The conversation around large language models (LLMs) used to be dominated by staggering training costs and breakthrough architectures. But as we hurtle towards 2025, the narrative has fundamentally shifted. Today, the real bottleneck isn’t how well we can train a model, but how quickly and cost-effectively we can serve billions of tokens under real-world traffic. It’s a battlefield where milliseconds matter, and GPU memory is gold.
Serving LLMs efficiently boils down to a few critical implementation details: how an inference runtime batches requests, how it artfully overlaps prefill and decode operations, and crucially, how it manages and reuses the infamous KV cache. Different engines make different strategic tradeoffs on these fronts, and these choices directly impact your throughput, latency (especially P50 and P99), and precious GPU memory footprint. If you’re building a production LLM application, understanding these nuances isn’t optional – it’s essential.
So, which of these workhorse runtimes are truly making a difference in the trenches? Let’s break down six of the most prominent contenders you’ll find powering production LLM stacks in 2025, exploring their design philosophies, performance characteristics, and where they shine brightest.
The Core Challenge: Making LLMs Fly in Production
Imagine your LLM service getting hit with hundreds, even thousands, of simultaneous user requests. Each one needs its unique prompt processed and then a response generated token by token. A naive approach would be to process them one after another, leading to abysmal latency and GPU underutilization. This is where the magic of LLM inference runtimes comes into play.
They act as sophisticated traffic controllers and resource managers. They implement continuous batching, which means new requests are constantly added to the GPU’s processing queue, keeping it busy. They also intelligently overlap the “prefill” phase (processing the input prompt) with the “decode” phase (generating new tokens), reducing idle time. But perhaps the most impactful innovation revolves around the Key-Value (KV) cache.
The KV cache stores the intermediate computations for each token in a sequence, preventing redundant calculations. It grows with every generated token and can quickly consume vast amounts of GPU memory. The way a runtime manages this cache – how it allocates, quantizes, and reuses these KV pairs – often determines its ultimate performance ceiling and memory efficiency. It’s truly the dark art of LLM serving.
A Deep Dive into the Top Contenders for 2025
Let’s peel back the layers on the runtimes that are setting the standard for LLM serving.
vLLM: The PagedAttention Pioneer
vLLM hit the scene and quickly became a darling of the LLM serving world, largely thanks to its innovative PagedAttention mechanism. Instead of allocating a single, large, contiguous buffer for each sequence’s KV cache, vLLM carves it into fixed-size blocks. Sequences then point to a list of these blocks, much like virtual memory paging in an operating system.
This design yields incredibly low KV fragmentation (often reported at less than 4% waste, a dramatic improvement over the 60-80% common in naive allocators). It also enables high GPU utilization through continuous batching and natively supports prefix sharing and KV reuse at the block level. With recent additions like FP8 KV quantization and FlashAttention integration, vLLM remains a default high-performance engine, offering excellent throughput, solid Time-To-First-Token (TTFT), and commendable hardware flexibility. If you need a robust, general-purpose LLM serving backend, vLLM is often your first stop.
TensorRT LLM: NVIDIA’s Latency Powerhouse
When every millisecond counts and your infrastructure is NVIDIA-centric, TensorRT LLM often takes the crown. Built on top of NVIDIA’s TensorRT, it’s a compilation-based engine that generates highly optimized, fused kernels tailored for specific models and shapes. This deep-level optimization is a game-changer for latency-sensitive applications.
Its KV subsystem is a rich toolkit, offering paged, quantized (INT8, FP8), and even circular buffer KV caches. Critically, it excels at KV cache reuse, including offloading KV to the CPU and reusing it across prompts – an approach NVIDIA reports can slash TTFT by up to 14x on H100s in specific scenarios. While it requires an investment in model-specific engine builds and tuning, the payoff in ultra-low single-request latency and high throughput for NVIDIA environments can be substantial. It’s the choice for those who want to wring every drop of performance out of their NVIDIA hardware.
Hugging Face TGI v3: For the Long Haul and Chat Workloads
Hugging Face’s Text Generation Inference (TGI) has long been a popular server-focused stack, known for its Rust-based server, continuous batching, and deep integration with the Hugging Face Hub. But TGI v3 marks a significant evolution, especially for long-context workloads like chat applications.
The key enhancement here is a new long-context pipeline featuring chunked prefill for long inputs and, crucially, prefix KV caching. This means if you’re managing long conversation histories, TGI v3 can intelligently reuse previous KV states, avoiding costly recomputations. While vLLM might sometimes edge it out on raw tokens per second for conventional prompts, TGI v3 can process significantly more tokens and be dramatically faster (up to 13x) than vLLM on very long prompts when prefix caching is enabled. For teams already invested in the Hugging Face ecosystem and dealing with chat-style traffic with lengthy histories, TGI v3 offers a compelling, integrated solution.
LMDeploy: Throughput King for Quantized Models
Emerging from the InternLM ecosystem, LMDeploy is a powerful toolkit for compression and deployment, particularly for NVIDIA GPUs. Its TurboMind engine boasts high-performance CUDA kernels, offering persistent, continuous batching and a blocked KV cache with sophisticated management and reuse.
LMDeploy stands out for its aggressive quantization support (AWQ, online INT8/INT4 KV quant), enabling it to run larger models on constrained GPUs with impressive speed. Vendor evaluations show LMDeploy achieving up to 1.8x higher request throughput than vLLM for certain 4-bit Llama-style models on A100s. If your goal is to maximize throughput per GPU, especially with quantized models, and you’re comfortable with LMDeploy’s specific tooling, this engine offers a serious advantage.
SGLang: Structured Programs and RadixAttention
SGLang isn’t just an inference runtime; it’s also a Domain Specific Language (DSL) for building structured LLM programs – think agents, RAG workflows, or complex tool pipelines. Its runtime innovation centers around RadixAttention, a unique KV reuse mechanism that leverages a radix tree structure to share prefixes.
This approach shines brightest when many requests share common prefixes, such as multi-turn chat, few-shot prompts, or agentic systems performing repeated context lookups. SGLang can achieve up to 6.4x higher throughput and 3.7x lower latency on such structured workloads compared to baseline systems. Its high KV cache hit rates (50-99%) translate directly into efficiency gains. For developers crafting intricate LLM applications where heavy prefix reuse is the norm, SGLang offers a profound advantage by treating KV reuse as a first-class citizen at the application level.
DeepSpeed Inference / ZeRO Inference: Scaling to Titanic Models
What if your model is simply too big to fit onto a single GPU, or even multiple GPUs, without extensive resources? This is where Microsoft’s DeepSpeed Inference, particularly with its ZeRO Inference and ZeRO Offload capabilities, comes into play. It’s designed to run truly massive models on limited GPU memory by offloading model weights, and sometimes the KV cache, to CPU or even NVMe (SSD).
The tradeoff, as you might expect, is latency. TTFT and P99 will be significantly higher compared to pure GPU-resident engines. However, DeepSpeed enables you to serve models that would otherwise be impossible on your hardware. For example, an OPT 30B model (which won’t fit natively on a single 32GB V100) can achieve around 30-43 tokens per second with full CPU or NVMe offload. This makes it ideal for offline inference, large-scale batch processing, or low QPS services where model size trumps immediate latency concerns, effectively turning your GPU into a throughput engine with solid-state drives in the loop.
Choosing Your Champion: Practical Considerations for 2025
With such a rich ecosystem, the “best” LLM inference runtime isn’t a one-size-fits-all answer. Your choice will inevitably hinge on your specific needs and constraints:
- For a strong, general-purpose default: Start with vLLM. Its PagedAttention and continuous batching offer an excellent balance of throughput, TTFT, and hardware flexibility, making it a reliable workhorse for most scenarios.
- If you’re all-in on NVIDIA and need extreme latency control: Opt for TensorRT LLM. Be prepared to invest in model-specific engine builds and tuning, perhaps running it behind Triton, but the low latency and fine-grained KV control are unmatched in this niche.
- When your stack is Hugging Face-centric and long chats are common: TGI v3 is your go-to. Its new long-prompt pipeline and prefix caching offer substantial real-world gains for conversational AI.
- To achieve maximum throughput per GPU with quantized models: Look to LMDeploy, especially its TurboMind engine with blocked KV. It’s particularly effective for 4-bit Llama family models and high-concurrency environments.
- Building complex agents, tool chains, or heavy RAG systems: Leverage SGLang. By designing your prompts to maximize KV reuse via RadixAttention, you can unlock significant throughput and latency advantages in structured workloads.
- When you absolutely must run gigantic models on limited GPUs: DeepSpeed Inference / ZeRO Inference is your lifeline. Accept higher latency, but gain the ability to serve models that simply wouldn’t fit otherwise, perfect for offline or low-QPS batch inference.
The KV Cache is King
As we’ve seen, while these engines offer diverse approaches, they are all converging on a singular truth: the KV cache is the ultimate bottleneck and the most critical resource to manage. The winners in the LLM serving race are the runtimes that treat KV as a first-class data structure—to be intelligently paged, quantized to save space, reused across requests to save computation, and even offloaded when memory is scarce. It’s no longer about just stuffing a big tensor into GPU memory; it’s about surgical precision and clever orchestration. Understanding these KV strategies is no longer just for experts; it’s becoming a foundational skill for anyone building the next generation of LLM-powered applications.
