a comparison report across all tested configurations.
This helps identify the best configuration for the workload.
"""
if not self.results:
return "No results to compare"
report = []
report.append("BENCHMARK COMPARISON REPORT")
report.append("=" * 70)
report.append("")
# Sort by overall throughput
sorted_results = sorted(
self.results,
key=lambda x: x['performance']['overall_throughput_tokens_per_second'],
reverse=True
)
for i, result in enumerate(sorted_results, 1):
report.append(f"{i}. {result['engine_name']}")
report.append(f" Configuration: {result['configuration']}")
report.append(f" Throughput: {result['performance']['overall_throughput_tokens_per_second']:.2f} tokens/sec")
report.append(f" P95 Latency: {result['performance']['latency_p95']:.3f} sec")
report.append(f" Quality Score: {result['quality_mean']:.3f} +/- {result['quality_std']:.3f}")
report.append("")
# Identify best configuration for different priorities
best_throughput = max(
self.results,
key=lambda x: x['performance']['overall_throughput_tokens_per_second']
)
best_latency = min(
self.results,
key=lambda x: x['performance']['latency_p95']
)
best_quality = max(
self.results,
key=lambda x: x['quality_mean']
)
report.append("RECOMMENDATIONS")
report.append("-" * 70)
report.append(f"Best throughput: {best_throughput['engine_name']} - {best_throughput['configuration']}")
report.append(f"Best latency: {best_latency['engine_name']} - {best_latency['configuration']}")
report.append(f"Best quality: {best_quality['engine_name']} - {best_quality['configuration']}")
return "\n".join(report)
def export_results(self, filename: str):
"""
Export detailed results to a JSON file for further analysis
or sharing with stakeholders.
"""
import json
with open(filename, 'w') as f:
json.dump(self.results, f, indent=2)
This comprehensive benchmark framework enables you to make informed decisions about which inference engine and configuration to deploy. You can see the tradeoffs between throughput, latency, and quality, and choose the configuration that best meets your requirements.
ADVANCED TOPICS: MULTI-GPU DEPLOYMENTS AND DISTRIBUTED INFERENCE
For very large models or high-throughput requirements, you may need to deploy across multiple GPUs. This introduces additional complexity in benchmarking because you must account for communication overhead, load balancing, and distributed scheduling.
Tensor parallelism splits individual model layers across multiple GPUs. Each GPU computes part of each layer, and GPUs communicate intermediate results. This enables running models too large for a single GPU, but introduces communication overhead that affects performance. The communication overhead depends on the interconnect technology. NVLink between GPUs in the same server provides very high bandwidth with low latency. InfiniBand between servers has higher latency and lower bandwidth.
Pipeline parallelism splits the model vertically, with different layers on different GPUs. A request flows through the pipeline, with each GPU processing its assigned layers. This reduces communication compared to tensor parallelism but introduces pipeline bubbles where GPUs sit idle waiting for work.
When benchmarking multi-GPU deployments, you must measure the actual throughput and latency of the distributed system, not just extrapolate from single-GPU results. Communication overhead, load balancing inefficiencies, and synchronization costs can significantly impact performance.
CONCLUSION: BENCHMARKING AS ENGINEERING DISCIPLINE
Effective LLM benchmarking requires treating it as a serious engineering discipline rather than a casual comparison exercise. You must understand your workload, select appropriate tools, design realistic tests, measure meaningful metrics, and interpret results in context.
The single-prompt benchmark that dominates YouTube and blog posts tells you almost nothing about production performance. Real applications involve concurrent users, batched requests, multi-turn conversations, and complex workflows. The performance characteristics under these conditions differ dramatically from the simple case.
By profiling your workload, selecting the right inference engine, designing profile-specific benchmarks, and measuring the right metrics, you can make informed decisions about hardware, software, and deployment configurations. This approach transforms benchmarking from a marketing exercise into a valuable engineering tool that actually predicts production performance.
The investment in proper benchmarking pays dividends throughout your project. You avoid costly mistakes like selecting hardware that cannot handle your workload or choosing an inference engine optimized for the wrong use case. You can confidently plan capacity, estimate costs, and set performance expectations.
As LLM technology continues to evolve, benchmarking methodologies must evolve with it. New optimization techniques, new hardware architectures, and new deployment patterns will require new benchmarking approaches. The principles outlined in this article provide a foundation for adapting your benchmarking strategy as the technology landscape changes.
Remember that benchmarks measure what you tell them to measure. If you measure the wrong things, you will optimize for the wrong goals. Take the time to understand your workload, design appropriate tests, and measure meaningful metrics. Your production deployment will thank you.
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