Large Language Models (LLMs) have exploded in popularity, and so have specialized frameworks to serve them efficiently. Four notable open-source LLM serving frameworks stand out – Ollama, vLLM, SGLang, and LLaMA.cpp Server – each with different design philosophies and strengths. This article provides an in-depth overview of these frameworks, covering how they work, their performance optimizations, standout features, and ideal use cases.
Ollama
Ollama is an open-source framework designed to make running LLMs locally simple and cross-platform. First emerging in 2023 and actively developed into 2024, it wraps powerful language models in an easy-to-use package. Ollama works as a lightweight server on your machine that can download, manage, and run models with minimal setup. Under the hood, it builds on proven backends (like the C++ llama.cpp library) to execute models efficiently on local hardware, including CPUs and Apple Silicon GPUs. By 2024, Ollama introduced a Modelfile system – a configuration file to customize prompts and settings – and supports multiple models, meaning you can switch which model is serving on the fly via API calls. This flexibility is a key advantage over using llama.cpp directly, which traditionally requires choosing one model at startup.
In terms of performance, Ollama prioritizes accessibility over extreme throughput. It supports model quantization (down to 4-bit or 5-bit) to shrink model size and speed up inference, leveraging the GGML/GGUF format from llama.cpp. This allows even 30B+ parameter models to run on commodity hardware, albeit at reduced precision. On modern GPUs, Ollama can utilize acceleration: for example, running a 14B model on an NVIDIA H100 (with 4-bit quantization) achieved around 75 tokens/second – respectable for local inference. However, Ollama is not built for large-scale concurrent serving. It typically handles one request at a time per model and has limited batching capabilities. Sources note that local-focused solutions like Ollama struggle to scale beyond low request rates. Thus, Ollama’s strength is not raw throughput or multi-user concurrency, but convenience and low barrier to entry.
Ollama’s strongest features are its ease of use and integration. It provides one-stop model management. With just a single command, you can easily download and launch a pre-trained model from Ollama’s curated library or from Hugging Face, making it incredibly simple to get started.
The server offers both a simple generate endpoint and an OpenAI-compatible API, so existing OpenAI API clients can speak to local models with minimal changes. This makes it easy to integrate Ollama into applications or developer tools. Indeed, Ollama runs on macOS, Windows, and Linux, supporting developers on any platform without requiring specialized hardware or cloud services. Its Modelfile feature allows packaging a model with custom system prompts or parameters for particular behaviors. Combined with built-in support for Docker and Homebrew, and active community contributions, Ollama’s competitive edge is making local LLM serving as painless as possible.
The use cases for Ollama center on local and personal deployments or small-scale services. It’s ideal for developers who want to experiment with LLMs on their own machines, for offline or private data scenarios, and for applications that don’t need to handle high request volume.
For example, one can run a coding assistant in VSCode using Ollama as the backend, or serve a chatbot in a small organization without GPU servers. In summary, Ollama shines for ease-of-use, model management, and multi-model flexibility on local hardware, while trading off the massive throughput and scaling capabilities that more specialized servers provide.
vLLM
vLLM is a high-performance LLM serving library that emerged from UC Berkeley research in 2023. It remains a cutting-edge solution for production-grade model serving on GPU servers. In contrast to Ollama’s simplicity for local use, vLLM is all about maximizing throughput and efficiency on powerful hardware. It is implemented in Python (with optimized kernels) and provides an HTTP API compatible with the OpenAI specification, making it relatively easy to drop into existing workflows. The core idea behind vLLM is to solve the performance bottlenecks of naive transformers inference via novel memory management and scheduling algorithms.
At the heart of vLLM is the PagedAttention mechanism, introduced in a research paper in late 2023. PagedAttention treats the model’s attention key/value cache like a virtual memory system, storing these key/value tensors in flexible “pages” rather than one contiguous block. This eliminates the severe memory fragmentation and over-allocation that plague traditional implementations (which often waste 60–80% of memory on unused cache space). By dynamically managing the KV cache in non-contiguous chunks, vLLM can serve longer sequences or more concurrent sequences without running out of GPU memory. The result is dramatically higher resource utilization. In benchmarks, vLLM delivered up to 24× higher throughput than standard HuggingFace Transformers inference, and several times higher throughput than even optimized servers like Hugging Face’s Text Generation Inference (TGI). These gains were achieved without any changes to model architecture, showcasing the effect of better memory management alone.
Another key optimization in vLLM is continuous batching of requests. Traditional batched inference processes a fixed batch of requests, then waits for all to finish before starting the next batch – underutilizing the GPU if some requests finish early. vLLM instead uses an assembly-line approach where incoming requests are added to the batch dynamically as soon as there is room, without pausing between batches. This means the GPU is kept busy and new queries don’t have to wait long for service, greatly reducing latency under load. Combined with other optimizations (like efficient CUDA kernels and support for FP16 precision to save memory), vLLM pushes hardware to its limits. It supports multi-GPU scaling and distributed deployment as well, enabling it to handle models that exceed a single GPU’s memory by sharding across devices or nodes.
vLLM’s strongest features are its state-of-the-art throughput and latency for serving LLMs, alongside a relatively user-friendly interface. Developers can run a vLLM server that exposes an OpenAI-like API, so swapping an OpenAI GPT-3 endpoint with a local vLLM endpoint is straightforward. This lowers the barrier for teams to adopt it in place of paid APIs, benefiting from lower cost and control. Its “secret sauce” algorithms (PagedAttention and continuous batching) give it a competitive edge in scenarios with many simultaneous requests or long conversations that would exhaust other systems’ memory.
vLLM is best suited for high-demand production environments: for example, an AI service receiving multiple user queries per second, or a research demo like Chatbot Arena that needed to serve models to millions of user interactions.
In terms of use cases, vLLM targets teams with access to GPUs who need efficiency and scalability. It requires NVIDIA CUDA (or similar acceleration), so it’s not for edge devices but rather for cloud servers or on-prem GPU rigs. If an organization wants to deploy a large model (like Llama-65B or larger) serving multiple users with low latency, vLLM is a top choice. It may require more engineering effort to set up (installing dependencies, configuring the server, possibly modifying code to utilize its API), but once running, it excels at multi-user chatbots, real-time LLM-powered apps, and other throughput-intensive workloads. vLLM’s design has remained focused on core inference optimizations; while new competitors have emerged (like SGLang) pushing performance even further, vLLM is still a foundational reference point for fast LLM serving.
SGLang
SGLang is a newer entrant that takes LLM serving to the next level of performance and flexibility. The name stands for “Structured Generation Language,” reflecting that SGLang is not only a serving engine but also a programming interface for complex LLM-driven applications. Developed by the LMSYS team (who also created Vicuna and Chatbot Arena), SGLang has quickly evolved through 2024 and has been integrated into the PyTorch ecosystem. It co-designs a fast backend runtime with a frontend domain-specific language to allow fine-grained control of LLM inference workflows. In essence, SGLang aims to deliver top-tier serving performance while enabling advanced usage patterns like chaining multiple LLM calls, running tool-using agents, enforcing output formats, and handling multi-modal inputs – all within one unified framework.
On the backend, SGLang introduces innovations such as RadixAttention and other optimizations. RadixAttention is a technique for automatic KV cache reuse across multiple generation calls. This is crucial for complex LLM programs where the same prefix or partial results may be reused in subsequent prompts (for example, an agent that iteratively appends to a conversation). While vLLM’s PagedAttention handles efficient cache management for a single long sequence, RadixAttention lets SGLang reuse and share cache content across different queries without redundant computation. This dramatically improves efficiency in scenarios like reasoning or tool use, where a conversation might branch or loop. SGLang’s runtime also implements continuous batching, a zero-overhead scheduling system, and support for advanced strategies like speculative decoding, tensor parallelism (for multi-GPU), and chunked processing of long inputs. It incorporates memory optimizations similar to PagedAttention (referred to as “token attention/paged attention” in its feature list). Impressively, SGLang supports quantization on the fly – including FP8 and INT4 quantized execution, GPTQ quantization, etc. – to squeeze maximum speed from hardware. In short, SGLang’s backend combined many state-of-the-art techniques to minimize latency and maximize throughput for LLM inference.
Performance results show SGLang to be a front-runner. In complex multi-call workloads (like an agent executing multiple LLM steps), SGLang achieved up to 5× higher throughput than existing systems such as Guidance or vLLM. Even in straightforward generation tasks, it consistently delivers competitive or superior speed. A mid-2024 benchmark found SGLang achieving up to 3.1× the throughput of vLLM on a 70B model, and often matching or exceeding NVIDIA’s highly optimized TensorRT-LLM library. Crucially, SGLang attains this while being fully open-source (Apache 2.0) and implemented primarily in Python, with its core schedulers under 4000 lines of code. This makes it relatively accessible to customize or extend, compared to heavily optimized C++ code in some other frameworks. By co-designing the frontend DSL with the backend, SGLang can exploit patterns in the prompt/program to optimize execution. For example, developers can write a structured sequence of prompts (with loops, conditionals, tool calls, etc.) in SGLang’s Python DSL, and the runtime will efficiently execute it as a single pipeline, reusing caches and parallelizing where possible.
The standout feature of SGLang is this combination of speed and control. It offers a flexible front-end language that allows users to script how the LLM should generate text, accept multi-modal inputs (like images for vision-language models), or produce structured outputs. This is a big step beyond the simple text-in/text-out interface of most serving engines. With SGLang, one can enforce output formats or constraints easily, coordinate multiple LLM calls (say, one for reasoning, one for final answer) within one session, and even run multiple queries in parallel when appropriate. In essence, SGLang is not just serving answers, but serving programs running on the LLM. This makes it especially appealing for complex applications like AI agents, chatbots that use tools or external knowledge, and research in prompt programming.
As for use cases and audience, SGLang is geared towards advanced developers, researchers, and production teams that need both performance and flexibility. If one simply needs a basic text completion API, vLLM or simpler solutions suffice. But if the task involves complex interactions – e.g. a chatbot that must call external APIs or maintain a long reasoning chain – SGLang provides an elegant way to implement it with minimal overhead. SGLang had already seen adoption by major players: notably, Elon Musk’s xAI used SGLang to serve its 70B model Grok, and Microsoft Azure leveraged SGLang to deploy the DeepSeek-R1 model on AMD GPUs. These real-world deployments underscore SGLang’s industry-grade capability. It also joined the official PyTorch ecosystem in 2024, indicating broad community support and compatibility moving forward. While still young, SGLang represents the cutting edge of open-source LLM serving, with data showing it has surpassed prior systems in many metrics. Its continuing development is one to watch, as it aims to be a “next-generation efficient engine” for LLMs.
LLaMA.cpp Server
LLaMA.cpp Server is the serving mode of the popular llama.cpp project – a lightweight C/C++ implementation for running LLMs, originally created in early 2023. The llama.cpp project rose to prominence by allowing Meta’s LLaMA and other models to be run on local CPUs with surprisingly good performance, thanks to aggressive optimizations and quantization. By 2024, llama.cpp introduced an integrated HTTP server that turns it into an easy-to-use local service. The LLaMA.cpp Server is a minimalist, OpenAI-compatible LLM server that you can launch with a single command, specifying a model file and a port. True to its roots, it is extremely lightweight – essentially just one self-contained binary – and requires no Python or complex dependencies. As such, it’s an attractive option for deploying models on edge devices or in environments where a small footprint is critical.
LLaMA.cpp uses an efficient C++ library (with no external deep learning framework) to run inference. It heavily relies on quantized model formats (e.g., 4-bit, 5-bit, 8-bit) so that large models can fit in CPU RAM or modest VRAM. The server loads a model into memory and then listens for requests. It supports the OpenAI Chat Completion API protocol out of the box, meaning clients can send a chat, and get a streamed or completed response in OpenAI format. One limitation is that, unlike Ollama, the LLaMA.cpp server typically runs a single model at a time – you choose the model when starting the server process. However, what it lacks in multi-model flexibility, it makes up in sheer universality: this server can run on virtually any hardware. It can utilize CPUs (with multi-threading), GPUs via CUDA or Apple Metal (if compiled with those backends), and even WebAssembly for browser/portable scenarios. This broad hardware support, combined with minimal resource overhead, has made llama.cpp a foundational piece of the local LLM movement.
In terms of performance optimizations, LLaMA.cpp has steadily improved. It introduced features like speculative decoding (using a smaller “draft” model to accelerate a larger model’s generation), and supports embedding generation and grammar-constrained generation right in the server. These capabilities are notable since they enable use cases beyond basic text completion: for instance, one can get sentence embeddings for semantic search or force the LLM to output JSON complying to a schema. While llama.cpp’s C++ implementations of transformer operations are highly optimized for its scope, its throughput on a single CPU is inherently much lower than GPU-based frameworks. A typical 7B parameter model might generate on the order of a few tokens per second per core (depending on quantization and hardware).
Users often run it with 8 or more threads to get acceptable speeds, and smaller quantized models can reach perhaps 10-20 tokens/sec on a high-end desktop CPU. With GPU offloading (e.g., offloading some layers to a CUDA GPU), speeds improve, but llama.cpp still does not do the sophisticated batching that vLLM/SGLang do.
Thus, LLaMA.cpp Server is best for low-concurrency, single-user scenarios or batch jobs where latency isn’t mission-critical. Its focus is on being decent and extremely portable, rather than maximizing absolute throughput.
The server mode simply formalized what many were doing with custom scripts, providing a convenient HTTP API. Because it’s so lightweight, one can embed llama.cpp in other software to add an offline AI feature. Its competitive edge is versatility: it can run on a Raspberry Pi or inside a browser, and it supports a wide array of models (not just LLaMA variants but also GPT-J, MPT, etc., after conversion to its format). It’s also easy to install – often just a compile or a Homebrew command – and doesn’t require the complex dependency management of Python environments.
The ideal use cases for LLaMA.cpp Server overlap somewhat with Ollama’s, in that both target local deployment. However, llama.cpp is even more low-level. It’s perfect for hobbyists, researchers, or small apps that need to run an LLM in constrained environments. For example, one could use it to power a local chatbot that runs entirely offline, or to experiment with LLMs on non-NVIDIA hardware (like an Apple M1/M2, where it uses Metal acceleration). Its grammar enforcement feature can be useful for generating structured data on edge devices. In production, one might see llama.cpp used in specialized cases such as IoT devices that need some language understanding capability without cloud connectivity. By 2025, llama.cpp’s development has plateaued in terms of new features, but it remains actively maintained and widely used. It essentially set the standard for “LLM anywhere”, and its server component extends that philosophy by making deployment as a service trivial.
In summary, LLaMA.cpp Server offers maximum portability and simplicity, at the cost of lower raw performance, making it a vital tool in the LLM serving landscape for certain niches.
Comparing the Frameworks
Each of these LLM serving frameworks excels in different aspects, and choosing between them depends on the context of use. Ollama and LLaMA.cpp Server prioritize approachability and broad accessibility, while vLLM and SGLang focus on squeezing the most performance out of modern hardware. Ollama provides a polished user experience with features like easy model downloads, multi-model serving, and an intuitive API on any operating system. It’s best seen as a tool for developers or small teams to prototype and deploy LLMs without deep MLOps expertise. In contrast, LLaMA.cpp Server strips things down to the bare essentials – just load a model and serve – which makes it incredibly versatile. It doesn’t manage models for you or batch requests intelligently, but it will run almost anywhere and is battle-tested in countless community projects.
On the other end, vLLM and SGLang represent the state-of-the-art in efficient LLM serving. vLLM introduced fundamental improvements like PagedAttention and continuous batching that set new standards for throughput. It essentially made high-quality LLM serving feasible without exorbitant hardware, and many platforms built on its techniques. SGLang, building on that foundation, has pushed the envelope further – not only matching vLLM’s speed but often exceeding it by leveraging cache reuse and other optimizations. Moreover, SGLang’s structured generation approach targets a more programmable future for LLMs, where developers can easily orchestrate complex interactions through the framework itself. This makes SGLang especially powerful for cutting-edge applications (AI agents, tool use, multi-modal chatbots) where simply generating text isn’t enough. SGLang had demonstrated substantial performance gains and attracted early adopters in industry, signaling a strong trajectory.
In comparing these frameworks, we see a clear trade-off between ease-of-use vs. maximum performance. Ollama and LLaMA.cpp make it trivial to get started – one can be up and running with a local model in minutes – but they are not designed to handle a barrage of requests or the largest models at lightning speed. vLLM and SGLang require more setup and assume powerful GPUs; in return, they deliver superior throughput, latency, and scalability, capable of serving multiple users or very long inputs efficiently. Another dimension is flexibility: SGLang stands out by allowing complex scripting of LLM behavior (thanks to its DSL), whereas vLLM focuses purely on high-performance text generation APIs. Ollama and LLaMA.cpp both support certain extensions (like OpenAI-compatible endpoints, and in llama.cpp’s case, embeddings and grammars), but they don’t natively provide multi-step orchestration – you’d handle that logic outside the server.
Hardware support is also a differentiator. LLaMA.cpp and Ollama can run on CPU-only environments and take advantage of Apple Silicon or other non-NVIDIA accelerators, while vLLM for now is oriented toward NVIDIA CUDA (and similar environments) for full benefits. SGLang, notably, has shown flexibility by working on NVIDIA and AMD GPUs, reflecting a design aligned with PyTorch’s cross-platform capabilities. In terms of community and real-world adoption, all four have made impacts: llama.cpp arguably has the largest open-source community footprint, vLLM and Ollama have strong GitHub followings and are often discussed as top LLMops tools, and SGLang – though newer – boasts usage in high-profile settings (e.g. xAI’s Grok model) and active development by the LMSYS community.
To summarize, Ollama is best for ease of deployment and multi-model management on local machines; vLLM is best for high-throughput serving of LLMs in production (especially when sticking to a straightforward prompting interface); SGLang is best for cutting-edge applications requiring both performance and fine-grained control/structure; and LLaMA.cpp Server is best for lightweight deployments and maximal portability, running LLMs anywhere even without GPUs.
The table below provides a side-by-side comparison of key features and characteristics:As the table illustrates, no single framework is “best” in all categories – each occupies its own niche in the LLM landscape. Ollama offers approachability, vLLM and SGLang push performance and scalability, and LLaMA.cpp provides ultimate simplicity and portability. In practice, these projects even complement each other: for instance, a developer might use Ollama or LLaMA.cpp for local testing, vLLM for a production service, and explore SGLang for specialized applications that need the extra control. The landscape in 2025 is rich and evolving, with these frameworks driving forward what is possible with open-source LLM deployment. By understanding their differences, users can choose the right tool for their needs and even contribute to these communities to shape the future of LLM serving.
vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention – vLLM Blog (June 20, 2023). https://blog.vllm.ai/2023/06/20/vllm.html
Efficient Memory Management for Large Language Model Serving with PagedAttention – Yu et al., arXiv (Sept 2023). https://arxiv.org/abs/2307.05296
Meet vLLM: For faster, more efficient LLM inference and serving – Red Hat Blog (Dec 2023). https://www.redhat.com/en/blog/meet-vllm-faster-more-efficient-llm-inference-and-serving
Best LLMOps Tools: Comparison of Open-Source LLM Production Frameworks – Winder Research (2023). https://winder.ai/llmops-tools-comparison-open-source-llm-production-frameworks/
Fast and Expressive LLM Inference with RadixAttention and SGLang – LMSYS Org Blog (Jan 17, 2024). https://lmsys.org/blog/2024-01-17-sglang/
Achieving Faster Open-Source Llama3 Serving with SGLang Runtime (vs. TensorRT-LLM, vLLM) – LMSYS Org Blog (Jul 25, 2024). https://lmsys.org/blog/2024-07-25-sglang-llama3/
SGLang Joins PyTorch Ecosystem: Efficient LLM Serving Engine – PyTorch Ecosystem Blog (Oct 2024). https://pytorch.org/blog/sglang-joins-pytorch/
Ollama – Get up and running with large language models locally – Ollama Documentation (2024). https://ollama.com (and GitHub: https://github.com/ollama/ollama)
Serve Large Language Models APIs Locally – GitLab Docs (2023). https://docs.gitlab.com/development/ai_features/local_models/
Part 3: Ollama for AI Model Serving – Cohorte Blog (Aug 2023). https://www.cohorte.co/blog/ollama-for-ai-model-serving
Llama.cpp: LLM inference in C/C++ (Github Repository) – ggerganov/llama.cpp (stars, docs, 2023-2024). https://github.com/ggml-org/llama.cpp
Hacker News Discussion: “Llama.cpp guide – Running LLMs locally” – Hacker News (Oct 2023). https://news.ycombinator.com/item?id=42274489
About Hyperbolic
Hyperbolic is the on-demand AI cloud made for developers. We provide fast, affordable access to compute, inference, and AI services. Over 165,000 developers use Hyperbolic to train, fine-tune, and deploy models at scale.
Our platform has quickly become a favorite among AI researchers, including those like Andrej Karpathy. We collaborate with teams at Hugging Face, Vercel, Quora, Chatbot Arena, LMSYS, OpenRouter, Black Forest Labs, Stanford, Berkeley, and beyond.
Founded by AI researchers from UC Berkeley and the University of Washington, Hyperbolic is built for the next wave of AI innovation—open, accessible, and developer-first.
Website | X | Discord | LinkedIn | YouTube | GitHub | Documentation
