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AI · alternatives · 2026

24 alternatives to LoongForge

A high-performance training framework for LLM, VLM, VLA, Wan

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 1 we have checked on their own sites, 1 of them free or with a free tier.

  1. 1

    A high-performance framework for training LLMs, VLMs, diffusion, and embodied models on NVIDIA GPUs and Kunlun XPUs. - GitHub - baidu-baige/LoongForge: A high-performance framework for training LLMs, VLMs, diffusion, and embodied models on NVIDIA GPUs and Kunlun XPUs.

    May 2026 · github.com · its alternatives →

  2. 2

    Hi HN! We’re been working hard on this low-code tool for rapid prompt discovery, robustness testing and LLM evaluation. We’ve just released documentation to help new users learn how to use it and what it can already do. Let us know what you think! :)

    2023 · chainforge.ai · its alternatives →

  3. 3

    Hi HN! We're Gabriel & Viraj, and we're excited to open source TensorZero. To be a little cheeky, TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. 1. Integrate our model gateway 2. Send metrics or feedback 3. Unlock compounding improvements in quality, cost, and latency It enables a data & learning flywheel for LLMs by unifying: • Inference: one API for all LLMs, with <1ms P99 overhead • Observability: inference & feedback → your database • Optimization: better prompts, models, inference strategies • Experimentation:…

    2025 · github.com · its alternatives →

  4. 4

    APG9 hadir sebagai zona gaming terbaik dengan peluang menang besar dan bonus dahsyat di setiap putaran. Rasakan sensasi cuan sekarang juga!.

    2017 · deepforge.org · its alternatives →

  5. 5

    Build your own high performance LLM inference engine in C++ and CUDA - a smaller version of vLLM - jmaczan/tiny-vllm

    May 2026 · github.com · its alternatives →

  6. 6

    Hi HN! I'm just sharing a project I've been working on during the LLM Efficiency Challenge - you can now finetune Llama with QLoRA 5x faster than Huggingface's original implementation on your own local GPU. Some highlights: 1. Manual autograd engine - hand derived backprop steps. 2. QLoRA &#x2F; LoRA 80% faster, 50% less memory. 3. All kernels written in OpenAI's Triton language. 4. 0% loss in accuracy - no approximation methods - all exact. 5. No change of hardware necessary. Supports NVIDIA GPUs since 2018+. CUDA 7.5+. 6. Flash Attention support via Xformers. 7. Supports 4bit and 16bit…

    2023 · github.com · its alternatives →

  7. 7
    InsForge▲622

    We built InsForge because we wanted to use pure prompts to build a production-grade app faster. In practice, AI isn’t reliable enough when generating code or configuring backend pieces like schema, RLS, auth, or functions. Postgres MCP and Supabase MCP are a good starting point, but they felt more like API wrappers for agents than something designed to guide AI safely through backend tasks. So we built our own MCP server on top of Postgres and added a set of context-engineering tools to make prompt-driven workflows more predictable. It gradually turned into a Postgres-based BaaS. Key…

    Mar 2026 · insforge.dev · its alternatives →

  8. 8

    The low-code platform for testing AI apps

    2024 · langtail.com · its alternatives →

  9. 9
    LLM Gatewayfree tier▲296

    I built an OpenAI-compatible LLM gateway that routes requests to OpenAI, Anthropic, Ollama, vLLM, llama-server, SGLang... anything that speaks &#x2F;v1&#x2F;chat&#x2F;completions. Single Go binary, one YAML config file, no infrastructure. It does the things you'd expect from this kind of gateway... semantic routing via a three-layer cascade (keyword heuristics, embedding similarity, LLM classifier) that picks the best model when clients omit the model field, weighted round-robin load balancing across local inference servers with health checks and failover. The part I think is most…

    2025 · llmgateway.io · its alternatives →

  10. 10
    LLM Stats▲293

    I was overspending on GPT-4o. It was really hard to compare different models I could switch to, so I built this LLM comparison tool. It shows leaderboards, pricing, and performance data across 100+ LLMs (including all major providers and open-source models). Key features: - Live pricing comparisons - Benchmark Scores (MMLU, HumanEval, GPQA, etc.) - Context length vs cost analysis - Speed&#x2F;throughput tests across providers - Quality vs price visualizations - Open source (all data verifiable) Try it out: https:&#x2F;&#x2F;llmstats.com I'd like to know your opinion :) Tech stack: Next.js,…

    Oct 2025 · llm-stats.com · its alternatives →

  11. 11

    Fine-tune LLM agents with online reinforcement learning - KhoomeiK/LlamaGym

    2024 · github.com · its alternatives →

  12. 12

    Find your best LLM for a local inference

    2023 · its alternatives →

  13. 13

    Hey, Jared Palmer (creator of this playground) here. Really excited to ship this. I’ve been building this over the past few weeks to compare LLMs from different providers like OpenAI, Anthropic, Cohere, etc. At Vercel, I manage our Frameworks division (including Next.js, Svelte, and Turbo) and wanted to also dogfood some of the latest features in a slightly larger application. This playground takes a lot of inspiration from https:&#x2F;&#x2F;nat.dev and is built on Tailwind, ui.shadcn.com, and some upcoming Vercel products we’re announcing soon. We’re going to continue adding models to…

    2023 · play.vercel.ai · its alternatives →

  14. 14

    Hi everyone, we’re a small team, supported by Mozilla, who are working on re-imagining a UI for training, tuning and testing local LLMs. Everything is open source. If you’ve been training your own LLMs or have always wanted to, we’d love for you to play with the tool and give feedback on what the future development experience for LLM engineering could look like.

    2025 · github.com · its alternatives →

  15. 15

    Fast LLMs for low-latency and high-performance workflows

    Jun 2026 · jetbrains.com · its alternatives →

  16. 16

    ErisForge is a Python library designed to modify Large Language Models (LLMs) by applying transformations to their internal layers. Named after Eris, the goddess of strife and discord, ErisForge allows you to alter model behavior in a controlled manner, creating both ablated and augmented versions of LLMs that respond differently to specific types of input. It is also quite useful to perform studies on propaganda and bias in LLMs (planning to experiment with deepseek). Features - Modify internal layers of LLMs to produce altered behaviors. - Ablate or enhance model responses with the…

    2025 · github.com · its alternatives →

  17. 17

    Hi HN community, I have been working on benchmarking publicly available LLMs these past couple of weeks. More precisely, I am interested on the finetuning piece since a lot of businesses are starting to entertain the idea of self-hosting LLMs trained on their proprietary data rather than relying on third party APIs. To this point, I am tracking the following 4 pillars of evaluation that businesses are typically look into: - Performance - Time to train an LLM - Cost to train an LLM - Inference (throughput &#x2F; latency &#x2F; cost per token) For each LLM, my aim is to benchmark them for…

    2023 · github.com · its alternatives →

  18. 18

    I have been working in AI space for a while now, first at FAANG with ML since 2021, then with LLM in start-ups since early 2023. I think LLM Application development is extremely iterative, more so than any other types of development. This is because to improve an LLM application performance (accuracy, hallucinations, latency, cost), you need to try various combinations of LLM models, prompt templates (e.g., few-shot, chain-of-thought), prompt context with different RAG architecture, different agent architecture, and more. There are thousands of possible combinations and you need a process…

    2024 · github.com · its alternatives →

  19. 19

    Neuralink for your AI Agents - LangChain - Autogen - CrewAI - Upsonic/Tiger

    2024 · github.com · its alternatives →

  20. 20

    The LLM Evaluation Framework. Contribute to confident-ai/deepeval development by creating an account on GitHub.

    2023 · github.com · its alternatives →

  21. 21

    The LLM Evaluation Framework. Contribute to confident-ai/deepeval development by creating an account on GitHub.

    2023 · github.com · its alternatives →

  22. 22

    Hi, I was a corporate lawyer for many years working with a lot of financial services and insurance companies. In practicing law, I noticed there was a lot of repetition in the tasks I was working on even as a highly paid attorney that could be automated. I wanted to solve the problem of dealing with a lot information and data in a practical way, using AI. This motivated me to start AI Bloks&#x2F;LLMWare with my husband, who had a deep background in software and is a very early adopter of AI. We have been on this journey with our open source project LLMWare for the past 4 months, producing a…

    2024 · github.com · its alternatives →

  23. 23AT

    I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens&#x2F;sec): Overall output fluency Demo: https:&#x2F;&#x2F;llmapitest.com&#x2F; Code: https:&#x2F;&#x2F;github.com&#x2F;qjr87&#x2F;llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…

    2025 · llmapitest.com · its alternatives →

  24. 24

    The from-scratch guide to building large language models in NodeJS.

    9d ago · book.humanexperiences.io · its alternatives →

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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →