Foundation model deployment is no longer one problem. It can mean running a local LLM for private experimentation, serving an open-weight model under heavy GPU traffic, packaging a model behind an API, or managing ML systems across Kubernetes and CI/CD. The right open-source tool depends on the workload, not the logo on the repo.
TL;DR: Use Ollama for local LLM experiments, vLLM for high-throughput GPU inference, TGI for production serving inside the Hugging Face ecosystem, BentoML for model APIs, and Kubeflow or Seldon Core when Kubernetes is already your operating layer.
Some of these tools are specialized LLM-serving engines. Some are older but still useful model-serving systems. Some are broader MLOps platforms. Be aware: “foundation model deployment” now covers several layers of infrastructure: local execution, inference serving, lifecycle management, orchestration, monitoring, and scaling.
Which open-source foundation model deployment tool should you use?
Tool | Best scenario | GPU or CPU? | Scalability | Current status |
|---|---|---|---|---|
vLLM | High-throughput LLM inference, especially for open-weight models | Mostly GPU | High | Highly current; one of the strongest open-source choices for production LLM serving |
Ollama | Running LLMs locally for development, demos, private use, or small internal tools | CPU or GPU | Low to medium | Highly current; simplest local LLM runner |
Hugging Face TGI | Production LLM serving inside the Hugging Face ecosystem | Mostly GPU | High | Highly current; production-oriented LLM server |
BentoML | Building model APIs and deploying AI inference services | CPU or GPU | Medium to high | Current; strong general-purpose inference platform |
Seldon Core | Kubernetes-based model deployment, scaling, monitoring, and LLMOps | CPU or GPU | High | Current, especially with Seldon Core 2 |
Kubeflow | Full ML platform on Kubernetes, including pipelines and model management | CPU or GPU | High | Current; powerful but heavy platform choice |
MLflow | Model lifecycle, registry, tracking, and deployment management | CPU or GPU | Medium | Current; useful for lifecycle management, not LLM-serving-first |
MLRun | MLOps and GenAI orchestration across the application lifecycle | CPU or GPU | Medium to high | Current; useful for production ML and GenAI pipelines |
Metaflow | Managing real-world ML, AI, and data science workflows | CPU or GPU | Medium | Current; more workflow/platform than model-serving server |
TensorFlow Serving | Serving TensorFlow models in production | CPU or GPU | Medium to high | Stable but older; useful for TensorFlow, less central for modern LLM deployment |
TorchServe | Serving PyTorch models where TorchServe is already installed | CPU or GPU | Medium | Use with caution; limited maintenance |
SGLang | LLM and multimodal serving, RL rollouts, distributed inference clusters | Mostly GPU | High | Current; useful for large-scale inference and RL training pipelines |
llama.cpp | Running LLMs on consumer hardware, edge devices, or lightweight local servers using a lightweight C/C++ runtime | CPU or GPU | Low to medium | Current; especially useful for local and edge LLM inference + GGUF-based models |
1. vLLM
vLLM is a high-throughput, memory-efficient inference and serving engine for large language models. It is especially useful for teams deploying open-weight LLMs on GPUs and trying to improve serving throughput, batching, and memory use. Its best-known technique is PagedAttention, which helps manage attention key-value memory more efficiently during inference. The current vLLM documentation also highlights continuous batching, chunked prefill, prefix caching, quantization, and distributed inference.
GitHub: vLLM GitHub
Documentation: vLLM Docs
Best for: production LLM serving, high-throughput inference, open-weight models, GPU-heavy workloads.
Status: Highly current. One of the most important open-source LLM-serving tools for GPU-heavy production inference.
2. Ollama
Ollama is an open-source tool for running large language models locally on a laptop, workstation, or private server. It is useful for local development, demos, privacy-sensitive prototyping, small internal tools, and teams that want a simple way to pull and run models without building a full production serving stack. It is much simpler than Kubernetes-based deployment systems, but it is not designed to be the main serving layer for high-scale production workloads.
GitHub: Ollama GitHub
Website: Ollama Website
Best for: local LLMs, demos, private experiments, lightweight internal tools.
Status: Highly current. Best for local LLM use, fast prototyping, and small-scale private deployments.
3. Hugging Face Text Generation Inference
Hugging Face Text Generation Inference, or TGI, is a toolkit for deploying and serving large language models. It supports high-performance text generation for popular open-source LLMs and is tightly connected to the Hugging Face ecosystem. Hugging Face describes TGI as a toolkit for deploying and serving LLMs, and its GitHub page says it is used in production to power Hugging Chat, the Inference API, and Inference Endpoints.
GitHub: TGI GitHub
Documentation: TGI Docs
Best for: production LLM serving, Hugging Face models, teams already working inside the Hugging Face ecosystem.
Status: Highly current. Best for production LLM serving when Hugging Face is already part of the stack.
4. TensorFlow Serving
TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. It is strongest when teams already use TensorFlow and need a stable serving layer for trained models. It can be extended beyond TensorFlow models, but it is not the first tool most teams reach for when deploying modern open-weight LLMs.
GitHub: TensorFlow Serving GitHub
Documentation: TensorFlow Serving Docs
Best for: production TensorFlow model serving, stable ML inference systems, older production ML stacks.
Status: Stable but older. Still useful for TensorFlow production models, but less central for modern foundation model deployment.
5. TorchServe
TorchServe is a model-serving framework for PyTorch models. It was designed to simplify deployment and serving for PyTorch-based ML systems. However, the project is now marked as being in limited maintenance: existing releases remain available, but there are no planned updates, bug fixes, new features, or security patches.
GitHub: TorchServe GitHub
Documentation: TorchServe Docs
Best for: existing PyTorch deployments where TorchServe is already installed and migration is not immediate.
Status: Use with caution. It should not be the default choice for a new foundation model deployment in 2026.
6. MLflow
MLflow is a platform for managing the machine learning lifecycle, including experiment tracking, model packaging, model registry, and deployment workflows. It is useful when the problem is not only serving the model, but managing the path from experiment to production. MLflow’s deployment tools can serve models locally and connect to other serving targets, but it is not a specialized high-throughput LLM inference server.
Website: MLflow Website
Documentation: MLflow Docs
GitHub: MLflow GitHub
Best for: model lifecycle management, experiment tracking, model registry, reproducible deployment workflows.
Status: Current. Strong for lifecycle management and deployment workflows, but not LLM-serving-first.
7. Kubeflow
Kubeflow is a Kubernetes-native platform for building, deploying, and managing machine learning workflows. It is useful for teams that already operate on Kubernetes and need a broader ML platform rather than a single model server. Kubeflow can support pipelines, model metadata, notebooks, and other parts of the ML lifecycle, but it may be too heavy if the only goal is to run one model.
Website: Kubeflow Website
GitHub: Kubeflow GitHub
Documentation: Kubeflow Docs
Best for: Kubernetes-native ML platforms, scalable ML workflows, teams with platform engineering support.
Status: Current. Powerful, but heavy. Best for organizations that already have Kubernetes maturity.
8. Seldon Core
Seldon Core is a Kubernetes-native framework for deploying, managing, and scaling AI systems. Seldon Core 2 is positioned for both MLOps and LLMOps, with support for standardized deployment across model types, on-prem environments, and cloud environments. It is a good fit when the deployment problem includes scaling, monitoring, pipelines, and governance around production models.
GitHub: Seldon Core GitHub
Documentation: Seldon Docs
Best for: Kubernetes model serving, MLOps, LLMOps, monitoring, production AI systems.
Status: Current. Especially useful for teams that want Kubernetes-native deployment and production controls.
9. Metaflow
Metaflow is an open-source framework for building and managing real-world ML, AI, and data science projects. It was originally developed at Netflix and is especially useful for moving data science work from local development into production workflows. It is not a dedicated model-serving server, but it can help teams manage the broader workflow around ML and AI systems.
Website: Metaflow Website
GitHub: Metaflow GitHub
Documentation: Metaflow Docs
Best for: ML workflows, data science projects, productionizing research code, managing dependencies and execution.
Status: Current. More workflow platform than serving engine, but still relevant in foundation model deployment stacks.
10. MLRun
MLRun is an open-source AI orchestration framework for managing ML and generative AI applications across their lifecycle. It supports data preparation, model tuning, customization, validation, optimization, real-time serving, pipelines, observability, and deployment across cloud, hybrid, and on-prem environments.
Website: MLRun Website
GitHub: MLRun GitHub
Documentation: MLRun Docs
Best for: MLOps, GenAI orchestration, lifecycle management, real-time serving pipelines.
Status: Current. Useful for teams building production ML and GenAI applications that need orchestration beyond simple model serving.
11. BentoML
BentoML is a framework and platform for building, serving, and deploying AI applications and model inference APIs. It helps package models into reproducible services and supports production-grade deployment patterns. It is useful when teams need to turn models into APIs and manage inference services without building every serving layer from scratch.
Website: BentoML Website
GitHub: BentoML GitHub
Documentation: BentoML Docs
Best for: model APIs, AI inference services, custom model serving, production deployment.
Status: Current. Strong general-purpose platform for building and deploying AI inference services.
12. SGLang
SGLang is a serving framework for LLMs and multimodal models. It is designed for low-latency, high-throughput inference on anything from a single GPU to massive distributed GPU clusters. SGLang focuses on production-scale serving, advanced scheduling, distributed parallelism, and RL rollout generation for frontier AI systems. Its core features include continuous batching, RadixAttention prefix caching, speculative decoding, tensor/pipeline/expert parallelism, quantization support and multi-LoRA serving.
GitHub: SGLang GitHub
Documentation: SGLang Docs
Best for: large-scale LLM serving, distributed inference, RL rollouts, multimodal production systems
Status: Current. Used in both frontier-model training and high-scale production deployments.
13. llama.cpp
llama.cpp is inference engine and runtime for running LLMs locally with minimal setup. Written entirely in C/C++, it focuses on efficient CPU and GPU inference, lightweight deployment, hardware portability, and quantized execution across consumer devices, edge systems, laptops, workstations, and servers. It is one of the foundational tools behind the modern GGUF local-LLM.
Website: llama.cpp Website
GitHub: llama.cpp GitHub
Best for: local LLM and highly optimized quantized inference, lightweight deployment, CPU-based LLMs.
Status: Current. Widely used open-source runtime for local and edge LLM inference.
What changed in foundation model deployment?
The original model-serving world was mostly about taking a trained model and exposing it through a production endpoint. That is still important, but foundation models changed the deployment problem.
Modern teams now need to think about:
Throughput: how many tokens or requests the system can serve.
Latency: how quickly the model starts and completes a response.
Memory: how efficiently the system handles model weights and KV cache.
Local execution: whether models can run privately on developer machines or internal servers.
Kubernetes readiness: whether the tool fits enterprise infrastructure.
Lifecycle management: how models move from experiment to production.
Observability: whether teams can monitor, debug, and improve the system after deployment.
That is why vLLM, Ollama, and TGI are in this list. They reflect where foundation model deployment has moved: away from generic model serving alone and toward LLM-specific inference, local model running, and high-throughput production serving.
Quick recommendations
Use Ollama if you want the fastest way to run an LLM locally.
Use vLLM if you care about high-throughput GPU inference for open-weight LLMs.
Use Hugging Face TGI if your team already works heavily with Hugging Face models and wants a production LLM-serving stack.
Use BentoML if you want to package models into production APIs.
Use Seldon Core or Kubeflow if Kubernetes is already central to your infrastructure.
Use MLflow, Metaflow, or MLRun if the bigger problem is lifecycle management, workflow orchestration, or production ML operations.
Use TensorFlow Serving if you still have TensorFlow models in production.
Use TorchServe only if you already depend on it and understand the maintenance risk.
Use SGLang for more optimized for advanced scheduling, RL/post-training, large distributed deployments.
Use llama.cpp if you want highly optimized local or edge LLM inference on consumer hardware.
FAQ
What is foundation model deployment?
Foundation model deployment is the process of running, serving, scaling, and managing large AI models in real applications. It can include local model execution, cloud inference, API packaging, Kubernetes deployment, monitoring, and lifecycle management.
Which open-source tool is best for local LLM deployment?
Ollama is usually the simplest choice for local LLM deployment. It is built for running models on a laptop, workstation, or private server without setting up a large production serving system.
Which open-source tool is best for high-throughput LLM serving?
vLLM is one of the strongest open-source choices for high-throughput LLM serving, especially for open-weight models running on GPUs. It focuses on serving efficiency, batching, memory management, and inference throughput.
What is the difference between vLLM and TGI?
vLLM is often chosen for high-throughput open-weight LLM serving and memory-efficient inference. TGI is Hugging Face’s production-oriented LLM serving toolkit and is especially useful for teams already working inside the Hugging Face ecosystem.
Is TorchServe still a good choice?
TorchServe can still be used in existing PyTorch deployments, but it is no longer actively maintained. For new projects, teams should usually consider more current serving options unless they have a specific reason to keep TorchServe.
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