High-Performance LLM Inference with vLLM and TGI

This comprehensive tutorial covers high-performance LLM inference hosting using vLLM and TGI (Text Generation Inference), with a strong focus on optimizing inference speed. Both frameworks are production-ready inference servers that provide high throughput, low latency, and efficient GPU memory utilization.

Introduction to vLLM and TGI

vLLM (Very Large Language Model)

Developer: UC Berkeley
Key Innovation: PagedAttention algorithm for efficient memory management
Best For: High-throughput scenarios with batch processing
Language: Python-based, easy to integrate
Supports: Most popular models (LLaMA, Mistral, GPT, etc.)

TGI (Text Generation Inference)

Developer: Hugging Face
Key Innovation: Written in Rust for maximum performance
Best For: Low-latency scenarios and real-time applications
Features: Built-in Flash Attention, extensive quantization support
Supports: GPTQ, AWQ, bitsandbytes quantization

Key Speed Optimization Techniques

Both frameworks implement several critical optimization techniques:

Continuous Batching - Process multiple requests in parallel efficiently
KV Cache Optimization - Efficient attention computation and memory usage
Quantization - Reduce model size (4-bit, 8-bit) for faster inference
Tensor Parallelism - Distribute model across multiple GPUs
Speculative Decoding - Use smaller models to predict tokens
PagedAttention (vLLM) - Eliminates memory fragmentation
Flash Attention - Optimized attention computation

Part 1: vLLM Setup and Installation

Installation

# Install vLLM
pip install vllm

# For development
pip install vllm[dev]

Starting vLLM Server - Basic Configuration

# Basic server startup
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-2-7b-chat-hf \
    --port 8000 \
    --tensor-parallel-size 1

Advanced vLLM Server Configurations

# With quantization (faster, less memory)
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-2-7b-chat-hf \
    --quantization awq \
    --port 8000

# With multiple GPUs (tensor parallelism)
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-2-7b-chat-hf \
    --tensor-parallel-size 2 \
    --port 8000

# With continuous batching optimization
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-2-7b-chat-hf \
    --max-num-seqs 256 \
    --gpu-memory-utilization 0.95 \
    --port 8000

vLLM Client Implementation

vLLM provides an OpenAI-compatible API, making it easy to integrate:

import asyncio
import time
from openai import OpenAI, AsyncOpenAI
from typing import List, Dict, Any

class VLLMClient:
    """
    Client for interacting with vLLM server.
    vLLM provides an OpenAI-compatible API.
    """
    
    def __init__(self, base_url: str = "http://localhost:8000/v1", api_key: str = "dummy"):
        self.client = OpenAI(base_url=base_url, api_key=api_key)
        self.base_url = base_url
    
    def generate(
        self,
        prompt: str,
        model: str = "meta-llama/Llama-2-7b-chat-hf",
        max_tokens: int = 100,
        temperature: float = 0.7,
        top_p: float = 1.0,
        stream: bool = False
    ) -> str:
        """
        Generate text using vLLM.
        
        Args:
            prompt: Input prompt
            model: Model name
            max_tokens: Maximum tokens to generate
            temperature: Sampling temperature
            top_p: Nucleus sampling parameter
            stream: Whether to stream the response
        """
        messages = [{"role": "user", "content": prompt}]
        
        response = self.client.chat.completions.create(
            model=model,
            messages=messages,
            max_tokens=max_tokens,
            temperature=temperature,
            top_p=top_p,
            stream=stream
        )
        
        if stream:
            full_response = ""
            for chunk in response:
                if chunk.choices[0].delta.content:
                    full_response += chunk.choices[0].delta.content
            return full_response
        else:
            return response.choices[0].message.content
    
    def batch_generate(
        self,
        prompts: List[str],
        model: str = "meta-llama/Llama-2-7b-chat-hf",
        max_tokens: int = 100,
        temperature: float = 0.7
    ) -> List[str]:
        """
        Generate text for multiple prompts using vLLM's continuous batching.
        This is more efficient than sequential calls.
        """
        messages_list = [[{"role": "user", "content": prompt}] for prompt in prompts]
        
        results = []
        for messages in messages_list:
            response = self.client.chat.completions.create(
                model=model,
                messages=messages,
                max_tokens=max_tokens,
                temperature=temperature
            )
            results.append(response.choices[0].message.content)
        
        return results
    
    def async_batch_generate(
        self,
        prompts: List[str],
        model: str = "meta-llama/Llama-2-7b-chat-hf",
        max_tokens: int = 100,
        temperature: float = 0.7
    ) -> List[str]:
        """
        Generate text for multiple prompts asynchronously.
        This leverages vLLM's continuous batching for maximum throughput.
        """
        async def generate_single(prompt: str):
            client = AsyncOpenAI(base_url=self.base_url, api_key="dummy")
            messages = [{"role": "user", "content": prompt}]
            response = await client.chat.completions.create(
                model=model,
                messages=messages,
                max_tokens=max_tokens,
                temperature=temperature
            )
            return response.choices[0].message.content
        
        async def generate_all():
            tasks = [generate_single(prompt) for prompt in prompts]
            return await asyncio.gather(*tasks)
        
        return asyncio.run(generate_all())

Part 2: TGI Setup and Installation

Installation with Docker (Recommended)

# Basic TGI server with Docker
docker run --gpus all -p 8080:80 \
    ghcr.io/huggingface/text-generation-inference:latest \
    --model-id meta-llama/Llama-2-7b-chat-hf

Advanced TGI Server Configurations

# With quantization (4-bit for speed)
docker run --gpus all -p 8080:80 \
    ghcr.io/huggingface/text-generation-inference:latest \
    --model-id meta-llama/Llama-2-7b-chat-hf \
    --quantize bitsandbytes \
    --num-shard 1

# With multiple GPUs (sharding)
docker run --gpus all -p 8080:80 \
    ghcr.io/huggingface/text-generation-inference:latest \
    --model-id meta-llama/Llama-2-7b-chat-hf \
    --num-shard 2

# With custom batching configuration
docker run --gpus all -p 8080:80 \
    -v $PWD/data:/data \
    ghcr.io/huggingface/text-generation-inference:latest \
    --model-id meta-llama/Llama-2-7b-chat-hf \
    --max-batch-total-tokens 4096 \
    --num-shard 1 \
    --port 80

TGI Client Implementation

TGI provides a REST API (not OpenAI-compatible by default):

import requests
import json
from typing import List, Dict, Any

class TGIClient:
    """
    Client for interacting with TGI server.
    TGI provides a REST API (not OpenAI-compatible by default).
    """
    
    def __init__(self, base_url: str = "http://localhost:8080"):
        self.base_url = base_url
    
    def generate(
        self,
        prompt: str,
        max_new_tokens: int = 100,
        temperature: float = 0.7,
        top_p: float = 0.95,
        top_k: int = 50,
        do_sample: bool = True,
        stream: bool = False
    ) -> str:
        """
        Generate text using TGI.
        
        Args:
            prompt: Input prompt
            max_new_tokens: Maximum tokens to generate
            temperature: Sampling temperature
            top_p: Nucleus sampling parameter
            top_k: Top-k sampling parameter
            do_sample: Whether to use sampling
            stream: Whether to stream the response
        """
        url = f"{self.base_url}/generate"
        payload = {
            "inputs": prompt,
            "parameters": {
                "max_new_tokens": max_new_tokens,
                "temperature": temperature,
                "top_p": top_p,
                "top_k": top_k,
                "do_sample": do_sample
            }
        }
        
        if stream:
            payload["stream"] = True
            response = requests.post(url, json=payload, stream=True)
            full_response = ""
            for line in response.iter_lines():
                if line:
                    data = json.loads(line)
                    if "token" in data:
                        full_response += data["token"]["text"]
            return full_response
        else:
            response = requests.post(url, json=payload)
            response.raise_for_status()
            result = response.json()
            return result["generated_text"]
    
    def batch_generate(
        self,
        prompts: List[str],
        max_new_tokens: int = 100,
        temperature: float = 0.7
    ) -> List[str]:
        """
        Generate text for multiple prompts using TGI's batching.
        """
        results = []
        for prompt in prompts:
            result = self.generate(
                prompt=prompt,
                max_new_tokens=max_new_tokens,
                temperature=temperature
            )
            results.append(result)
        return results

Part 3: vLLM Speed Optimization Strategies

1. Continuous Batching (Automatic)

vLLM automatically batches requests for optimal throughput:

# Tune batching with max-num-seqs parameter
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-2-7b-chat-hf \
    --max-num-seqs 256 \
    --port 8000

Configuration Guide:

Higher max-num-seqs = More throughput, more memory usage
Typical values: 64-256 depending on GPU memory
Monitor GPU memory utilization to find optimal value

2. PagedAttention (Automatic)

PagedAttention is vLLM’s key innovation for efficient memory management:

No configuration needed - automatically enabled
Eliminates memory fragmentation
Enables higher batch sizes
Up to 24x higher throughput vs traditional methods

3. Quantization for Speed

Quantization reduces model size and increases inference speed:

# AWQ quantization (recommended for speed)
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-2-7b-chat-hf \
    --quantization awq \
    --port 8000

# GPTQ quantization
python -m vllm.entrypoints.openai.api_server \
    --model TheBloke/Llama-2-7B-Chat-GPTQ \
    --quantization gptq \
    --port 8000

Benefits:

2-3x speedup
4x memory reduction
Minimal quality loss (<2% typically)

4. Tensor Parallelism (Multi-GPU)

Distribute model across multiple GPUs:

# Use 2 GPUs
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-2-7b-chat-hf \
    --tensor-parallel-size 2 \
    --port 8000

# Use 4 GPUs for larger models
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-2-70b-chat-hf \
    --tensor-parallel-size 4 \
    --port 8000

Performance:

Near-linear scaling up to 4 GPUs
Best for large models (70B+)
Requires compatible GPUs

5. KV Cache Optimization

Optimize GPU memory utilization for faster inference:

python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-2-7b-chat-hf \
    --gpu-memory-utilization 0.95 \
    --port 8000

Tuning Guide:

Default: 0.9 (90% of GPU memory)
Higher values (0.95) = More caching, faster inference
Leave some headroom to prevent OOM errors

6. Complete Optimization Example

Here’s a fully optimized vLLM configuration:

# Maximum speed configuration
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-2-7b-chat-hf \
    --quantization awq \
    --tensor-parallel-size 2 \
    --max-num-seqs 256 \
    --gpu-memory-utilization 0.95 \
    --port 8000

Expected Performance:

3-5x faster than baseline
50-100 requests/second (depending on prompt length)
<100ms latency for short prompts

Part 4: TGI Speed Optimization Strategies

1. Continuous Batching

TGI automatically batches requests:

docker run --gpus all -p 8080:80 \
    ghcr.io/huggingface/text-generation-inference:latest \
    --model-id meta-llama/Llama-2-7b-chat-hf \
    --max-batch-total-tokens 4096

Configuration:

Higher max-batch-total-tokens = More throughput
Typical values: 2048-8192

2. Flash Attention (Automatic)

Flash Attention is enabled by default for supported models:

No configuration needed
2-4x faster attention computation
Reduced memory usage
Works with most modern architectures

3. Quantization

TGI supports multiple quantization methods:

# bitsandbytes (4-bit or 8-bit)
docker run --gpus all -p 8080:80 \
    ghcr.io/huggingface/text-generation-inference:latest \
    --model-id meta-llama/Llama-2-7b-chat-hf \
    --quantize bitsandbytes

# GPTQ quantization
docker run --gpus all -p 8080:80 \
    ghcr.io/huggingface/text-generation-inference:latest \
    --model-id TheBloke/Llama-2-7B-Chat-GPTQ \
    --quantize gptq

# AWQ quantization
docker run --gpus all -p 8080:80 \
    ghcr.io/huggingface/text-generation-inference:latest \
    --model-id TheBloke/Llama-2-7B-Chat-AWQ \
    --quantize awq

4. Model Sharding (Multi-GPU)

Distribute model across GPUs:

# Use 2 GPUs
docker run --gpus all -p 8080:80 \
    ghcr.io/huggingface/text-generation-inference:latest \
    --model-id meta-llama/Llama-2-7b-chat-hf \
    --num-shard 2

5. Token Streaming

Stream tokens for better perceived latency:

# Client code for streaming
def generate_stream(self, prompt: str):
    url = f"{self.base_url}/generate_stream"
    payload = {
        "inputs": prompt,
        "parameters": {"max_new_tokens": 100}
    }
    
    response = requests.post(url, json=payload, stream=True)
    for line in response.iter_lines():
        if line:
            data = json.loads(line)
            if "token" in data:
                yield data["token"]["text"]

6. Complete Optimization Example

# Maximum speed configuration
docker run --gpus all -p 8080:80 \
    ghcr.io/huggingface/text-generation-inference:latest \
    --model-id meta-llama/Llama-2-7b-chat-hf \
    --quantize bitsandbytes \
    --num-shard 2 \
    --max-batch-total-tokens 4096 \
    --port 80

Part 5: Performance Benchmarking

Benchmarking Framework

import statistics

def benchmark_inference_speed(
    client,
    prompts: List[str],
    num_runs: int = 5
) -> Dict[str, Any]:
    """
    Benchmark inference speed with multiple runs.
    
    Args:
        client: vLLM or TGI client
        prompts: List of prompts to test
        num_runs: Number of benchmark runs
    """
    latencies = []
    throughputs = []
    
    for run in range(num_runs):
        start_time = time.time()
        
        if isinstance(client, VLLMClient):
            results = client.async_batch_generate(prompts)
        elif isinstance(client, TGIClient):
            results = client.batch_generate(prompts)
        else:
            raise ValueError("Unknown client type")
        
        end_time = time.time()
        total_time = end_time - start_time
        
        latencies.append(total_time / len(prompts))
        throughputs.append(len(prompts) / total_time)
    
    return {
        "avg_latency": statistics.mean(latencies),
        "std_latency": statistics.stdev(latencies) if len(latencies) > 1 else 0,
        "avg_throughput": statistics.mean(throughputs),
        "std_throughput": statistics.stdev(throughputs) if len(throughputs) > 1 else 0,
        "min_latency": min(latencies),
        "max_latency": max(latencies),
        "num_prompts": len(prompts),
        "num_runs": num_runs
    }

Speed Comparison Utility

class InferenceSpeedOptimizer:
    """
    Demonstrates various techniques to improve inference speed.
    """
    
    @staticmethod
    def measure_latency(func, *args, **kwargs) -> Dict[str, float]:
        """
        Measure the latency of a function call.
        """
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        
        return {
            "result": result,
            "latency": end_time - start_time,
            "tokens_per_second": len(result.split()) / (end_time - start_time) if result else 0
        }
    
    @staticmethod
    def measure_throughput(func, prompts: List[str], *args, **kwargs) -> Dict[str, float]:
        """
        Measure throughput (requests per second) for batch processing.
        """
        start_time = time.time()
        results = func(prompts, *args, **kwargs)
        end_time = time.time()
        
        total_time = end_time - start_time
        return {
            "results": results,
            "total_time": total_time,
            "throughput": len(prompts) / total_time,
            "avg_latency": total_time / len(prompts)
        }

Part 6: Optimization Configuration Examples

vLLM Configurations

def vllm_optimization_examples():
    """
    Example vLLM server configurations for different optimization goals.
    """
    examples = {
        "max_speed": {
            "command": """python -m vllm.entrypoints.openai.api_server \\
    --model meta-llama/Llama-2-7b-chat-hf \\
    --quantization awq \\
    --tensor-parallel-size 2 \\
    --max-num-seqs 256 \\
    --gpu-memory-utilization 0.95 \\
    --port 8000""",
            "description": "Maximum speed: quantization + tensor parallelism + high batching"
        },
        
        "balanced": {
            "command": """python -m vllm.entrypoints.openai.api_server \\
    --model meta-llama/Llama-2-7b-chat-hf \\
    --max-num-seqs 128 \\
    --gpu-memory-utilization 0.9 \\
    --port 8000""",
            "description": "Balanced: good speed and quality"
        },
        
        "low_memory": {
            "command": """python -m vllm.entrypoints.openai.api_server \\
    --model meta-llama/Llama-2-7b-chat-hf \\
    --quantization awq \\
    --max-num-seqs 64 \\
    --gpu-memory-utilization 0.8 \\
    --port 8000""",
            "description": "Low memory: quantization + reduced batching"
        },
        
        "high_quality": {
            "command": """python -m vllm.entrypoints.openai.api_server \\
    --model meta-llama/Llama-2-7b-chat-hf \\
    --max-num-seqs 64 \\
    --gpu-memory-utilization 0.85 \\
    --dtype float16 \\
    --port 8000""",
            "description": "High quality: no quantization, lower batching"
        }
    }
    
    return examples

TGI Configurations

def tgi_optimization_examples():
    """
    Example TGI server configurations for different optimization goals.
    """
    examples = {
        "max_speed": {
            "command": """docker run --gpus all -p 8080:80 \\
    ghcr.io/huggingface/text-generation-inference:latest \\
    --model-id meta-llama/Llama-2-7b-chat-hf \\
    --quantize bitsandbytes \\
    --num-shard 2 \\
    --max-batch-total-tokens 4096 \\
    --port 80""",
            "description": "Maximum speed: quantization + sharding + high batching"
        },
        
        "balanced": {
            "command": """docker run --gpus all -p 8080:80 \\
    ghcr.io/huggingface/text-generation-inference:latest \\
    --model-id meta-llama/Llama-2-7b-chat-hf \\
    --num-shard 1 \\
    --max-batch-total-tokens 2048 \\
    --port 80""",
            "description": "Balanced: good speed and quality"
        },
        
        "low_memory": {
            "command": """docker run --gpus all -p 8080:80 \\
    ghcr.io/huggingface/text-generation-inference:latest \\
    --model-id meta-llama/Llama-2-7b-chat-hf \\
    --quantize bitsandbytes \\
    --num-shard 1 \\
    --max-batch-total-tokens 1024 \\
    --port 80""",
            "description": "Low memory: quantization + reduced batching"
        },
        
        "high_quality": {
            "command": """docker run --gpus all -p 8080:80 \\
    ghcr.io/huggingface/text-generation-inference:latest \\
    --model-id meta-llama/Llama-2-7b-chat-hf \\
    --num-shard 1 \\
    --max-batch-total-tokens 1024 \\
    --dtype float16 \\
    --port 80""",
            "description": "High quality: no quantization, lower batching"
        }
    }
    
    return examples

Part 7: Best Practices for Speed Optimization

15 Essential Optimization Techniques

Choose the Right Inference Server
- vLLM: Best for high-throughput batch processing
- TGI: Best for low-latency real-time applications
- Both support continuous batching and modern optimizations
Use Quantization
- 4-bit quantization (AWQ, GPTQ, bitsandbytes)
- 2-3x speedup with 4x memory reduction
- Minimal quality loss (<2% typically)
- AWQ recommended for vLLM, bitsandbytes for TGI
Optimize Batching
- Increase max-num-seqs (vLLM) or max-batch-total-tokens (TGI)
- Higher values = more throughput
- Monitor GPU memory usage
- Find sweet spot for your hardware
Use Multiple GPUs
- Tensor parallelism (vLLM) or sharding (TGI)
- 2-4x speedup with 2-4 GPUs
- Best for models 13B+
- Requires compatible GPUs
Optimize KV Cache
- Increase gpu-memory-utilization (vLLM)
- Increase max-total-tokens (TGI)
- More cache = faster inference
- Balance with available memory
Use Async Requests
- Send multiple requests concurrently
- Leverage continuous batching
- Use asyncio or threading
- Don’t wait for sequential processing
Stream Responses
- Better perceived latency
- Users see results faster
- Reduces time-to-first-token
- Improves user experience
Choose Appropriate Model Size
- Smaller models = faster inference
- 7B models for most tasks
- 13B for complex reasoning
- 70B only when necessary
Monitor Resource Usage
- Watch GPU memory utilization
- Monitor GPU compute usage
- Adjust parameters based on bottlenecks
- Use nvidia-smi for monitoring
Cache Frequent Prompts
- Cache identical prompts
- Use memoization or Redis
- Reduces redundant computation
- Significant speedup for repeated queries
Optimize Prompt Length
- Shorter prompts = faster inference
- Remove unnecessary context
- Use prompt compression techniques
- KV cache size matters
Tune Generation Parameters
- Lower max_tokens = faster inference
- Adjust temperature, top_p, top_k
- Use greedy decoding (temperature=0) for speed
- Balance quality vs speed
Use Appropriate Data Types
- float16 instead of float32
- Reduces memory usage
- Increases speed
- Minimal quality impact
Benchmark and Iterate
- Test different configurations
- Measure latency and throughput
- Optimize for your specific workload
- Use profiling tools
Hardware Considerations
- Use latest GPU architectures (A100, H100)
- Ensure sufficient PCIe bandwidth
- Use NVMe storage for model loading
- Consider CPU-GPU memory transfer

Part 8: Complete Working Example

def example_usage():
    """
    Complete example demonstrating vLLM and TGI usage.
    """
    print("=" * 80)
    print("LLM Inference Hosting Tutorial - Example Usage")
    print("=" * 80)
    
    # Example prompts for testing
    prompts = [
        "Explain machine learning in one sentence.",
        "What is the capital of France?",
        "Write a haiku about programming.",
    ]
    
    # vLLM Example
    print("\n1. vLLM Client Example:")
    print("-" * 80)
    try:
        vllm_client = VLLMClient()
        result = vllm_client.generate(
            prompt="What is Python?",
            max_tokens=50,
            temperature=0.7
        )
        print(f"Result: {result}")
        
        # Batch generation
        print("\nBatch generation:")
        results = vllm_client.async_batch_generate(prompts)
        for i, result in enumerate(results):
            print(f"\nPrompt {i+1}: {result[:100]}...")
            
    except Exception as e:
        print(f"Error (vLLM server may not be running): {e}")
        print("\nStart vLLM server with:")
        print("python -m vllm.entrypoints.openai.api_server \\")
        print("  --model meta-llama/Llama-2-7b-chat-hf \\")
        print("  --port 8000")
    
    # TGI Example
    print("\n2. TGI Client Example:")
    print("-" * 80)
    try:
        tgi_client = TGIClient()
        result = tgi_client.generate(
            prompt="What is Python?",
            max_new_tokens=50,
            temperature=0.7
        )
        print(f"Result: {result}")
        
        # Batch generation
        print("\nBatch generation:")
        results = tgi_client.batch_generate(prompts)
        for i, result in enumerate(results):
            print(f"\nPrompt {i+1}: {result[:100]}...")
            
    except Exception as e:
        print(f"Error (TGI server may not be running): {e}")
        print("\nStart TGI server with Docker:")
        print("docker run --gpus all -p 8080:80 \\")
        print("  ghcr.io/huggingface/text-generation-inference:latest \\")
        print("  --model-id meta-llama/Llama-2-7b-chat-hf")
    
    # Optimization Examples
    print("\n3. Optimization Configuration Examples:")
    print("-" * 80)
    
    print("\nvLLM Optimizations:")
    vllm_examples = vllm_optimization_examples()
    for name, config in vllm_examples.items():
        print(f"\n{name.upper()}:")
        print(f"  {config['description']}")
        print(f"  {config['command']}")
    
    print("\n\nTGI Optimizations:")
    tgi_examples = tgi_optimization_examples()
    for name, config in tgi_examples.items():
        print(f"\n{name.upper()}:")
        print(f"  {config['description']}")
        print(f"  {config['command']}")
    
    print("\n" + "=" * 80)


if __name__ == "__main__":
    example_usage()

Part 9: vLLM vs TGI Performance Comparison

When to Choose vLLM

Advantages:

✅ Higher throughput for batch processing (2-3x)
✅ Better memory efficiency with PagedAttention
✅ Easier Python integration
✅ More flexible configuration options
✅ Better for high-concurrency scenarios
✅ Active development and community support

Best For:

API serving with high request volume
Batch processing workflows
Research and experimentation
Python-first environments

When to Choose TGI

Advantages:

✅ Lower latency for single requests (10-20% faster)
✅ Rust-based for maximum performance
✅ Better Docker integration
✅ Built-in Flash Attention
✅ Extensive quantization support
✅ Production-ready with Hugging Face backing

Best For:

Real-time chat applications
Interactive user experiences
Low-latency requirements
Docker/Kubernetes deployments

Performance Metrics (Same Hardware)

Metric	vLLM	TGI
Throughput (batch)	100-150 req/s	70-100 req/s
Single request latency	100-120ms	80-100ms
Memory efficiency	Excellent (PagedAttention)	Good
GPU utilization	95%+	90%+
Concurrent users	High (256+)	Medium (128+)

Recommendation

Choose vLLM if: You need maximum throughput and batch processing efficiency
Choose TGI if: You need lowest possible latency for real-time applications
Both are excellent: Production-ready, well-maintained, and highly optimized

Key Takeaways

Both frameworks are production-ready with excellent performance
Quantization is critical - 2-3x speedup with minimal quality loss
Continuous batching enables efficient multi-request processing
GPU utilization should be monitored and optimized
Choose based on use case: vLLM for throughput, TGI for latency
Start simple, then optimize - measure before adding complexity
Hardware matters - invest in modern GPUs for best results
Cache aggressively - eliminate redundant computation
Stream for UX - improve perceived performance
Monitor and iterate - continuous optimization is key

With these techniques, you can achieve 3-5x speedup over baseline inference and serve hundreds of requests per second efficiently!

Introduction to vLLM and TGI

vLLM (Very Large Language Model)

TGI (Text Generation Inference)

Key Speed Optimization Techniques

Part 1: vLLM Setup and Installation

Installation

Starting vLLM Server - Basic Configuration

Advanced vLLM Server Configurations

vLLM Client Implementation

Part 2: TGI Setup and Installation

Installation with Docker (Recommended)

Advanced TGI Server Configurations

TGI Client Implementation

Part 3: vLLM Speed Optimization Strategies

1. Continuous Batching (Automatic)

2. PagedAttention (Automatic)

3. Quantization for Speed

4. Tensor Parallelism (Multi-GPU)

5. KV Cache Optimization

6. Complete Optimization Example

Part 4: TGI Speed Optimization Strategies

1. Continuous Batching

2. Flash Attention (Automatic)

3. Quantization

4. Model Sharding (Multi-GPU)

5. Token Streaming

6. Complete Optimization Example

Part 5: Performance Benchmarking

Benchmarking Framework

Speed Comparison Utility

Part 6: Optimization Configuration Examples

vLLM Configurations

TGI Configurations

Part 7: Best Practices for Speed Optimization

15 Essential Optimization Techniques

Part 8: Complete Working Example

Part 9: vLLM vs TGI Performance Comparison

When to Choose vLLM

When to Choose TGI

Performance Metrics (Same Hardware)

Recommendation

Key Takeaways

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