Supercharging GPUs: Optimization Techniques for LLMs

Welcome back, future LLMOps maestros! In our previous chapters, we laid the groundwork for understanding LLM inference pipelines and how to set them up. We’ve seen that serving Large Language Models in production is a whole different ball game compared to traditional machine learning models. One of the biggest challenges? The sheer computational power and memory these models demand, especially from GPUs.

In this chapter, we’re diving deep into the exciting world of GPU optimization for LLMs. Our goal isn’t just to make models run, but to make them fly – faster, more efficiently, and at a lower cost. We’ll explore cutting-edge techniques that can dramatically reduce latency and boost throughput, turning your GPU infrastructure into a lean, mean, inference machine.

By the end of this chapter, you’ll understand:

• Why LLMs are so demanding on GPUs.
• How quantization can shrink models and speed up computation.
• The magic of continuous batching for maximizing GPU utilization.
• The role of specialized inference runtimes like vLLM and TensorRT-LLM.
• Practical steps to start implementing these optimizations.

Ready to unleash the full potential of your GPUs? Let’s get started!

The GPU Challenge: Why LLMs are Different

Before we optimize, let’s truly understand why LLMs pose such a unique challenge for GPUs. It’s not just about model size, though that’s a huge factor!

1. Massive Model Sizes: LLMs often have billions of parameters. Storing these parameters requires a significant amount of GPU memory (VRAM). A 7-billion parameter model in FP16 (half-precision floating point) can easily consume 14GB of VRAM just for the model weights. Larger models, like 70B, demand hundreds of GBs. This directly impacts how many models you can fit on a single GPU or how many GPUs a single model needs.

2. Memory Bandwidth Intensive: Inference involves constantly loading these parameters and intermediate activations from VRAM to the GPU’s processing cores. This makes LLM inference often memory-bandwidth bound rather than compute-bound. Think of it like a highway: even if you have super-fast cars (compute cores), if the highway itself (memory bandwidth) is narrow, traffic will slow down.

3. Sequential Token Generation: Unlike classification, where you get one output, LLMs generate text token by token. Each token requires a forward pass through the entire network, and the process is inherently sequential. This “auto-regressive” nature makes it hard to parallelize fully across a single request, creating latency challenges.

4. KV Cache Explosion: For the attention mechanism (the “A” in Transformer), the model needs to remember previous tokens’ “keys” and “values” (the KV cache). As the output sequence grows, this KV cache also grows, consuming more and more VRAM. For long contexts, the KV cache can become a significant memory bottleneck, limiting the number of concurrent requests a GPU can handle.

5. Variable Output Lengths: Different user prompts lead to different response lengths. This variability makes static resource allocation inefficient, as you often have to provision for the worst-case scenario (longest possible output) even for short responses, leading to wasted resources.

These factors combined mean that simply throwing more powerful GPUs at the problem isn’t always the most efficient or cost-effective solution. We need smarter software and algorithmic approaches!

Slimming Down: Quantization for Performance and Cost

Imagine you have a giant library, and you need to carry all the books home. What if you could magically shrink each book to half its size without losing any important content? That’s essentially what quantization aims to do for LLMs.

What is Quantization?

Quantization is a technique that reduces the precision of the numerical representations of a model’s weights and activations. Most LLMs are trained using 32-bit floating-point numbers (FP32) or 16-bit floating-point numbers (FP16 or BF16). Quantization converts these to lower precision formats, such as 8-bit integers (INT8) or even 4-bit integers (INT4).

Why is it Important?

1. Reduced Memory Footprint: Lower precision numbers require less memory to store. An INT4 model is roughly 4x smaller than an FP16 model. This means you can fit larger models onto smaller GPUs, or more models onto the same GPU, directly impacting your hardware costs.
2. Faster Computation: Modern GPUs and specialized hardware (like NVIDIA’s Tensor Cores) are highly optimized for lower-precision arithmetic. This can lead to significant speedups during inference, as the GPU can process more data per clock cycle.
3. Lower Costs: Smaller memory footprint and faster computation often translate directly to lower GPU costs, as you might need fewer or less expensive GPUs, or pay less for cloud GPU instances.

The Trade-off: Accuracy

The main challenge with quantization is preserving model accuracy. Reducing precision can sometimes lead to a slight degradation in performance. Researchers are constantly developing new quantization techniques that minimize this accuracy loss, often by carefully selecting which values to quantize or how to rescale them.

Common Quantization Techniques (as of 2026):

• GPTQ (GPT-Q): A popular post-training quantization (PTQ) method that quantizes a model to 4-bit precision with minimal accuracy loss. It’s often used for static quantization, meaning the model is quantized once and then used for inference.
• AWQ (Activation-aware Weight Quantization): Another PTQ method that focuses on preserving accuracy by optimizing quantization for weights based on their activation patterns. It aims to reduce the “outlier” problem where a few extreme values can disproportionately affect quantization.
• QLoRA (Quantized Low-Rank Adaptation): While primarily a fine-tuning technique, QLoRA enables fine-tuning large models in 4-bit precision, making it possible to adapt huge LLMs on consumer-grade GPUs. The resulting LoRA adapters can then be merged or used with quantized base models, offering both fine-tuning and inference benefits.

When choosing a quantization method, you’ll often need to experiment to find the best balance between speed, memory reduction, and acceptable accuracy for your specific use case. It’s not a one-size-fits-all solution!

Maximizing Throughput: The Power of Continuous Batching

Remember how we said LLMs generate tokens sequentially? This makes traditional static batching (where you wait for a fixed number of requests to arrive before processing them together) inefficient. Why? Because requests often have different input and output lengths. With static batching, you have to pad shorter sequences to match the longest one, wasting precious GPU cycles and memory.

Enter Continuous Batching, also known as Dynamic Batching or PagedAttention. This technique is a game-changer for LLM inference throughput.

How Traditional Static Batching Works (and Fails):

Imagine you have three friends who want to go on a roller coaster. With static batching, you wait until you have a full car (say, 4 seats). If one friend is very tall and needs extra legroom, everyone else has to stretch out too, wasting space. And if only 3 friends show up, you still wait for a 4th, or send a half-empty car.

In this diagram, notice how the GPU waits for a full batch and then processes padded sequences, leading to wasted computation.

What is Continuous Batching?

Instead of waiting for a full, fixed-size batch, continuous batching processes requests as soon as they arrive and become available. It dynamically adds new requests to the GPU’s processing queue and removes completed ones. The key insight is that while each request generates tokens sequentially, multiple requests can generate their next token concurrently on the GPU. This means the GPU is always busy doing useful work.

The Magic of PagedAttention (vLLM’s Innovation):

A core component of continuous batching is efficient KV cache management. PagedAttention, introduced by vLLM, handles the KV cache in a way similar to how operating systems handle virtual memory paging.

• Memory Blocks: The KV cache for each request is broken down into fixed-size “blocks.”
• Dynamic Allocation: These blocks are allocated and deallocated dynamically as needed, rather than reserving a large contiguous chunk for the maximum possible sequence length. This prevents over-provisioning memory for shorter sequences.
• Sharing: It can even share KV cache blocks between different requests if they share a common prompt prefix (e.g., in a multi-turn conversation or when using a shared system prompt), leading to further memory savings.

Benefits of Continuous Batching:

• Higher Throughput: Maximizes GPU utilization by keeping the GPU busy with useful work, significantly increasing the number of tokens generated per second.
• Lower Latency: New requests can start processing almost immediately without waiting for a full batch, reducing perceived latency for individual users.
• Reduced GPU Memory Waste: No more padding sequences with idle tokens. Efficient KV cache management further saves VRAM, allowing more concurrent requests.

Notice how requests dynamically enter and leave the processing queue, and the GPU is always working on generating actual tokens, not padding. This leads to much higher efficiency.

Specialized LLM Inference Runtimes: The Software Superchargers

While quantization and batching are powerful techniques, implementing them efficiently from scratch is incredibly complex. Fortunately, the LLM community has developed highly optimized inference runtimes that do the heavy lifting for us. These frameworks are designed from the ground up to squeeze every drop of performance out of GPUs for LLMs.

Let’s look at some of the leading contenders:

1. vLLM

vLLM is an open-source library for fast LLM inference that has quickly become a community favorite due to its impressive performance and ease of use. Its core innovation is PagedAttention, which we just discussed.

Key Features of vLLM:

• PagedAttention: Achieves high throughput by efficiently managing the KV cache, significantly reducing memory waste.
• Continuous Batching: Dynamically batches incoming requests, maximizing GPU utilization by keeping the GPU busy with real work.
• Optimized CUDA Kernels: Implements highly optimized low-level CUDA code for common LLM operations, taking full advantage of NVIDIA GPU architecture.
• Support for various models: Compatible with a wide range of Hugging Face models, making it easy to swap out different LLMs.
• Distributed Inference: Supports running large models across multiple GPUs or even multiple nodes.
• Quantization Support: Integrates with common quantization techniques like GPTQ and AWQ.

vLLM provides a simple API to serve LLMs, making it relatively easy to deploy and achieve significant performance gains, often outperforming other solutions for raw throughput.

2. NVIDIA TensorRT-LLM

TensorRT-LLM is an open-source library from NVIDIA specifically designed to optimize and accelerate LLM inference on NVIDIA GPUs. It’s built on top of NVIDIA’s TensorRT deep learning optimizer, which is renowned for its performance.

Key Features of TensorRT-LLM:

• Graph Optimization: Converts LLM models into highly optimized TensorRT engines, which fuse operations, select optimal kernels, and apply advanced optimizations tailored for NVIDIA hardware. This is a deep, hardware-aware optimization.
• Quantization: Strong support for various quantization methods, including INT8 and INT4, leveraging NVIDIA’s Tensor Cores for maximum acceleration.
• In-flight Batching (Continuous Batching): Implements dynamic batching similar to vLLM to maximize throughput.
• Custom Kernels: Utilizes highly optimized CUDA kernels for LLM-specific operations (e.g., FlashAttention, custom attention mechanisms).
• Multi-GPU / Multi-Node Inference: Designed for scalable deployment across multiple GPUs and servers, crucial for very large models.
• Support for many models: Integrates with popular models from Hugging Face.

TensorRT-LLM often offers the highest performance on NVIDIA hardware due to its deep integration with the underlying GPU architecture and specialized optimizations. It requires a bit more effort to set up compared to vLLM, as it involves an explicit “build” step to create the optimized engine.

3. Text Generation Inference (TGI)

Text Generation Inference (TGI) is another popular open-source solution, developed by Hugging Face, specifically for production-ready LLM inference. It’s often used by those who are already deeply integrated into the Hugging Face ecosystem.

Key Features of TGI:

• Rust Backend: Written in Rust for performance and memory safety, offering a robust foundation.
• Continuous Batching: Efficiently manages requests and KV cache, similar to vLLM.
• Quantization Support: Integrates with quantization techniques like bitsandbytes.
• Streaming Output: Supports streaming generated tokens back to the client, improving perceived latency for users waiting for responses.
• Load Balancing and Scaling: Designed for deployment in Kubernetes with features like token-level load balancing across multiple instances.
• Web API: Provides a straightforward HTTP API for inference requests, making client integration simple.

TGI offers a robust and feature-rich solution, particularly well-suited for those already in the Hugging Face ecosystem, providing a comprehensive solution from model hub to serving.

Choosing the Right Runtime:

• vLLM: An excellent general-purpose choice, easy to use, and often provides great performance due to PagedAttention. Ideal for quick deployment and high throughput.
• TensorRT-LLM: If you’re exclusively on NVIDIA GPUs and need the absolute highest performance, especially with large models and specific quantization needs, TensorRT-LLM is often the top performer. It requires a bit more setup and familiarity with NVIDIA’s ecosystem.
• TGI: A strong contender, especially if you value streaming, a robust API, and deep integration with Hugging Face’s model ecosystem.

Many organizations often benchmark these runtimes with their specific models and workloads to determine the best fit for their latency, throughput, and cost requirements.

Step-by-Step Implementation: Serving with vLLM

Let’s get our hands (conceptually) dirty! We’ll set up a basic vLLM server to see how easy it is to leverage these optimizations. For this exercise, we’ll assume you have a Python environment and ideally access to a CUDA-enabled GPU.

Prerequisites:

• Python 3.8+
• pip package manager
• (Recommended for optimal performance) A CUDA-enabled GPU with NVIDIA drivers installed (e.g., CUDA Toolkit 12.1+ for recent vLLM versions). vLLM can run on CPU, but performance will be significantly slower and won’t showcase the GPU optimizations.

Step 1: Install vLLM

First, we need to install the vllm library. It’s best practice to do this in a virtual environment to avoid conflicts with other Python projects.

Open your terminal and run:

# Create a virtual environment (optional but highly recommended)
python -m venv vllm_env
source vllm_env/bin/activate # On Windows, use: .\vllm_env\Scripts\activate

# Install vLLM
# As of 2026-03-20, vLLM's latest stable release is often available directly via pip.
# Ensure your CUDA toolkit version matches vLLM's requirements for optimal GPU support.
# If you encounter issues, refer to the official vLLM GitHub for specific CUDA/Python versions:
# https://github.com/vllm-project/vllm
pip install vllm

Explanation:

• python -m venv vllm_env: This command creates a new, isolated Python environment named vllm_env. This prevents conflicts between different project dependencies.
• source vllm_env/bin/activate: This activates the virtual environment. All subsequent pip and python commands will operate within this isolated environment.
• pip install vllm: This installs the vllm library and its dependencies. If you have a GPU and CUDA is properly configured, vllm will automatically try to install the CUDA-enabled versions of its dependencies, which are crucial for GPU acceleration.

Step 2: Start the vLLM API Server

Now, let’s start a basic API server using a pre-trained LLM. We’ll use a smaller model like microsoft/phi-2 for demonstration, as it’s efficient for local testing and provides a good balance of size and capability.

Create a file named start_vllm_server.sh (or start_vllm_server.bat for Windows) and add the following:

#!/bin/bash

# Ensure the virtual environment is active if you're not running this from an already activated shell.
# source vllm_env/bin/activate # Uncomment this line if you need to activate the venv within the script.

echo "Starting vLLM OpenAI-compatible API server..."

# Start the vLLM OpenAI-compatible API server
# Using 'microsoft/phi-2' as an example small, efficient model.
# Adjust --model to any model available on Hugging Face Hub if you want to try others.
# --port 8000 is the default for vLLM, but explicitly setting it is good practice.
# --gpu-memory-utilization can be adjusted to control how much GPU memory vLLM uses.
# --dtype auto lets vLLM pick the best precision (e.g., bfloat16 if supported, else float16).
python -m vllm.entrypoints.api_server \
    --model microsoft/phi-2 \
    --port 8000 \
    --dtype auto \
    --gpu-memory-utilization 0.90 \
    --enforce-eager # Optional: for debugging or if you encounter issues with compiled kernels.

Explanation:

• python -m vllm.entrypoints.api_server: This command tells Python to run the API server module that vllm provides, which exposes an OpenAI-compatible API.
• --model microsoft/phi-2: This crucial argument specifies the Hugging Face model to load. phi-2 is a good choice for quick testing as it’s relatively small (2.7B parameters) and performs well.
• --port 8000: The port on which the API server will listen for incoming HTTP requests.
• --dtype auto: This tells vLLM to automatically select the most efficient data type for the model based on your hardware. For modern NVIDIA GPUs, this will often default to bfloat16 if supported, otherwise float16. This is a form of automatic mixed-precision.
• --gpu-memory-utilization 0.90: This is a crucial optimization parameter. It tells vLLM to use up to 90% of the available GPU memory. This leaves some room for the operating system or other processes, but ensures vLLM gets most of the VRAM for its KV cache and model weights, maximizing the number of concurrent requests it can handle.
• --enforce-eager: (Optional) This flag can be useful for debugging or if you encounter issues with vLLM’s optimized CUDA kernels. It forces PyTorch to execute operations eagerly rather than compiling them, which might provide more readable stack traces. Remove it for production for maximum performance.

Now, make the script executable and run it:

chmod +x start_vllm_server.sh
./start_vllm_server.sh

You should see output indicating that the model is being downloaded (if not cached) and loaded, and then the server starting up. This process might take a few moments depending on your internet speed and GPU.

Step 3: Send an Inference Request

While the server is running in its dedicated terminal, open a new terminal window (and activate your vllm_env virtual environment if you created one). We’ll use curl or a Python script to send a request to our newly running vLLM server.

Using curl (quick command-line test):

curl http://localhost:8000/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "microsoft/phi-2",
        "prompt": "Write a short story about a brave knight and a dragon.",
        "max_tokens": 100,
        "temperature": 0.7,
        "stream": false
    }'

Using Python (more programmatic control):

Create a file named send_request.py:

import requests
import json

# Define the API endpoint
API_URL = "http://localhost:8000/v1/completions"

# Define the payload for the request
payload = {
    "model": "microsoft/phi-2",
    "prompt": "Explain the concept of continuous batching in simple terms.",
    "max_tokens": 150,
    "temperature": 0.7,
    "top_p": 0.95,
    "stream": False # Set to True if you want to receive tokens as they are generated
}

# Set the headers for the request
headers = {
    "Content-Type": "application/json"
}

print(f"Sending request to {API_URL}...")

try:
    # Send the POST request
    response = requests.post(API_URL, headers=headers, data=json.dumps(payload))
    response.raise_for_status() # Raise an exception for HTTP errors (4xx or 5xx)

    # Parse and print the response
    result = response.json()
    print("\n--- Full Response ---")
    print(json.dumps(result, indent=2))

    # Extract and print the generated text from the first choice
    if result and result.get("choices"):
        generated_text = result["choices"][0]["text"]
        print("\n--- Generated Text ---")
        print(generated_text)

except requests.exceptions.RequestException as e:
    print(f"An error occurred: {e}")
    if 'response' in locals() and response is not None:
        print(f"Response status code: {response.status_code}")
        print(f"Response body: {response.text}")
except json.JSONDecodeError:
    print(f"Failed to decode JSON response. Raw response: {response.text}")

Run the Python script:

python send_request.py

Explanation:

• We’re sending a POST request to the /v1/completions endpoint, which is designed to be compatible with OpenAI’s API.
• prompt: This is the input text, or query, for the LLM.
• max_tokens: This parameter sets the maximum number of tokens the model should generate in its response.
• temperature: This controls the randomness of the output. A higher value (e.g., 1.0) makes the output more creative and varied, while a lower value (e.g., 0.1) makes it more deterministic and focused.
• top_p: Another parameter for controlling randomness, focusing on the most probable tokens that sum up to top_p probability.
• stream: If set to True, the server will send tokens back as they are generated, rather than waiting for the full response. This can improve perceived latency for users.

You should see the LLM’s generated response printed in your terminal! This simple setup uses vLLM’s built-in continuous batching and optimized kernels to serve the model efficiently. You’ve just deployed an optimized LLM inference endpoint!

Mini-Challenge: Experiment with Quantization (Conceptual)

While a full quantization setup for a custom model can be complex, let’s explore how you would configure vLLM to use an already quantized model from the Hugging Face Hub, even if you don’t run the full conversion yourself right now.

Challenge: Imagine you found a 4-bit quantized version of microsoft/phi-2 on Hugging Face, typically indicated by a suffix like -GPTQ or -AWQ in its name (e.g., TheBloke/phi-2-GPTQ). How would you modify the start_vllm_server.sh script to load and serve this quantized model?

Hint: Look for Hugging Face model names that explicitly mention quantization, and consider if vLLM has a parameter to specify the quantization method. Check the vLLM documentation for a --quantization flag.

What to Observe/Learn:

• How easy it is to switch between different model versions (including quantized ones) with vLLM.
• The impact of quantization on GPU memory usage (if you monitor nvidia-smi during loading). A quantized model should use significantly less VRAM.
• The potential slight difference in output quality compared to the full precision model (though for small models and good quantization, it might be hard to notice).

#!/bin/bash
echo "Starting vLLM OpenAI-compatible API server with GPTQ quantized model..."
python -m vllm.entrypoints.api_server \
    --model TheBloke/phi-2-GPTQ \
    --port 8000 \
    --dtype auto \
    --quantization gptq \
    --gpu-memory-utilization 0.90

Common Pitfalls & Troubleshooting

Even with powerful tools like vLLM and TensorRT-LLM, deploying LLMs efficiently can present challenges. Here are a few common pitfalls and how to approach them:

1. GPU Memory Errors (Out of Memory - OOM):

   • Symptom: Your server crashes with “CUDA out of memory” or similar VRAM exhaustion errors.
   • Causes:
     • Loading a model that’s simply too large for your GPU’s VRAM.
     • Generating very long sequences, leading to an excessively large KV cache.
     • Too many concurrent requests, exceeding the total KV cache capacity.
     • --gpu-memory-utilization set too high (e.g., 1.0), leaving no room for system processes or other overhead.
   • Solutions:
     • Quantization: This is your first line of defense. Use INT8 or INT4 models.
     • Smaller Models: Choose a smaller LLM that still meets your performance criteria.
     • Reduce max_tokens: Limit the maximum output length to control KV cache size.
     • Adjust gpu-memory-utilization: Lower this value (e.g., to 0.85 or 0.80) to leave more headroom.
     • Multi-GPU: Distribute the model across multiple GPUs (vLLM and TensorRT-LLM support this for very large models).
     • Optimize KV Cache: Ensure you’re using a runtime with PagedAttention or similar KV cache optimizations.

2. Suboptimal GPU Utilization (Low Throughput):

   • Symptom: nvidia-smi shows low GPU utilization (e.g., < 50%) but potentially high GPU memory usage, or requests are slow despite available GPU resources.
   • Causes:
     • Inefficient batching (e.g., static batching or very small batch sizes).
     • CPU bottlenecks in pre-processing, post-processing, or even the API server logic.
     • The model is too small to fully saturate the GPU, or your workload isn’t large enough.
     • Network latency between client and server, or slow client requests.
   • Solutions:
     • Continuous Batching: Ensure your inference runtime supports and is configured for continuous batching (like vLLM or TGI) to keep the GPU busy.
     • Increase Load: Send more concurrent requests to fully saturate the GPU and test its limits.
     • Profile: Use profiling tools (e.g., NVIDIA Nsight Systems, torch.profiler) to identify bottlenecks in your code or the inference stack.
     • Optimize Client: Ensure your client application isn’t the bottleneck by sending requests efficiently.

3. Latency vs. Throughput Trade-offs:

   • Symptom: You can achieve high throughput or low latency, but not both simultaneously at peak levels.
   • Causes: These two metrics are often inversely related. Maximizing throughput usually involves larger batches, which can increase the time for any single request to complete (as it waits for others in the batch).
   • Solutions:
     • Service Level Objectives (SLOs): Clearly define your SLOs for both latency (e.g., 99th percentile token generation time for a single request) and throughput (e.g., tokens per second under heavy load).
     • Prioritization: If low latency is critical for certain requests (e.g., real-time chat), consider dedicating resources or using a separate endpoint with smaller batching.
     • Experimentation: Benchmark different batching strategies, model configurations, and runtime parameters to find the optimal balance for your application’s specific needs.
     • Quantization: Can help improve both by making each token generation faster.

Summary

Phew! We’ve covered a lot of ground in supercharging our GPUs for LLM inference. Here’s a quick recap of the key takeaways:

• Unique LLM Challenges: LLMs are uniquely demanding on GPUs due to their massive size, memory bandwidth requirements, sequential token generation, and the ever-growing KV cache.
• Quantization: This powerful technique reduces model size and speeds up inference by lowering numerical precision (e.g., to INT4 or INT8) with minimal impact on accuracy, leading to significant cost savings.
• Continuous Batching: Essential for maximizing GPU utilization and throughput. It dynamically processes multiple requests, avoiding wasted computation from padding and efficiently managing the KV cache (e.g., with PagedAttention).
• Specialized LLM Inference Runtimes: Tools like vLLM, NVIDIA TensorRT-LLM, and Hugging Face TGI provide highly optimized software solutions. They abstract away much of the complexity of low-level GPU programming, offering out-of-the-box performance enhancements.
• Practical Implementation: We successfully set up a vLLM server to demonstrate how these optimizations are applied in a real-world serving scenario.
• Troubleshooting: Understanding common pitfalls like GPU OOM errors, low utilization, and the latency-throughput trade-off is crucial for robust LLMOps.

By applying these optimization techniques, you’re not just deploying LLMs; you’re deploying them with surgical precision and maximum efficiency. This mastery is a cornerstone of robust LLMOps, allowing you to deliver powerful AI capabilities at scale and within budget.

What’s next? Now that our GPUs are supercharged, we need to ensure our entire system can scale horizontally and handle increasing demand. In the next chapter, we’ll explore Scaling Strategies for LLM Inference, diving into techniques like auto-scaling, load balancing, and distributed serving. Get ready to think big!

References

• vLLM GitHub Repository: The official source for vLLM, including comprehensive documentation, installation guides, and examples. https://github.com/vllm-project/vllm
• NVIDIA TensorRT-LLM GitHub Repository: Official documentation and source for NVIDIA’s LLM inference optimizer, detailing its features and usage. https://github.com/NVIDIA/TensorRT-LLM
• Hugging Face Text Generation Inference (TGI) GitHub: Details on Hugging Face’s production-ready inference solution, including API reference and deployment guides. https://github.com/huggingface/text-generation-inference
• LLMOps workflows on Azure Databricks (Quantization): Discusses quantization in the context of LLMOps best practices and deployments on Azure. https://learn.microsoft.com/en-us/azure/databricks/machine-learning/mlops/llmops#quantization
• Architectural Approaches for AI and Machine Learning in Multitenant Systems (Microsoft Azure): Provides broader context on efficient AI serving and architectural considerations in cloud environments. https://learn.microsoft.com/en-us/azure/architecture/guide/multitenant/approaches/ai-machine-learning

This page is AI-assisted and reviewed. It references official documentation and recognized resources where relevant.