Alternatives to BaseRT
Compare BaseRT alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to BaseRT in 2026. Compare features, ratings, user reviews, pricing, and more from BaseRT competitors and alternatives in order to make an informed decision for your business.
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1
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 -
2
Lucebox
Lucebox
Lucebox is a plug-and-play computer built for running local AI models and agents at full speed. Inside the custom chassis, a Ryzen AI MAX+ 395 with 128GB of unified LPDDR5X memory is paired with an RTX 3090, and the two work together through an open-source inference engine hand-tuned for exactly this hardware. The architecture is what makes it fast. Large models live in the 128GB unified memory tier, while the 3090's high-bandwidth VRAM acts as a fast tier. Speculative decoding (DFlash) and speculative prefill (PFlash) bridge the two, producing inference speeds up to 10x higher than llama.cpp on the same silicon and beating machines like the Mac Studio and DGX Spark at a fraction of their effective cost.Starting Price: $4,900 - One time payment -
3
EXAONE Deep
LG
EXAONE Deep is a series of reasoning-enhanced language models developed by LG AI Research, featuring parameter sizes of 2.4 billion, 7.8 billion, and 32 billion. These models demonstrate superior capabilities in various reasoning tasks, including math and coding benchmarks. Notably, EXAONE Deep 2.4B outperforms other models of comparable size, EXAONE Deep 7.8B surpasses both open-weight models of similar scale and the proprietary reasoning model OpenAI o1-mini, and EXAONE Deep 32B shows competitive performance against leading open-weight models. The repository provides comprehensive documentation covering performance evaluations, quickstart guides for using EXAONE Deep models with Transformers, explanations of quantized EXAONE Deep weights in AWQ and GGUF formats, and instructions for running EXAONE Deep models locally using frameworks like llama.cpp and Ollama.Starting Price: Free -
4
LFM2
Liquid AI
LFM2 is a next-generation series of on-device foundation models built to deliver the fastest generative-AI experience across a wide range of endpoints. It employs a new hybrid architecture that achieves up to 2x faster decode and prefill performance than comparable models, and up to 3x improvements in training efficiency compared to the previous generation. These models strike an optimal balance of quality, latency, and memory for deployment on embedded systems, allowing real-time, on-device AI across smartphones, laptops, vehicles, wearables, and other endpoints, enabling millisecond inference, device resilience, and full data sovereignty. Available in three dense checkpoints (0.35 B, 0.7 B, and 1.2 B parameters), LFM2 demonstrates benchmark performance that outperforms similarly sized models in tasks such as knowledge recall, mathematics, multilingual instruction-following, and conversational dialogue evaluations. -
5
vLLM
vLLM
vLLM is a high-performance library designed to facilitate efficient inference and serving of Large Language Models (LLMs). Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. It offers state-of-the-art serving throughput by efficiently managing attention key and value memory through its PagedAttention mechanism. It supports continuous batching of incoming requests and utilizes optimized CUDA kernels, including integration with FlashAttention and FlashInfer, to enhance model execution speed. Additionally, vLLM provides quantization support for GPTQ, AWQ, INT4, INT8, and FP8, as well as speculative decoding capabilities. Users benefit from seamless integration with popular Hugging Face models, support for various decoding algorithms such as parallel sampling and beam search, and compatibility with NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs, and more. -
6
distil labs
distil labs
distil labs optimizes AI workloads by replacing expensive frontier-model calls with custom small language models tuned to a specific task while maintaining the required quality bar. It observes real production traffic, captures traces from existing LLM requests, and automatically builds an evaluation set to understand how the workload actually behaves. It then generates and validates synthetic training data, matches the data distribution to the target workload, performs supervised fine-tuning and reinforcement learning, quantizes the model, and deploys an optimized endpoint. Results are automatically evaluated against the current model on accuracy, latency, and efficiency before teams choose to scale traffic. The resulting OpenAI-compatible endpoint combines a specialized SLM, prompt optimization, caching, and tuned serving for the use case.Starting Price: $0.04 per 1M tokens -
7
Laguna XS 2.1
Poolside
Laguna XS 2.1 is an upgraded open weight agentic coding model designed for long-horizon work on a local machine. It uses a 33-billion-parameter Mixture-of-Experts architecture with 3 billion activated parameters per token, retaining the same efficient architecture as Laguna XS.2 while improving multilingual software engineering and terminal-style task performance. The model is built to support coding agents that inspect repositories, reason through complex changes, use tools, execute commands, and continue working across extended tasks. It is served with a 256K context window, giving agents room to work with large codebases, lengthy histories, and multi-step workflows. Laguna XS 2.1 is supported by vLLM, SGLang, NVIDIA TensorRT-LLM, Hugging Face Transformers, and Ollama, with native llama.cpp support planned. It is available in BF16, FP8, INT4, and NVFP4 checkpoints, allowing developers to choose between maximum fidelity and configurations suited to tighter VRAM or compute budgets. -
8
LTM-2-mini
Magic AI
LTM-2-mini is a 100M token context model: LTM-2-mini. 100M tokens equals ~10 million lines of code or ~750 novels. For each decoded token, LTM-2-mini’s sequence-dimension algorithm is roughly 1000x cheaper than the attention mechanism in Llama 3.1 405B1 for a 100M token context window. The contrast in memory requirements is even larger – running Llama 3.1 405B with a 100M token context requires 638 H100s per user just to store a single 100M token KV cache.2 In contrast, LTM requires a small fraction of a single H100’s HBM per user for the same context. -
9
Wafer
Wafer
Wafer delivers the fastest open source LLMs for enterprise through serverless and dedicated inference built for production AI workloads. Its serverless inference gives teams access to top open models with no infrastructure, no deployment overhead, and fast APIs, including GLM-5.2-Fast for low-latency inference with EAGLE speculative decoding and a per-stream throughput SLA, GLM-5.2 as a flagship model with stronger coding and reasoning capabilities, and more. Wafer’s technology uses agents that optimize inference across the stack, identifying and enhancing bottlenecks in orchestration, algorithms, serving engines, GPU kernels, and diverse hardware. It profiles the stack to see whether latency or throughput comes from scheduling, decoding, kernels, memory pressure, or hardware fit, then tries many paths and ships the measured winner. Instead of relying on a single switch or heuristic, Wafer searches model, engine, kernel, and hardware combinations.Starting Price: Free -
10
CodeQwen
Alibaba
CodeQwen is the code version of Qwen, the large language model series developed by the Qwen team, Alibaba Cloud. It is a transformer-based decoder-only language model pre-trained on a large amount of data of codes. Strong code generation capabilities and competitive performance across a series of benchmarks. Supporting long context understanding and generation with the context length of 64K tokens. CodeQwen supports 92 coding languages and provides excellent performance in text-to-SQL, bug fixes, etc. You can just write several lines of code with transformers to chat with CodeQwen. Essentially, we build the tokenizer and the model from pre-trained methods, and we use the generate method to perform chatting with the help of the chat template provided by the tokenizer. We apply the ChatML template for chat models following our previous practice. The model completes the code snippets according to the given prompts, without any additional formatting.Starting Price: Free -
11
CodeT5
Salesforce
Code for CodeT5, a new code-aware pre-trained encoder-decoder model. Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. This is the official PyTorch implementation for the EMNLP 2021 paper from Salesforce Research. CodeT5-large-ntp-py is specially optimized for Python code generation tasks and employed as the foundation model for our CodeRL, yielding new SOTA results on the APPS Python competition-level program synthesis benchmark. This repo provides the code for reproducing the experiments in CodeT5. CodeT5 is a new pre-trained encoder-decoder model for programming languages, which is pre-trained on 8.35M functions in 8 programming languages (Python, Java, JavaScript, PHP, Ruby, Go, C, and C#). In total, it achieves state-of-the-art results on 14 sub-tasks in a code intelligence benchmark - CodeXGLUE. Generate code based on the natural language description. -
12
RunInfra
RunInfra
RunInfra turns plain English into production AI inference endpoints. Describe your use case, and the AI agent builds, optimizes, deploys, and scales it for you; no YAML, no DevOps, no GPU configuration, just chat. It is built for shipping open source AI models as production APIs, selecting compatible models, benchmarking real GPUs, applying kernel optimizations, and deploying OpenAI-compatible HTTP endpoints. RunInfra can build LLM, speech-to-text, text-to-speech, embedding, vision-language, image-generation, RAG search, document AI, transcription, AI assistant, and multi-model reasoning pipelines when the selected model and runtime support the route. Its workflow moves from description to optimization to deployment to integration; tell RunInfra what you need, let it profile real GPUs from L4 to B200, search model variants such as AWQ, GPTQ, and FP8, tune kernels with Forge, and ship an endpoint that works with OpenAI Python and JavaScript SDKs.Starting Price: $100 per month -
13
Mistral 7B
Mistral AI
Mistral 7B is a 7.3-billion-parameter language model that outperforms larger models like Llama 2 13B across various benchmarks. It employs Grouped-Query Attention (GQA) for faster inference and Sliding Window Attention (SWA) to efficiently handle longer sequences. Released under the Apache 2.0 license, Mistral 7B is accessible for deployment across diverse platforms, including local environments and major cloud services. Additionally, a fine-tuned version, Mistral 7B Instruct, demonstrates enhanced performance in instruction-following tasks, surpassing models like Llama 2 13B Chat.Starting Price: Free -
14
Llama 2
Meta
The next generation of our open source large language model. This release includes model weights and starting code for pretrained and fine-tuned Llama language models — ranging from 7B to 70B parameters. Llama 2 pretrained models are trained on 2 trillion tokens, and have double the context length than Llama 1. Its fine-tuned models have been trained on over 1 million human annotations. Llama 2 outperforms other open source language models on many external benchmarks, including reasoning, coding, proficiency, and knowledge tests. Llama 2 was pretrained on publicly available online data sources. The fine-tuned model, Llama-2-chat, leverages publicly available instruction datasets and over 1 million human annotations. We have a broad range of supporters around the world who believe in our open approach to today’s AI — companies that have given early feedback and are excited to build with Llama 2.Starting Price: Free -
15
NexaSDK
NexaSDK
Nexa SDK is a unified developer toolkit that lets you run and ship any AI model locally on virtually any device with support for NPUs, GPUs, and CPUs, offering seamless deployment without needing cloud connectivity; it provides a fast command-line interface, Python bindings, mobile (Android and iOS) SDKs, and Linux support so you can integrate AI into apps, IoT devices, automotive systems, and desktops with minimal setup and one line of code to run models, while also exposing an OpenAI-compatible REST API and function calling for easy integration with existing clients. Powered by the company’s custom NexaML inference engine built from the kernel up for optimal performance on every hardware stack, the SDK supports multiple model formats including GGUF, MLX, and Nexa’s proprietary format, delivers full multimodal support for text, image, and audio tasks (including embeddings, reranking, speech recognition, and text-to-speech), and prioritizes Day-0 support for the latest architectures. -
16
Qwen3.5-Plus
Alibaba
Qwen3.5-Plus is a high-performance native vision-language model designed for efficient text generation, deep reasoning, and multimodal understanding. Built on a hybrid architecture that combines linear attention with a sparse mixture-of-experts design, it delivers strong performance while optimizing inference efficiency. The model supports text, image, and video inputs and produces text outputs, making it suitable for complex multimodal workflows. With a massive 1 million token context window and up to 64K output tokens, Qwen3.5-Plus enables long-form reasoning and large-scale document analysis. It includes advanced capabilities such as structured outputs, function calling, web search, and tool integration via the Responses API. The model supports prefix continuation, caching, batch processing, and fine-tuning for flexible deployment. Designed for developers and enterprises, Qwen3.5-Plus provides scalable, high-throughput AI performance with OpenAI-compatible API access.Starting Price: $0.4 per 1M tokens -
17
Lemonfox.ai
Lemonfox.ai
Our models are deployed around the world to give you the best possible response times. Integrate our OpenAI-compatible API effortlessly into your application. Begin within minutes and seamlessly scale to serve millions of users. Benefit from our extensive scale and performance optimizations, making our API 4 times more affordable than OpenAI's GPT-3.5 API. Generate text and chat with our AI model that delivers ChatGPT-level performance at a fraction of the cost. Getting started just takes a few minutes with our OpenAI-compatible API. Harness the power of one of the most advanced AI image models to craft stunning, high-quality images, graphics, and illustrations in a few seconds.Starting Price: $5 per month -
18
Cheaper Inference
Keak
Cheaper Inference is an OpenAI-compatible API gateway that provides access to AI models from multiple providers through a single API key, without requiring users to change their request format. Developers can switch by replacing the provider base URL and API key while keeping the same model, messages, tools, streaming settings, and response handling. It supports text and image models, vision-capable chat requests, streaming, prompt caching, reasoning controls, and temporary image uploads for larger vision payloads. Models are selected per request, and the catalog can be filtered by type, vision, reasoning, streaming, or provider. Automatic retries handle network and provider failures, while eligible fallback routes can be tried before a request fails. Every request is visible in History, giving teams a record of request volume, token usage, and operational activity.Starting Price: $0.48 per output -
19
Luminal
Luminal
Luminal is a machine-learning framework built for speed, simplicity, and composability, focusing on static graphs and compiler-based optimization to deliver high performance even for complex neural networks. It compiles models into minimal “primops” (only 12 primitive operations) and then applies compiler passes to replace those with device-specific optimized kernels, enabling efficient execution on GPU or other backends. It supports modules (building blocks of networks with a standard forward API) and the GraphTensor interface (typed tensors and graphs at compile time) for model definition and execution. Luminal’s core remains intentionally small and hackable, with extensibility via external compilers for datatypes, devices, training, quantization, and more. Quick-start guidance shows how to clone the repo, build a “Hello World” example, or run a larger model like LLaMA 3 using GPU features. -
20
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. -
21
Mercury 2
Inception
Mercury 2 is the first reasoning model fast enough to pick up the phone, a reasoning diffusion language model built for real-time voice agents. Instead of making callers wait through seconds of dead air while an autoregressive model generates thinking tokens one by one, Mercury 2 uses a diffusion large language model architecture to generate tokens in parallel, decoding 1000+ tokens per second on standard NVIDIA GPUs. That speed is fast enough to run a full reasoning pass and start speaking within the latency budget of a natural conversation, reducing the cost of reasoning from seconds of silence to roughly 300 milliseconds. Mercury models work by corrupting clean text into noise, then training a standard Transformer to reverse the process and predict clean text across all positions simultaneously. Because each denoising pass touches many tokens, generation uses the GPU more efficiently than one-token-at-a-time decoding, making custom-silicon-like speed possible on NVIDIA H100s. -
22
Macyou
Macyou LLC
Macyou rents dedicated Apple Silicon Macs for AI workloads. Users configure a Mac (M4 Mac mini to Mac Studio M3 Ultra with 256 GB unified memory), pick a pre-configured stack — local LLMs via Ollama (Llama, Qwen, Mistral, DeepSeek), agent frameworks (CrewAI, LangGraph), or ML dev environments (MLX, Jupyter) — and get a running deployment in about 5 minutes. Every deployment exposes an OpenAI-compatible API, so existing OpenAI SDK code works by changing base_url; access also includes SSH with root and a browser-based remote desktop. Each customer gets a dedicated physical machine with full-disk encryption and a disk wipe between tenants, hosted in a GDPR-friendly jurisdiction. Pricing is a fixed monthly fee per machine with no per-token charges; Thunderbolt 5 clustering pools unified memory across nodes for larger models. Published, measured inference benchmarks (raw JSON, CC BY 4.0) show real tokens per second per chip.Starting Price: $79/month -
23
Mixtral 8x7B
Mistral AI
Mixtral 8x7B is a high-quality sparse mixture of experts model (SMoE) with open weights. Licensed under Apache 2.0. Mixtral outperforms Llama 2 70B on most benchmarks with 6x faster inference. It is the strongest open-weight model with a permissive license and the best model overall regarding cost/performance trade-offs. In particular, it matches or outperforms GPT-3.5 on most standard benchmarks.Starting Price: Free -
24
Photon
Moondream
Photon is Moondream’s official high-performance inference engine, designed to run vision-language models efficiently across cloud, desktop, and edge environments while delivering real-time performance for production AI systems. It is built as a custom inference layer tightly integrated with the Moondream model architecture, using optimized scheduling, native image processing, and purpose-built CUDA kernels to maximize speed and efficiency. This co-designed approach allows Photon to significantly reduce latency compared to traditional VLM setups, enabling responsive interactions on edge devices and real-time throughput on server-grade hardware. It supports deployment across a wide range of NVIDIA GPUs, from embedded systems like Jetson devices to high-end multi-GPU servers, making it adaptable for diverse operational needs. It includes production-ready features such as automatic batching, prefix caching, and memory-efficient attention mechanisms.Starting Price: $300 per month -
25
ChatGLM
Z.ai
ChatGLM-6B is an open-source, Chinese-English bilingual dialogue language model based on the General Language Model (GLM) architecture with 6.2 billion parameters. Combined with model quantization technology, users can deploy locally on consumer-grade graphics cards (only 6GB of video memory is required at the INT4 quantization level). ChatGLM-6B uses technology similar to ChatGPT, optimized for Chinese Q&A and dialogue. After about 1T identifiers of Chinese and English bilingual training, supplemented by supervision and fine-tuning, feedback self-help, human feedback reinforcement learning and other technologies, ChatGLM-6B with 6.2 billion parameters has been able to generate answers that are quite in line with human preferences.Starting Price: Free -
26
GLM-OCR
Z.ai
GLM-OCR is a multimodal optical character recognition model and open source repository that provides accurate, efficient, and comprehensive document understanding by combining text and visual modalities into a unified encoder–decoder architecture derived from the GLM-V family. Built with a visual encoder pre-trained on large-scale image–text data and a lightweight cross-modal connector feeding into a GLM-0.5B language decoder, the model supports layout detection, parallel region recognition, and structured output for text, tables, formulas, and complicated real-world document formats. It introduces Multi-Token Prediction (MTP) loss and stable full-task reinforcement learning to improve training efficiency, recognition accuracy, and generalization, achieving state-of-the-art benchmarks on major document understanding tasks.Starting Price: Free -
27
Nemotron 3.5 Lightning
NVIDIA
NVIDIA Nemotron 3.5 Lightning is an open 30B-parameter mixture-of-experts model with 3B active parameters, designed for high-volume, low-latency execution in long-running and always-on AI agents. Built for the execution layer of agentic systems, it handles frequent tasks such as tool calls, output validation, routine commands, and subagent delegation while larger reasoning models focus on planning and orchestration. Its MoE architecture activates only a fraction of parameters for each token, combining the capacity of a larger model with lower compute requirements. The model is trained for popular agent harnesses and supports speculative decoding through multi-token prediction, DFlash, and DSpark to improve inference speed across different serving scenarios. It is available with BF16 and NVFP4 checkpoints and can run from local systems such as DGX Spark and GeForce RTX hardware to data center environments. -
28
Sanctum
Sanctum
Sanctum is a private, local AI assistant that lets users run and interact with full-featured open source LLMs locally on their own device. It is built as a private Sanctum for AI, where data is encrypted, secure, and never leaves the user’s computer. It makes it easy to run AI locally, with an easy-to-download desktop solution that lets users run large language models on a Mac instantly without complicated installs, and after downloading, no internet connection is required. Sanctum is designed around privacy-first local processing: with on-device encryption and processing, users maintain complete privacy and control. Its Hugging Face integration gives access to thousands of GGUF models directly through Sanctum, making it possible to check compatibility, download models, and start using them on a PC or Mac. Sanctum also supports private PDF workflows, allowing users to chat with PDFs, ask questions, and summarize files in a secure environment. -
29
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 -
30
Atomic Agent
Atomic Agent
Atomic Agent is an open-source, local-first AI agent that runs on your own machine from the terminal. It drives your real tools — shell, files, and browser — and keeps every session on your own disk. Point it at a local model through llama.cpp, or connect a hosted model when you want more power. You choose the model, you own the data, you stay in control. No cloud lock-in. Install with a single command on macOS, Linux, or Windows. Built for developers and power users who want a capable autonomous agent without shipping their work to someone else's cloud.Starting Price: $0 -
31
Solar Mini
Upstage AI
Solar Mini is a pre‑trained large language model that delivers GPT‑3.5‑comparable responses with 2.5× faster inference while staying under 30 billion parameters. It achieved first place on the Hugging Face Open LLM Leaderboard in December 2023 by combining a 32‑layer Llama 2 architecture, initialized with high‑quality Mistral 7B weights, with an innovative “depth up‑scaling” (DUS) approach that deepens the model efficiently without adding complex modules. After DUS, continued pretraining restores and enhances performance, and instruction tuning in a QA format, especially for Korean, refines its ability to follow user prompts, while alignment tuning ensures its outputs meet human or advanced AI preferences. Solar Mini outperforms competitors such as Llama 2, Mistral 7B, Ko‑Alpaca, and KULLM across a variety of benchmarks, proving that compact size need not sacrifice capability.Starting Price: $0.1 per 1M tokens -
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LLaMA-Factory
hoshi-hiyouga
LLaMA-Factory is an open source platform designed to streamline and enhance the fine-tuning process of over 100 Large Language Models (LLMs) and Vision-Language Models (VLMs). It supports various fine-tuning techniques, including Low-Rank Adaptation (LoRA), Quantized LoRA (QLoRA), and Prefix-Tuning, allowing users to customize models efficiently. It has demonstrated significant performance improvements; for instance, its LoRA tuning offers up to 3.7 times faster training speeds with better Rouge scores on advertising text generation tasks compared to traditional methods. LLaMA-Factory's architecture is designed for flexibility, supporting a wide range of model architectures and configurations. Users can easily integrate their datasets and utilize the platform's tools to achieve optimized fine-tuning results. Detailed documentation and diverse examples are provided to assist users in navigating the fine-tuning process effectively.Starting Price: Free -
33
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 -
34
Celeris-1
Celeris-1
Celeris-1 is a low-latency, general-purpose language model platform and a diffusion model designed to deliver frontier-level intelligence at dramatically higher speed. Instead of generating one token at a time like traditional autoregressive models, Celeris uses a diffusion-based inference architecture that enables parallel generation and response times measured in milliseconds. On its published MMLU-Pro benchmark, Celeris-1 reaches 75.9 accuracy with a 158 ms median response time and 1,664 output tokens per second, placing it within a few points of frontier models while running more than 10x faster. The model is exposed through an OpenAI-compatible API, so developers can point existing SDKs and clients at Celeris with minimal code changes. Streaming is enabled for interactive applications, with responses as low as 24 ms and no buffering or batch delay.Starting Price: $0.20 per 1M tokens -
35
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 -
36
Geekflare Chat
Geekflare
Geekflare Chat is an all-in-one AI platform that bundles the world’s most powerful models from OpenAI, Anthropic Claude, and Google Gemini into a collaborative workspace. By consolidating OpenAI, Anthropic, and Google into one interface, Geekflare Chat removes the friction of modern AI. Teams can use the Multi-Model Comparison tool to evaluate responses from GPT-5.4, Claude 4.5, and Gemini 3.1 Pro side-by-side. Collaboration is built natively into the platform, allowing teams to share workspaces, build a centralized AI Knowledge Base, and standardize outputs with a shared Prompt Library. Start chatting for free, or upgrade to our Business Plan to give your entire team the AI advantage they need to move faster for just $29/month.Starting Price: $9/month -
37
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 -
38
Falcon Mamba 7B
Technology Innovation Institute (TII)
Falcon Mamba 7B is the first open-source State Space Language Model (SSLM), introducing a groundbreaking architecture for Falcon models. Recognized as the top-performing open-source SSLM worldwide by Hugging Face, it sets a new benchmark in AI efficiency. Unlike traditional transformers, SSLMs operate with minimal memory requirements and can generate extended text sequences without additional overhead. Falcon Mamba 7B surpasses leading transformer-based models, including Meta’s Llama 3.1 8B and Mistral’s 7B, showcasing superior performance. This innovation underscores Abu Dhabi’s commitment to advancing AI research and development on a global scale.Starting Price: Free -
39
LEAP
Liquid AI
The LEAP Edge AI Platform offers a full-stack on-device AI toolchain that enables developers to build edge AI applications, from model selection through inference, entirely on device. It includes a best-model search engine to find the most appropriate model for a given task and device constraint, a curated library of pre-trained model bundles ready for download, and fine-tuning tools (such as GPU-optimized scripts) for customizing models like LFM2 to specific use cases. It supports vision-enabled capabilities across iOS, Android, and laptop devices, and includes function-calling so AI models can interact with external systems via structured outputs. For deployment, LEAP provides an Edge SDK that lets developers load and query models locally, just like a cloud API, but entirely offline, and a model bundling service to package any supported model or checkpoint into a bundle optimized for edge deployment.Starting Price: Free -
40
Chatbot Arena
Chatbot Arena
Ask any question to two anonymous AI chatbots (ChatGPT, Gemini, Claude, Llama, and more). Choose the best response, you can keep chatting until you find a winner. If AI identity is revealed, your vote won't count. Upload an image and chat, or use text-to-image models like DALL-E 3, Flux, and Ideogram to generate images, Use RepoChat tab to chat with Github repos. Backed by over 1,000,000+ community votes, our platform ranks the best LLM and AI chatbots. Chatbot Arena is an open platform for crowdsourced AI benchmarking, hosted by researchers at UC Berkeley SkyLab and LMArena. We open source the FastChat project on GitHub and release open datasets.Starting Price: Free -
41
Llama 4 Maverick
Meta
Llama 4 Maverick is one of the most advanced multimodal AI models from Meta, featuring 17 billion active parameters and 128 experts. It surpasses its competitors like GPT-4o and Gemini 2.0 Flash in a broad range of benchmarks, especially in tasks related to coding, reasoning, and multilingual capabilities. Llama 4 Maverick combines image and text understanding, enabling it to deliver industry-leading results in image-grounding tasks and precise, high-quality output. With its efficient performance at a reduced parameter size, Maverick offers exceptional value, especially in general assistant and chat applications.Starting Price: Free -
42
Diskeeper
Condusiv Technologies
Diskeeper® is the #1 utility for keeping Windows servers and PCs running faster than new. Trusted by millions, Diskeeper prevents fragmentation & caches hot reads from idle, available DRAM for blazing performance! Everyone knows the frustration of a Windows system becoming progressively slower the longer it is used and burdened with more software and data. Condusiv’s Diskeeper with DRAM caching guarantees to fix worst performing physical servers or PCs with faster than new performance. Instead of "defragging," Diskeeper 18's patented engine ensures large, clean contiguous writes from Windows in real-time so fragmentation is no longer an issue for HDDs or SSDs. This eliminates the "death by a thousand cuts" scenario of small, tiny writes and reads that rob SSD and HDD performance. Diskeeper 18 electrifies Windows system performance to operate faster than new with the addition of dynamic memory caching - using idle, available DRAM to serve hot reads.Starting Price: $49.95/perpetual license -
43
Google AI Edge Gallery
Google
Google AI Edge Gallery is an experimental, open source Android app that demonstrates on-device machine learning and generative AI use cases, letting users download and run models locally (so they work offline once installed). It offers several features including AI Chat (multi-turn conversation), Ask Image (upload or use images to ask questions, identify objects, get descriptions), Audio Scribe (transcribe or translate recorded/uploaded audio), Prompt Lab (for single-turn tasks such as summarization, rewriting, code generation), and performance insights (metrics like latency, decode speed, etc.). Users can switch between different compatible models (including Gemma 3n and models from Hugging Face), bring their own LiteRT models, and explore model cards and source code for transparency. The app aims to protect privacy by doing all processing on the device, no internet connection needed for core operations after models are loaded, reducing latency, and enhancing data security.Starting Price: Free -
44
promptfoo
promptfoo
Promptfoo discovers and eliminates major LLM risks before they are shipped to production. Its founders have experience launching and scaling AI to over 100 million users using automated red-teaming and testing to overcome security, legal, and compliance issues. Promptfoo's open source, developer-first approach has made it the most widely adopted tool in this space, with over 20,000 users. Custom probes for your application that identify failures you actually care about, not just generic jailbreaks and prompt injections. Move quickly with a command-line interface, live reloads, and caching. No SDKs, cloud dependencies, or logins. Used by teams serving millions of users and supported by an active open source community. Build reliable prompts, models, and RAGs with benchmarks specific to your use case. Secure your apps with automated red teaming and pentesting. Speed up evaluations with caching, concurrency, and live reloading.Starting Price: Free -
45
NevronixAI
NevronixAI
NevronixAI introduces a groundbreaking platform that enables Face-to-Face Conversations with Large Language Models like ChatGPT and Llama. Designed specifically for brands seeking to provide a premium conversational experience for their customers, our platform redefines the way businesses interact with their audience. Our digital humans could represent your brand online, communicating with customers in real time to give them confidence in their purchases. -
46
Pioneer
Pioneer.ai
Pioneer is an inference API built for developers who would rather ship than babysit a GPU cluster. It lets teams point an existing OpenAI, Anthropic, or other client at Pioneer, keep the same API and code, and run inference like normal while Pioneer finds where the current model falls short. It clusters production traffic by use case, surfaces where accuracy, latency, or cost can improve, then builds and routes to small specialist models automatically. Its continuous improvement loop, Adaptive Inference, mines live production failures for high-signal examples, retrains a specialist model, evaluates the new checkpoint, and promotes improvements behind the same endpoint without requiring redeployment. Pioneer supports encoder models for structured extraction tasks such as named entity recognition, text classification, structured JSON extraction, privacy filtering, and safety classification, as well as decoder models for text generation, classification, open-ended prompting, etc. -
47
RefractOne
RefractOne
RefractOne is a market intelligence and AI-powered insights platform designed to transform how businesses consume, analyze, and act on information. We combine AI, advanced technology, and expert-led validation to decode complex market signals — delivering faster, smarter decisions aligned to your goals. Built for scale and impact, RefractOne serves as the growth engine behind high-performance B2B teams. Our platform offers: • Real-time, AI-powered sales intelligence and peer benchmarking • Competitive tracking and first-mover advantage tools • Voice-of-customer research and survey analytics • Innovation scouting and strategy foresight When clarity is critical and fragmented data is slowing you down, RefractOne brings the perspective and momentum to help you move decisively. Headquartered in Los Angeles, CA, we serve B2B organizations across the USA and globally. -
48
Cloudflare AI Gateway
Cloudflare
Cloudflare AI Gateway is an intelligent control plane for AI applications, built to connect to any model, dynamically route requests, and manage usage, billing, and logs from one unified gateway. It gives teams visibility and control over AI apps by connecting applications to AI Gateway, gathering insights on how people are using the application through analytics and logging, and controlling how the application scales with caching, rate limiting, request retries, model fallback, and more. AI Gateway helps reduce cost and latency by caching responses and reducing redundant API calls, so frequent requests can be served directly from Cloudflare’s cache instead of the original model provider. It improves reliability with dynamic controls that configure how and when model provider APIs are called based on attributes, fallbacks, latency, cost, or availability, with routing rules that can be adjusted from the dashboard or API without redeployments or downtime.Starting Price: $20 per month -
49
MindMac
MindMac
MindMac is a native macOS application designed to enhance productivity by integrating seamlessly with ChatGPT and other AI models. It supports multiple AI providers, including OpenAI, Azure OpenAI, Google AI with Gemini, Gemini Enterprise Agent Platform, Anthropic Claude, OpenRouter, Mistral AI, Cohere, Perplexity, OctoAI, and local LLMs via LMStudio, LocalAI, GPT4All, Ollama, and llama.cpp. MindMac offers over 150 built-in prompt templates to facilitate user interaction and allows for extensive customization of OpenAI parameters, appearance, context modes, and keyboard shortcuts. The application features a powerful inline mode, enabling users to generate content or ask questions within any application without switching windows. MindMac ensures privacy by storing API keys securely in the Mac's Keychain and sending data directly to the AI provider without intermediary servers. The app is free to use with basic features, requiring no account for setup.Starting Price: $29 one-time payment -
50
Baichuan-13B
Baichuan Intelligent Technology
Baichuan-13B is an open source and commercially available large-scale language model containing 13 billion parameters developed by Baichuan Intelligent following Baichuan -7B . It has achieved the best results of the same size on authoritative Chinese and English benchmarks. This release contains two versions of pre-training ( Baichuan-13B-Base ) and alignment ( Baichuan-13B-Chat ). Larger size, more data : Baichuan-13B further expands the number of parameters to 13 billion on the basis of Baichuan -7B , and trains 1.4 trillion tokens on high-quality corpus, which is 40% more than LLaMA-13B. It is currently open source The model with the largest amount of training data in the 13B size. Support Chinese and English bilingual, use ALiBi position code, context window length is 4096.Starting Price: Free