Unleash the full potential of LLMs: Optimize for performance with vLLM

Large language models (LLMs) are transforming industries, from customer service to cutting-edge applications, unlocking vast opportunities for innovation. Yet, their potential comes with a catch: high computational costs and complexity. Deploying LLMs often demands expensive hardware and intricate management, putting efficient, scalable solutions out of reach for many organizations. But what if you could harness LLM power without breaking the bank? Model compression and efficient inference with vLLM offer a game-changing answer, helping reduce costs and speed up deployment for businesses of all sizes.

The need for speed (and efficiency)

Running LLMs at scale is no small feat. These models crave powerful, costly hardware that drives up infrastructure expenses and operational headaches. The rise of real-time applications, like chatbots or multimodel workflows, only intensifies the pressure, demanding both speed and affordability. Optimization doesn’t just cut costs, it frees up engineering time, accelerates development cycles and lets teams focus on strategic priorities instead of hardware wrangling.

LLM compression: The key to efficient inference

Model compression tackles these challenges head-on by shrinking LLMs’ resource demands without compromising inference performance. Two standout techniques lead the charge:

• Quantization: This converts high-precision weights to lower-bit formats (e.g., FP8, INT8, INT4, etc.), slashing memory and compute needs. Neural Magic’s 500,000 evaluations on quantized LLMs show inference speedups of 2-4x on average, with accuracy drops as low as 0.5-1% (>99% recovery).
• Sparsity: This trims redundant parameters, making models leaner and faster. Fewer connections mean less storage and processing, simplifying deployment and reducing costs.

To push this vision forward, Red Hat recently acquired Neural Magic, a leader in LLM compression, reinforcing a commitment to fast, efficient inference on any hardware. Over the past year, Neural Magic has optimized popular models like Granite, Llama, Mistral, Qwen and others using cutting-edge quantization. These open source, inference-ready models are available on Hugging Face.

Multimodal model quantization support through LLM Compressor

For hands-on optimization, the open source LLM Compressor library offers:

• A rich set of quantization algorithms for weights and activations
• Integration with Hugging Face models and repositories
• Support for safetensors, a simple format for storing tensors safely that is compatible with vLLM
• Large model handling via Accelerate
• Support for proven algorithms like GPTQ, SmoothQuant, SparseGPT and more

vLLM: Streamlining inference across diverse hardware

Compression is half of the battle—the other half is a high-performance inference engine. Enter vLLM, an open source library built from the ground up for faster, more flexible LLM serving. Born at UC Berkeley and nearing 40,000 GitHub stars, vLLM is a favorite in academia and industry alike. It’s not just about speed—it’s about making LLM deployment practical, scalable and accessible. Here’s what sets vLLM apart:

• High performance: With techniques like PagedAttention (optimizing memory for larger models by dynamically managing key-value caches), vLLM delivers higher throughput than traditional frameworks like Hugging Face Transformers, with near-zero latency overhead. This means your applications, from chatbots to real-time analytics, respond quickly and scale more easily. See recent vLLM benchmarks here and here.
• Broad hardware compatibility: From NVIDIA and AMD GPUs to Google TPUs, Intel Gaudi, AWS Neuron or even CPUs, vLLM adapts to your setup. It optimizes for diverse accelerators, letting you leverage existing infrastructure or choose cost-effective options without retooling. Check supported hardware across quantization methods here.
• Dynamic batching and scalability: vLLM’s advanced request handling batches incoming queries dynamically, maximizing resource use without manual tuning. This is important for high-traffic scenarios like customer support bots or multiuser AI platforms where demand fluctuates unpredictably.
• Easier deployment: vLLM simplifies LLM management with built-in serving endpoints compatible with OpenAI’s API format. Deploying a model is as easy as a single command - vllm serve [your model here], cutting operational overhead and letting your team focus on innovation, not infrastructure. It’s a shortcut to production-ready solutions.
• Customizability for experts: Beyond ease of use, vLLM offers hooks for advanced users like custom tokenizers, model sharding and fine-tuned optimization flags, making it a flexible tool for engineers pushing the boundaries of LLM applications.
• Open source and community-driven: Backed by the Linux Foundation and a thriving community, vLLM offers transparency, rapid feature updates and a wealth of support. Contributions from industry leaders and researchers help keep vLLM at the cutting edge, while extensive documentation lowers the learning curve.

Paired with compressed models, vLLM creates an end-to-end pipeline that’s faster, more affordable and easier to manage. Whether you’re serving a single chatbot or powering a sprawling AI ecosystem, vLLM scales with your ambitions, delivering performance without the complexity.

The bottom line: Embrace optimization with vLLM

LLMs promise a competitive edge, especially if you can tame their costs and complexity. Optimization and vLLM help make that possible, turning potential into profits and operational efficiencies. Expect lower operational costs (think 40-50% GPU savings), faster time-to-market with streamlined deployment and happier customers thanks to real-time responsiveness. Whether you’re scaling a startup or steering an enterprise, this combo lets you deploy AI smarter and cheaper!

The proof is in the results. A popular gaming company used Neural Magic’s INT8 quantized Llama 70B with vLLM to power hundreds of thousands of daily code generations, hitting 10 queries per second at 50ms per token. By halving GPU usage, they slashed infrastructure costs by 50% without sacrificing performance. 

Get started today

Ready to tap into optimized LLMs and vLLM’s power? Here’s how:

1. Explore optimized models: Dive into pre-optimized LLMs on Hugging Face here, ready for instant deployment.
2. Optimize your own models: Use LLM Compressor to experiment with compression techniques and tailor models to your needs.
3. Test drive vLLM: Run a sample inference to see its speed and simplicity in action.

For production-ready solutions, Red Hat’s experts can guide you. Contact us to learn how we can help your business harness LLMs efficiently and effectively.