Behind the Stack Ep. 8 - Choosing the Right Inference Engine for Your LLM Deployment

Introduction: The Hidden Cost of Choosing the Wrong Inference Engine

Inference engines are the backbone of self-hosted LLM stacks. They’re responsible for turning model weights into real-time, token-by-token output.

But here's the trap: most people choose one based on benchmark scores - and completely miss the bigger picture.

In reality, the best inference engine for your deployment depends on who’s using it, where it’s running, and how often it’s being called. That means the trade-offs between engines like Llama.cpp and vLLM go far beyond just speed. While the Doubleword Stack supports all major inference engines, selecting the best one still depends on your specific workload characteristics.

In this guide, we break down:

• The two major deployment patterns for LLM inference
• What each pattern demands from your engine
• Which open-source projects are optimized for each
• And how to choose the right engine for your stack

Deployment Type 1: Local Inference Engines (Personal, On-Device)

This setup powers everything from chatbots on your laptop to private models on mobile devices. These deployments are typically:

• Run by a single user
• Deployed on unpredictable or low-power hardware
• Latency-sensitive, but not throughput-bound

Key priorities here include:

• Portability: The engine should run across CPUs, GPUs, ARM chips, etc.
• Lightweight binaries: Users shouldn’t have to download massive CUDA libraries.
• Memory efficiency: Devices are RAM- and VRAM-limited.
• Fast decoding: Users care more about how fast tokens appear, not how many you can serve per second.

Popular inference engines for this use case:

• Llama.cpp: Written in portable C/C++ with hand-tuned kernels for CPU and basic GPU support.
• Mistral.rs and similar projects: Built for maximum portability with smaller binaries.

These engines often support formats like GGUF, which combine portability with powerful weight-only quantization. This makes them great for:

• Chatbots
• Personal agent tools
• Running LLMs on consumer-grade hardware

Deployment Type 2: Multi-Tenant Inference Engines (Server, API, Data Center)

Multi-tenant deployments are about serving many users efficiently, often across fleets of GPUs.

Common characteristics of this setup:

• Deployed on a few powerful machines (e.g., A100s or H100s)
• Designed for high concurrency
• Throughput and cost-per-token are top priorities
• Portability is irrelevant - performance is king

Performance goals in this context include:

• Throughput: Maximize tokens/sec across users
• Latency: Optimize time-to-first-token and inter-token timing
• Concurrency: Efficiently batch and serve multiple requests in parallel

Inference engines built for this include:

• vLLM: Optimized for fast decoding and KV cache reuse
• SGLang: Designed for batched tool-using agents and workflows
• TensorRT-LLM (NIMs): NVIDIA’s high-performance engine with support for FP8, tensor cores, and FlashAttention

These engines make full use of:

• Vendor-supplied matrix multiplication libraries (e.g., CuBLAS)
• Custom kernels written in Triton or Cutlass
• Optimized attention patterns (e.g., FlashAttention v2)

The result: massive speed and efficiency - but limited portability.

The Emerging Middle: Distributed Inference Engines Like Dynamo

A new class of inference engines is blurring the line between engine and infrastructure platform. Dynamo is one of the most notable examples.

Dynamo sits on top of engines like vLLM or TensorRT-LLM and adds:

• Disaggregated prefill + decode, split across instances
• KV cache-aware load balancing
• Distributed scaling over hundreds of GPUs
• High-throughput serving with built-in orchestration

In other words, it takes raw inference engines and turns them into production-grade, cloud-scale LLM backends.

If you're serving millions of requests or running models across clusters, Dynamo-like platforms give you capabilities beyond what a single engine can offer.

How to Choose: Key Questions to Ask

Before selecting an inference engine, ask yourself:

1. Who’s using the model?
   
   • Just me? Go local.
   • Lots of users or API clients? Go multi-tenant.
2. Where will the model run?
   
   • Consumer hardware (laptops, mobile)? Use lightweight, portable engines.
   • High-end GPUs in a server rack? Use high-performance, vendor-optimized engines.
3. What are your bottlenecks?
   
   • Decode speed? Focus on quantization and fast local kernels.
   • Prefill and batching? Use KV-aware, FlashAttention-based systems.
     
     
4. How much infrastructure are you willing to manage?
   
   • None? Stick to something like Llama.cpp.
   • Comfortable with Kubernetes or GPU orchestration? Explore vLLM or Dynamo.

Final Thoughts

Inference engines are more than just wrappers around transformer models - they are deeply tuned systems designed around specific trade-offs. Choose wrong, and you’ll either waste GPU dollars or frustrate users with latency. But choose right, and you’ll unlock massive performance, cost savings, and better UX. That's why at Doubleword we support all major inference engines, allowing our clients to select the best one which depends on your specific workload characteristics.

In short:

• Use Llama.cpp or mistral.rs for portable, lightweight local inference.
• Use vLLM, SGLang, TensorRT-LLM for high-throughput, API-serving backends.
• Use Dynamo when you need to scale inference like infrastructure.