Alternatives to vLLM
Compare vLLM alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to vLLM in 2026. Compare features, ratings, user reviews, pricing, and more from vLLM competitors and alternatives in order to make an informed decision for your business.
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LocalAI
LocalAI
LocalAI is a free, open source, local-first AI platform designed as a drop-in replacement for the OpenAI API, allowing developers to run large language models and other AI systems entirely on their own hardware without relying on cloud services. It provides a complete AI stack for local inferencing, enabling text generation, image creation with diffusion models, audio transcription and speech synthesis, embeddings for semantic search, and multimodal capabilities such as vision analysis. It is compatible with OpenAI API specifications, allowing existing applications to integrate seamlessly by simply switching endpoints, while supporting a wide range of open source model families that can run on CPU or GPU, including consumer-grade devices. LocalAI emphasizes privacy and control by ensuring all processing happens locally, keeping data on-device and eliminating external dependencies.Starting Price: Free -
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OpenVINO
Intel
The Intel® Distribution of OpenVINO™ toolkit is an open-source AI development toolkit that accelerates inference across Intel hardware platforms. Designed to streamline AI workflows, it allows developers to deploy optimized deep learning models for computer vision, generative AI, and large language models (LLMs). With built-in tools for model optimization, the platform ensures high throughput and lower latency, reducing model footprint without compromising accuracy. OpenVINO™ is perfect for developers looking to deploy AI across a range of environments, from edge devices to cloud servers, ensuring scalability and performance across Intel architectures.Starting Price: Free -
3
Ollama
Ollama
Ollama is an innovative platform that focuses on providing AI-powered tools and services, designed to make it easier for users to interact with and build AI-driven applications. Run AI models locally. By offering a range of solutions, including natural language processing models and customizable AI features, Ollama empowers developers, businesses, and organizations to integrate advanced machine learning technologies into their workflows. With an emphasis on usability and accessibility, Ollama strives to simplify the process of working with AI, making it an appealing option for those looking to harness the potential of artificial intelligence in their projects.Starting Price: Free -
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NVIDIA TensorRT
NVIDIA
NVIDIA TensorRT is an ecosystem of APIs for high-performance deep learning inference, encompassing an inference runtime and model optimizations that deliver low latency and high throughput for production applications. Built on the CUDA parallel programming model, TensorRT optimizes neural network models trained on all major frameworks, calibrating them for lower precision with high accuracy, and deploying them across hyperscale data centers, workstations, laptops, and edge devices. It employs techniques such as quantization, layer and tensor fusion, and kernel tuning on all types of NVIDIA GPUs, from edge devices to PCs to data centers. The ecosystem includes TensorRT-LLM, an open source library that accelerates and optimizes inference performance of recent large language models on the NVIDIA AI platform, enabling developers to experiment with new LLMs for high performance and quick customization through a simplified Python API.Starting Price: Free -
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Tensormesh
Tensormesh
Tensormesh is a caching layer built specifically for large-language-model inference workloads that enables organizations to reuse intermediate computations, drastically reduce GPU usage, and accelerate time-to-first-token and latency. It works by capturing and reusing key-value cache states that are normally thrown away after each inference, thereby cutting redundant compute and delivering “up to 10x faster inference” while substantially lowering GPU load. It supports deployments in public cloud or on-premises, with full observability and enterprise-grade control, SDKs/APIs, and dashboards for integration into existing inference pipelines, and compatibility with inference engines such as vLLM out of the box. Tensormesh emphasizes performance at scale, including sub-millisecond repeated queries, while optimizing every layer of inference from caching through computation. -
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LMCache
LMCache
LMCache is an open source Knowledge Delivery Network (KDN) designed as a caching layer for large language model serving that accelerates inference by reusing KV (key-value) caches across repeated or overlapping computations. It enables fast prompt caching, allowing LLMs to “prefill” recurring text only once and then reuse those stored KV caches, even in non-prefix positions, across multiple serving instances. This approach reduces time to first token, saves GPU cycles, and increases throughput in scenarios such as multi-round question answering or retrieval augmented generation. LMCache supports KV cache offloading (moving cache from GPU to CPU or disk), cache sharing across instances, and disaggregated prefill, which separates the prefill and decoding phases for resource efficiency. It is compatible with inference engines like vLLM and TGI and supports compressed storage, blending techniques to merge caches, and multiple backend storage options.Starting Price: Free -
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FriendliAI
FriendliAI
FriendliAI is a generative AI infrastructure platform that offers fast, efficient, and reliable inference solutions for production environments. It provides a suite of tools and services designed to optimize the deployment and serving of large language models (LLMs) and other generative AI workloads at scale. Key offerings include Friendli Endpoints, which allow users to build and serve custom generative AI models, saving GPU costs and accelerating AI inference. It supports seamless integration with popular open source models from the Hugging Face Hub, enabling lightning-fast, high-performance inference. FriendliAI's cutting-edge technologies, such as Iteration Batching, Friendli DNN Library, Friendli TCache, and Native Quantization, contribute to significant cost savings (50–90%), reduced GPU requirements (6× fewer GPUs), higher throughput (10.7×), and lower latency (6.2×).Starting Price: $5.9 per hour -
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Qwen2.5-1M
Alibaba
Qwen2.5-1M is an open-source language model developed by the Qwen team, designed to handle context lengths of up to one million tokens. This release includes two model variants, Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M, marking the first time Qwen models have been upgraded to support such extensive context lengths. To facilitate efficient deployment, the team has also open-sourced an inference framework based on vLLM, integrated with sparse attention methods, enabling processing of 1M-token inputs with a 3x to 7x speed improvement. Comprehensive technical details, including design insights and ablation experiments, are available in the accompanying technical report.Starting Price: Free -
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Ministral 3B
Mistral AI
Mistral AI introduced two state-of-the-art models for on-device computing and edge use cases, named "les Ministraux": Ministral 3B and Ministral 8B. These models set a new frontier in knowledge, commonsense reasoning, function-calling, and efficiency in the sub-10B category. They can be used or tuned for various applications, from orchestrating agentic workflows to creating specialist task workers. Both models support up to 128k context length (currently 32k on vLLM), and Ministral 8B features a special interleaved sliding-window attention pattern for faster and memory-efficient inference. These models were built to provide a compute-efficient and low-latency solution for scenarios such as on-device translation, internet-less smart assistants, local analytics, and autonomous robotics. Used in conjunction with larger language models like Mistral Large, les Ministraux also serve as efficient intermediaries for function-calling in multi-step agentic workflows.Starting Price: Free -
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Phi-4-mini-flash-reasoning
Microsoft
Phi-4-mini-flash-reasoning is a 3.8 billion‑parameter open model in Microsoft’s Phi family, purpose‑built for edge, mobile, and other resource‑constrained environments where compute, memory, and latency are tightly limited. It introduces the SambaY decoder‑hybrid‑decoder architecture with Gated Memory Units (GMUs) interleaved alongside Mamba state‑space and sliding‑window attention layers, delivering up to 10× higher throughput and a 2–3× reduction in latency compared to its predecessor without sacrificing advanced math and logic reasoning performance. Supporting a 64 K‑token context length and fine‑tuned on high‑quality synthetic data, it excels at long‑context retrieval, reasoning tasks, and real‑time inference, all deployable on a single GPU. Phi-4-mini-flash-reasoning is available today via Azure AI Foundry, NVIDIA API Catalog, and Hugging Face, enabling developers to build fast, scalable, logic‑intensive applications. -
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NVIDIA Triton™ inference server delivers fast and scalable AI in production. Open-source inference serving software, Triton inference server streamlines AI inference by enabling teams deploy trained AI models from any framework (TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, custom and more on any GPU- or CPU-based infrastructure (cloud, data center, or edge). Triton runs models concurrently on GPUs to maximize throughput and utilization, supports x86 and ARM CPU-based inferencing, and offers features like dynamic batching, model analyzer, model ensemble, and audio streaming. Triton helps developers deliver high-performance inference aTriton integrates with Kubernetes for orchestration and scaling, exports Prometheus metrics for monitoring, supports live model updates, and can be used in all major public cloud machine learning (ML) and managed Kubernetes platforms. Triton helps standardize model deployment in production.Starting Price: Free
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NetApp AIPod
NetApp
NetApp AIPod is a comprehensive AI infrastructure solution designed to streamline the deployment and management of artificial intelligence workloads. By integrating NVIDIA-validated turnkey solutions, such as NVIDIA DGX BasePOD™ and NetApp's cloud-connected all-flash storage, AIPod consolidates analytics, training, and inference capabilities into a single, scalable system. This convergence enables organizations to rapidly implement AI workflows, from model training to fine-tuning and inference, while ensuring robust data management and security. With preconfigured infrastructure optimized for AI tasks, NetApp AIPod reduces complexity, accelerates time to insights, and supports seamless integration into hybrid cloud environments. -
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MiMo-V2-Flash
Xiaomi Technology
MiMo-V2-Flash is an open weight large language model developed by Xiaomi based on a Mixture-of-Experts (MoE) architecture that blends high performance with inference efficiency. It has 309 billion total parameters but activates only 15 billion active parameters per inference, letting it balance reasoning quality and computational efficiency while supporting extremely long context handling, for tasks like long-document understanding, code generation, and multi-step agent workflows. It incorporates a hybrid attention mechanism that interleaves sliding-window and global attention layers to reduce memory usage and maintain long-range comprehension, and it uses a Multi-Token Prediction (MTP) design that accelerates inference by processing batches of tokens in parallel. MiMo-V2-Flash delivers very fast generation speeds (up to ~150 tokens/second) and is optimized for agentic applications requiring sustained reasoning and multi-turn interactions.Starting Price: Free -
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Oumi
Oumi
Oumi is a fully open source platform that streamlines the entire lifecycle of foundation models, from data preparation and training to evaluation and deployment. It supports training and fine-tuning models ranging from 10 million to 405 billion parameters using state-of-the-art techniques such as SFT, LoRA, QLoRA, and DPO. The platform accommodates both text and multimodal models, including architectures like Llama, DeepSeek, Qwen, and Phi. Oumi offers tools for data synthesis and curation, enabling users to generate and manage training datasets effectively. For deployment, it integrates with popular inference engines like vLLM and SGLang, ensuring efficient model serving. The platform also provides comprehensive evaluation capabilities across standard benchmarks to assess model performance. Designed for flexibility, Oumi can run on various environments, from local laptops to cloud infrastructures such as AWS, Azure, GCP, and Lambda.Starting Price: Free -
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Devstral
Mistral AI
Devstral is an open source, agentic large language model (LLM) developed by Mistral AI in collaboration with All Hands AI, specifically designed for software engineering tasks. It excels at navigating complex codebases, editing multiple files, and resolving real-world issues, outperforming all open source models on the SWE-Bench Verified benchmark with a score of 46.8%. Devstral is fine-tuned from Mistral-Small-3.1 and features a long context window of up to 128,000 tokens. It is optimized for local deployment on high-end hardware, such as a Mac with 32GB RAM or an Nvidia RTX 4090 GPU, and is compatible with inference frameworks like vLLM, Transformers, and Ollama. Released under the Apache 2.0 license, Devstral is available for free and can be accessed via Hugging Face, Ollama, Kaggle, Unsloth, and LM Studio.Starting Price: $0.1 per million input tokens -
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LFM2.5
Liquid AI
Liquid AI’s LFM2.5 is the next generation of on-device AI foundation models designed to deliver high-performance, efficient AI inference on edge devices such as phones, laptops, vehicles, IoT systems, and embedded hardware without relying on cloud compute. It extends the previous LFM2 architecture by significantly increasing the pretraining scale and reinforcement learning stages, yielding a family of hybrid models around 1.2 billion parameters that balance instruction following, reasoning, and multimodal capabilities for real-world agentic use cases. The LFM2.5 family includes Base (for fine-tuning and customization), Instruct (general-purpose instruction-tuned), Japanese-optimized, Vision-Language, and Audio-Language variants, all optimized for fast, on-device inference under tight memory constraints and available as open-weight models deployable via frameworks like llama.cpp, MLX, vLLM, and ONNX.Starting Price: Free -
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Protopia AI
Protopia AI
Protopia AI’s Stained Glass Transform (SGT) offers a cutting-edge solution to secure sensitive data in AI workloads by preventing data exposure during processing and inference. It enables enterprises to maximize the value of their data by breaking down silos while retaining full ownership and security. SGT supports deployment across diverse environments, including on-premises, hybrid, and multi-tenant clouds, optimizing GPU use for performance. It runs up to 14,000 times faster than traditional cryptographic methods, ensuring AI inference adds only minimal latency. The platform is designed to meet the needs of industries with strict data privacy requirements, such as finance, defense, and healthcare. Protopia’s technology integrates with AWS Marketplace and partners like Lambda and vLLM to provide comprehensive, high-performance, secure AI inference solutions. -
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DeePhi Quantization Tool
DeePhi Quantization Tool
This is a model quantization tool for convolution neural networks(CNN). This tool could quantize both weights/biases and activations from 32-bit floating-point (FP32) format to 8-bit integer(INT8) format or any other bit depths. With this tool, you can boost the inference performance and efficiency significantly, while maintaining the accuracy. This tool supports common layer types in neural networks, including convolution, pooling, fully-connected, batch normalization and so on. The quantization tool does not need the retraining of the network or labeled datasets, only one batch of pictures are needed. The process time ranges from a few seconds to several minutes depending on the size of neural network, which makes rapid model update possible. This tool is collaborative optimized for DeePhi DPU and could generate INT8 format model files required by DNNC.Starting Price: $0.90 per hour -
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Xilinx
Xilinx
The Xilinx’s AI development platform for AI inference on Xilinx hardware platforms consists of optimized IP, tools, libraries, models, and example designs. It is designed with high efficiency and ease-of-use in mind, unleashing the full potential of AI acceleration on Xilinx FPGA and ACAP. Supports mainstream frameworks and the latest models capable of diverse deep learning tasks. Provides a comprehensive set of pre-optimized models that are ready to deploy on Xilinx devices. You can find the closest model and start re-training for your applications! Provides a powerful open source quantizer that supports pruned and unpruned model quantization, calibration, and fine tuning. The AI profiler provides layer by layer analysis to help with bottlenecks. The AI library offers open source high-level C++ and Python APIs for maximum portability from edge to cloud. Efficient and scalable IP cores can be customized to meet your needs of many different applications. -
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Lamini
Lamini
Lamini makes it possible for enterprises to turn proprietary data into the next generation of LLM capabilities, by offering a platform for in-house software teams to uplevel to OpenAI-level AI teams and to build within the security of their existing infrastructure. Guaranteed structured output with optimized JSON decoding. Photographic memory through retrieval-augmented fine-tuning. Improve accuracy, and dramatically reduce hallucinations. Highly parallelized inference for large batch inference. Parameter-efficient finetuning that scales to millions of production adapters. Lamini is the only company that enables enterprise companies to safely and quickly develop and control their own LLMs anywhere. It brings several of the latest technologies and research to bear that was able to make ChatGPT from GPT-3, as well as Github Copilot from Codex. These include, among others, fine-tuning, RLHF, retrieval-augmented training, data augmentation, and GPU optimization.Starting Price: $99 per month -
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Intel Gaudi Software
Intel
Intel’s Gaudi software gives developers access to a comprehensive set of tools, libraries, containers, model references, and documentation that support creation, migration, optimization, and deployment of AI models on Intel® Gaudi® accelerators. It helps streamline every stage of AI development including training, fine-tuning, debugging, profiling, and performance optimization for generative AI (GenAI) and large language models (LLMs) on Gaudi hardware, whether in data centers or cloud environments. It includes up-to-date documentation with code samples, best practices, API references, and guides for efficient use of Gaudi solutions such as Gaudi 2 and Gaudi 3, and it integrates with popular frameworks and tools to support model portability and scalability. Users can access performance data to review training and inference benchmarks, utilize community and support resources, and take advantage of containers and libraries tailored to high-performance AI workloads. -
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Falcon-7B
Technology Innovation Institute (TII)
Falcon-7B is a 7B parameters causal decoder-only model built by TII and trained on 1,500B tokens of RefinedWeb enhanced with curated corpora. It is made available under the Apache 2.0 license. Why use Falcon-7B? It outperforms comparable open-source models (e.g., MPT-7B, StableLM, RedPajama etc.), thanks to being trained on 1,500B tokens of RefinedWeb enhanced with curated corpora. See the OpenLLM Leaderboard. It features an architecture optimized for inference, with FlashAttention and multiquery. It is made available under a permissive Apache 2.0 license allowing for commercial use, without any royalties or restrictions.Starting Price: Free -
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NVIDIA DGX Cloud Serverless Inference is a high-performance, serverless AI inference solution that accelerates AI innovation with auto-scaling, cost-efficient GPU utilization, multi-cloud flexibility, and seamless scalability. With NVIDIA DGX Cloud Serverless Inference, you can scale down to zero instances during periods of inactivity to optimize resource utilization and reduce costs. There's no extra cost for cold-boot start times, and the system is optimized to minimize them. NVIDIA DGX Cloud Serverless Inference is powered by NVIDIA Cloud Functions (NVCF), which offers robust observability features. It allows you to integrate your preferred monitoring tools, such as Splunk, for comprehensive insights into your AI workloads. NVCF offers flexible deployment options for NIM microservices while allowing you to bring your own containers, models, and Helm charts.
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KServe
KServe
Highly scalable and standards-based model inference platform on Kubernetes for trusted AI. KServe is a standard model inference platform on Kubernetes, built for highly scalable use cases. Provides performant, standardized inference protocol across ML frameworks. Support modern serverless inference workload with autoscaling including a scale to zero on GPU. Provides high scalability, density packing, and intelligent routing using ModelMesh. Simple and pluggable production serving for production ML serving including prediction, pre/post-processing, monitoring, and explainability. Advanced deployments with the canary rollout, experiments, ensembles, and transformers. ModelMesh is designed for high-scale, high-density, and frequently-changing model use cases. ModelMesh intelligently loads and unloads AI models to and from memory to strike an intelligent trade-off between responsiveness to users and computational footprint.Starting Price: Free -
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Mirai
Mirai
Mirai is a developer-focused on-device AI infrastructure platform designed to convert, optimize, and run machine learning models directly on Apple devices with high performance and privacy. It provides a unified pipeline that enables teams to convert and quantize models, benchmark them, distribute them, and execute inference locally. It is built specifically for Apple Silicon and aims to deliver near-zero latency, zero inference cost, and full data privacy by keeping sensitive processing on the user’s device. Through its SDK and inference engine, developers can integrate AI features into applications quickly, using hardware-aware optimizations that unlock the full power of the GPU and Neural Engine. Mirai also includes dynamic routing capabilities that automatically decide whether a request should run locally or in the cloud based on latency, privacy, or workload requirements. -
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MiniMax M2
MiniMax
MiniMax M2 is an open source foundation model built specifically for agentic applications and coding workflows, striking a new balance of performance, speed, and cost. It excels in end-to-end development scenarios, handling programming, tool-calling, and complex, long-chain workflows with capabilities such as Python integration, while delivering inference speeds of around 100 tokens per second and offering API pricing at just ~8% of the cost of comparable proprietary models. The model supports “Lightning Mode” for high-speed, lightweight agent tasks, and “Pro Mode” for in-depth full-stack development, report generation, and web-based tool orchestration; its weights are fully open source and available for local deployment with vLLM or SGLang. MiniMax M2 positions itself as a production-ready model that enables agents to complete independent tasks, such as data analysis, programming, tool orchestration, and large-scale multi-step logic at real organizational scale.Starting Price: $0.30 per million input tokens -
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Amazon EC2 Inf1 Instances
Amazon
Amazon EC2 Inf1 instances are purpose-built to deliver high-performance and cost-effective machine learning inference. They provide up to 2.3 times higher throughput and up to 70% lower cost per inference compared to other Amazon EC2 instances. Powered by up to 16 AWS Inferentia chips, ML inference accelerators designed by AWS, Inf1 instances also feature 2nd generation Intel Xeon Scalable processors and offer up to 100 Gbps networking bandwidth to support large-scale ML applications. These instances are ideal for deploying applications such as search engines, recommendation systems, computer vision, speech recognition, natural language processing, personalization, and fraud detection. Developers can deploy their ML models on Inf1 instances using the AWS Neuron SDK, which integrates with popular ML frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing for seamless migration with minimal code changes.Starting Price: $0.228 per hour -
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Intel Open Edge Platform
Intel
The Intel Open Edge Platform simplifies the development, deployment, and scaling of AI and edge computing solutions on standard hardware with cloud-like efficiency. It provides a curated set of components and workflows that accelerate AI model creation, optimization, and application development. From vision models to generative AI and large language models (LLM), the platform offers tools to streamline model training and inference. By integrating Intel’s OpenVINO toolkit, it ensures enhanced performance on Intel CPUs, GPUs, and VPUs, allowing organizations to bring AI applications to the edge with ease. -
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NVIDIA Picasso
NVIDIA
NVIDIA Picasso is a cloud service for building generative AI–powered visual applications. Enterprises, software creators, and service providers can run inference on their models, train NVIDIA Edify foundation models on proprietary data, or start from pre-trained models to generate image, video, and 3D content from text prompts. Picasso service is fully optimized for GPUs and streamlines training, optimization, and inference on NVIDIA DGX Cloud. Organizations and developers can train NVIDIA’s Edify models on their proprietary data or get started with models pre-trained with our premier partners. Expert denoising network to generate photorealistic 4K images. Temporal layers and novel video denoiser generate high-fidelity videos with temporal consistency. A novel optimization framework for generating 3D objects and meshes with high-quality geometry. Cloud service for building and deploying generative AI-powered image, video, and 3D applications. -
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Towhee
Towhee
You can use our Python API to build a prototype of your pipeline and use Towhee to automatically optimize it for production-ready environments. From images to text to 3D molecular structures, Towhee supports data transformation for nearly 20 different unstructured data modalities. We provide end-to-end pipeline optimizations, covering everything from data decoding/encoding, to model inference, making your pipeline execution 10x faster. Towhee provides out-of-the-box integration with your favorite libraries, tools, and frameworks, making development quick and easy. Towhee includes a pythonic method-chaining API for describing custom data processing pipelines. We also support schemas, making processing unstructured data as easy as handling tabular data.Starting Price: Free -
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Options for every business to train deep learning and machine learning models cost-effectively. AI accelerators for every use case, from low-cost inference to high-performance training. Simple to get started with a range of services for development and deployment. Tensor Processing Units (TPUs) are custom-built ASIC to train and execute deep neural networks. Train and run more powerful and accurate models cost-effectively with faster speed and scale. A range of NVIDIA GPUs to help with cost-effective inference or scale-up or scale-out training. Leverage RAPID and Spark with GPUs to execute deep learning. Run GPU workloads on Google Cloud where you have access to industry-leading storage, networking, and data analytics technologies. Access CPU platforms when you start a VM instance on Compute Engine. Compute Engine offers a range of both Intel and AMD processors for your VMs.
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Amazon SageMaker makes it easy to deploy ML models to make predictions (also known as inference) at the best price-performance for any use case. It provides a broad selection of ML infrastructure and model deployment options to help meet all your ML inference needs. It is a fully managed service and integrates with MLOps tools, so you can scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden. From low latency (a few milliseconds) and high throughput (hundreds of thousands of requests per second) to long-running inference for use cases such as natural language processing and computer vision, you can use Amazon SageMaker for all your inference needs.
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Intel Tiber AI Cloud
Intel
Intel® Tiber™ AI Cloud is a powerful platform designed to scale AI workloads with advanced computing resources. It offers specialized AI processors, such as the Intel Gaudi AI Processor and Max Series GPUs, to accelerate model training, inference, and deployment. Optimized for enterprise-level AI use cases, this cloud solution enables developers to build and fine-tune models with support for popular libraries like PyTorch. With flexible deployment options, secure private cloud solutions, and expert support, Intel Tiber™ ensures seamless integration, fast deployment, and enhanced model performance.Starting Price: Free -
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Latent AI
Latent AI
We take the hard work out of AI processing on the edge. The Latent AI Efficient Inference Platform (LEIP) enables adaptive AI at the edge by optimizing for compute, energy and memory without requiring changes to existing AI/ML infrastructure and frameworks. LEIP is a modular, fully-integrated workflow designed to train, quantize, adapt and deploy edge AI neural networks. LEIP is a modular, fully-integrated workflow designed to train, quantize and deploy edge AI neural networks. Latent AI believes in a vibrant and sustainable future driven by the power of AI and the promise of edge computing. Our mission is to deliver on the vast potential of edge AI with solutions that are efficient, practical, and useful. Latent AI helps a variety of federal and commercial organizations gain the most from their edge AI with an automated edge MLOps pipeline that creates ultra-efficient, compressed, and secured edge models at scale while also removing all maintenance and configuration concerns -
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Falcon-40B
Technology Innovation Institute (TII)
Falcon-40B is a 40B parameters causal decoder-only model built by TII and trained on 1,000B tokens of RefinedWeb enhanced with curated corpora. It is made available under the Apache 2.0 license. Why use Falcon-40B? It is the best open-source model currently available. Falcon-40B outperforms LLaMA, StableLM, RedPajama, MPT, etc. See the OpenLLM Leaderboard. It features an architecture optimized for inference, with FlashAttention and multiquery. It is made available under a permissive Apache 2.0 license allowing for commercial use, without any royalties or restrictions. ⚠️ This is a raw, pretrained model, which should be further finetuned for most usecases. If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at Falcon-40B-Instruct.Starting Price: Free -
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Qualcomm Cloud AI SDK
Qualcomm
The Qualcomm Cloud AI SDK is a comprehensive software suite designed to optimize trained deep learning models for high-performance inference on Qualcomm Cloud AI 100 accelerators. It supports a wide range of AI frameworks, including TensorFlow, PyTorch, and ONNX, enabling developers to compile, optimize, and execute models efficiently. The SDK provides tools for model onboarding, tuning, and deployment, facilitating end-to-end workflows from model preparation to production deployment. Additionally, it offers resources such as model recipes, tutorials, and code samples to assist developers in accelerating AI development. It ensures seamless integration with existing systems, allowing for scalable and efficient AI inference in cloud environments. By leveraging the Cloud AI SDK, developers can achieve enhanced performance and efficiency in their AI applications. -
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Hugging Face Transformers
Hugging Face
Transformers is a library of pretrained natural language processing, computer vision, audio, and multimodal models for inference and training. Use Transformers to train models on your data, build inference applications, and generate text with large language models. Explore the Hugging Face Hub today to find a model and use Transformers to help you get started right away. Simple and optimized inference class for many machine learning tasks like text generation, image segmentation, automatic speech recognition, document question answering, and more. A comprehensive trainer that supports features such as mixed precision, torch.compile, and FlashAttention for training and distributed training for PyTorch models. Fast text generation with large language models and vision language models. Every model is implemented from only three main classes (configuration, model, and preprocessor) and can be quickly used for inference or training.Starting Price: $9 per month -
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NetMind AI
NetMind AI
NetMind.AI is a decentralized computing platform and AI ecosystem designed to accelerate global AI innovation. By leveraging idle GPU resources worldwide, it offers accessible and affordable AI computing power to individuals, businesses, and organizations of all sizes. The platform provides a range of services, including GPU rental, serverless inference, and an AI ecosystem that encompasses data processing, model training, inference, and agent development. Users can rent GPUs at competitive prices, deploy models effortlessly with on-demand serverless inference, and access a wide array of open-source AI model APIs with high-throughput, low-latency performance. NetMind.AI also enables contributors to add their idle GPUs to the network, earning NetMind Tokens (NMT) as rewards. These tokens facilitate transactions on the platform, allowing users to pay for services such as training, fine-tuning, inference, and GPU rentals. -
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Mistral Large 3
Mistral AI
Mistral Large 3 is a next-generation, open multimodal AI model built with a powerful sparse Mixture-of-Experts architecture featuring 41B active parameters out of 675B total. Designed from scratch on NVIDIA H200 GPUs, it delivers frontier-level reasoning, multilingual performance, and advanced image understanding while remaining fully open-weight under the Apache 2.0 license. The model achieves top-tier results on modern instruction benchmarks, positioning it among the strongest permissively licensed foundation models available today. With native support across vLLM, TensorRT-LLM, and major cloud providers, Mistral Large 3 offers exceptional accessibility and performance efficiency. Its design enables enterprise-grade customization, letting teams fine-tune or adapt the model for domain-specific workflows and proprietary applications. Mistral Large 3 represents a major advancement in open AI, offering frontier intelligence without sacrificing transparency or control.Starting Price: Free -
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Nebius Token Factory
Nebius
Nebius Token Factory is a scalable AI inference platform designed to run open-source and custom AI models in production without manual infrastructure management. It offers enterprise-ready inference endpoints with predictable performance, autoscaling throughput, and sub-second latency — even at very high request volumes. It delivers 99.9% uptime availability and supports unlimited or tailored traffic profiles based on workload needs, simplifying the transition from experimentation to global deployment. Nebius Token Factory supports a broad set of open source models such as Llama, Qwen, DeepSeek, GPT-OSS, Flux, and many others, and lets teams host and fine-tune models through an API or dashboard. Users can upload LoRA adapters or full fine-tuned variants directly, with the same enterprise performance guarantees applied to custom models.Starting Price: $0.02 -
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MaiaOS
Zyphra Technologies
Zyphra is an artificial intelligence company based in Palo Alto with a growing presence in Montreal and London. We’re building MaiaOS, a multimodal agent system combining advanced research in next-gen neural network architectures (SSM hybrids), long-term memory & reinforcement learning. We believe the future of AGI will involve a combination of cloud and on-device deployment strategies with an increasing shift toward local inference. MaiaOS is built around a deployment framework that maximizes inference efficiency for real-time intelligence. Our AI & product teams come from leading organizations and institutions including Google DeepMind, Anthropic, StabilityAI, Qualcomm, Neuralink, Nvidia, and Apple. We have deep expertise across AI models, learning algorithms, and systems/infrastructure with a focus on inference efficiency and AI silicon performance. Zyphra's team is committed to democratizing advanced AI systems. -
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kluster.ai
kluster.ai
Kluster.ai is a developer-centric AI cloud platform designed to deploy, scale, and fine-tune large language models (LLMs) with speed and efficiency. Built for developers by developers, it offers Adaptive Inference, a flexible and scalable service that adjusts seamlessly to workload demands, ensuring high-performance processing and consistent turnaround times. Adaptive Inference provides three distinct processing options: real-time inference for ultra-low latency needs, asynchronous inference for cost-effective handling of flexible timing tasks, and batch inference for efficient processing of high-volume, bulk tasks. It supports a range of open-weight, cutting-edge multimodal models for chat, vision, code, and more, including Meta's Llama 4 Maverick and Scout, Qwen3-235B-A22B, DeepSeek-R1, and Gemma 3 . Kluster.ai's OpenAI-compatible API allows developers to integrate these models into their applications seamlessly.Starting Price: $0.15per input -
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Baseten
Baseten
Baseten is a high-performance platform designed for mission-critical AI inference workloads. It supports serving open-source, custom, and fine-tuned AI models on infrastructure built specifically for production scale. Users can deploy models on Baseten’s cloud, their own cloud, or in a hybrid setup, ensuring flexibility and scalability. The platform offers inference-optimized infrastructure that enables fast training and seamless developer workflows. Baseten also provides specialized performance optimizations tailored for generative AI applications such as image generation, transcription, text-to-speech, and large language models. With 99.99% uptime, low latency, and support from forward deployed engineers, Baseten aims to help teams bring AI products to market quickly and reliably.Starting Price: Free -
44
webAI
webAI
Users enjoy personalized interactions, creating custom AI models to meet individual needs with decentralized technology, Navigator offers rapid, location-independent responses. Experience innovation where technology complements human expertise. Collaboratively create, manage, and monitor content with co-workers, friends, and AI. Build custom AI models in minutes vs hours. Revitalize large models with attention steering, streamlining training and cutting compute costs. Seamlessly translates user interactions into manageable tasks. It selects and executes the most suitable AI model for each task, delivering responses that align with user expectations. Private forever, with no back doors, distributed storage, and seamless inference. It leverages distributed, edge-friendly technology for lightning-fast interactions, no matter where you are. Join our vibrant distributed storage ecosystem, where you can unlock access to the world's first watermarked universal model dataset.Starting Price: Free -
45
Fireworks AI
Fireworks AI
Fireworks partners with the world's leading generative AI researchers to serve the best models, at the fastest speeds. Independently benchmarked to have the top speed of all inference providers. Use powerful models curated by Fireworks or our in-house trained multi-modal and function-calling models. Fireworks is the 2nd most used open-source model provider and also generates over 1M images/day. Our OpenAI-compatible API makes it easy to start building with Fireworks. Get dedicated deployments for your models to ensure uptime and speed. Fireworks is proudly compliant with HIPAA and SOC2 and offers secure VPC and VPN connectivity. Meet your needs with data privacy - own your data and your models. Serverless models are hosted by Fireworks, there's no need to configure hardware or deploy models. Fireworks.ai is a lightning-fast inference platform that helps you serve generative AI models.Starting Price: $0.20 per 1M tokens -
46
Mu
Microsoft
Mu is a 330-million-parameter encoder–decoder language model designed to power the agent in Windows settings by mapping natural-language queries to Settings function calls, running fully on-device via NPUs at over 100 tokens per second while maintaining high accuracy. Drawing on Phi Silica optimizations, Mu’s encoder–decoder architecture reuses a fixed-length latent representation to cut computation and memory overhead, yielding 47 percent lower first-token latency and 4.7× higher decoding speed on Qualcomm Hexagon NPUs compared to similar decoder-only models. Hardware-aware tuning, including a 2/3–1/3 encoder–decoder parameter split, weight sharing between input and output embeddings, Dual LayerNorm, rotary positional embeddings, and grouped-query attention, enables fast inference at over 200 tokens per second on devices like Surface Laptop 7 and sub-500 ms response times for settings queries. -
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Modular
Modular
Modular is a unified AI inference platform designed to run models efficiently across diverse hardware environments. It enables developers to deploy and scale AI workloads on GPUs, CPUs, and ASICs using a single, integrated stack. The platform optimizes performance from low-level GPU kernels to high-level API endpoints. Modular supports both managed cloud deployments and self-hosted environments, offering flexibility for different use cases. It allows users to run open-source or custom models with high performance and cost efficiency. With features like hardware portability and dynamic scaling, it reduces vendor lock-in and infrastructure complexity. By combining performance optimization and deployment simplicity, Modular helps teams build and run AI applications at scale. -
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Unsloth
Unsloth
Unsloth is an open source platform designed to accelerate and optimize the fine-tuning and training of Large Language Models (LLMs). It enables users to train custom models, such as ChatGPT, in just 24 hours instead of the typical 30 days, achieving speeds up to 30 times faster than Flash Attention 2 (FA2) while using 90% less memory. Unsloth supports both LoRA and QLoRA fine-tuning techniques, allowing for efficient customization of models like Mistral, Gemma, and Llama versions 1, 2, and 3. Unsloth's efficiency stems from manually deriving computationally intensive mathematical steps and handwriting GPU kernels, resulting in significant performance gains without requiring hardware modifications. Unsloth delivers a 10x speed increase on a single GPU and up to 32x on multi-GPU systems compared to FA2, with compatibility across NVIDIA GPUs from Tesla T4 to H100, and portability to AMD and Intel GPUs.Starting Price: Free -
49
SquareFactory
SquareFactory
End-to-end project, model and hosting management platform, which allows companies to convert data and algorithms into holistic, execution-ready AI-strategies. Build, train and manage models securely with ease. Create products that consume AI models from anywhere, any time. Minimize risks of AI investments, while increasing strategic flexibility. Completely automated model testing, evaluation deployment, scaling and hardware load balancing. From real-time, low-latency, high-throughput inference to batch, long-running inference. Pay-per-second-of-use model, with an SLA, and full governance, monitoring and auditing tools. Intuitive interface that acts as a unified hub for managing projects, creating and visualizing datasets, and training models via collaborative and reproducible workflows. -
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VESSL AI
VESSL AI
Build, train, and deploy models faster at scale with fully managed infrastructure, tools, and workflows. Deploy custom AI & LLMs on any infrastructure in seconds and scale inference with ease. Handle your most demanding tasks with batch job scheduling, only paying with per-second billing. Optimize costs with GPU usage, spot instances, and built-in automatic failover. Train with a single command with YAML, simplifying complex infrastructure setups. Automatically scale up workers during high traffic and scale down to zero during inactivity. Deploy cutting-edge models with persistent endpoints in a serverless environment, optimizing resource usage. Monitor system and inference metrics in real-time, including worker count, GPU utilization, latency, and throughput. Efficiently conduct A/B testing by splitting traffic among multiple models for evaluation.Starting Price: $100 + compute/month
