Life & fun · alternatives · 2026
24 alternatives to Llama2.c on a Commodore C64
Warning: Inference takes ages. Pics & Media: https://x.com/VulcanIgnis/status/1893420241310335329
Llama2.c on a Commodore C64 is a port of the Llama 2 language model that runs on vintage Commodore 64 hardware. It enables the classic 1980s computer to perform AI inference, though processing is significantly slow.… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
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Llama 2▲263The next generation of Meta's open source LLM
2023 · developer.meta.com · its alternatives →
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Llama▲3123.1-405B: an open source model to rival GPT-4o / Claude-3.5
2024 · developer.meta.com · its alternatives →
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High-speed download of LLaMA, Facebook's 65B parameter GPT model - shawwn/llama-dl
2023 · github.com · its alternatives →
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We wrote our inference engine on Rust, it is faster than llama cpp in all of the use cases. Your feedback is very welcomed. Written from scratch with idea that you can add support of any kernel and platform.
2025 · github.com · its alternatives →
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Llama 4▲422A new era of natively multimodal AI innovation
2025 · developer.meta.com · its alternatives →
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Llama 3.3 70B▲347Llama 405B-level performance, at a fraction of the cost
2024 · developer.meta.com · its alternatives →
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Fine-tune LLM agents with online reinforcement learning - KhoomeiK/LlamaGym
2024 · github.com · its alternatives →
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I spent a lot of time and money on this rather big side project of mine that attempts to replicate the mechanistic interpretability research on proprietary LLMs that was quite popular this year and produced great research papers by Anthropic [1], OpenAI [2] and Deepmind [3]. I am quite proud of this project and since I consider myself the target audience for HackerNews did I think that maybe some of you would appreciate this open research replication as well. Happy to answer any questions or face any feedback. Cheers [1]…
2024 · github.com · its alternatives →
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Here's a project I've been working on for the last few months. It's a new (I think) algorithm, that allows to adjust smoothly - and in real time - how many calculations you'd like to do during inference of an LLM model. It seems that it's possible to do just 20-25% of weight multiplications instead of all of them, and still get good inference results. I implemented it to run on M1/M2/M3 GPU. The mmul approximation itself can be pushed to run 2x fast before the quality of output collapses. The inference speed is just a bit faster than Llama.cpp's, because the rest of implementation…
2024 · asciinema.org · its alternatives →
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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 / 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 →
- 11
I've been playing around with https://github.com/zphang/minimal-llama/ and https://github.com/tloen/alpaca-lora/blob/main/finetune.py, and wanted to create a simple UI where you can just paste text, tweak the parameters, and finetune the model quickly using a modern GPU. To prepare the data, simply separate your text with two blank lines. There's an inference tab, so you can test how the tuned model behaves. This is my first foray into the world of LLM finetuning, Python, Torch, Transformers, LoRA, PEFT, and Gradio. Enjoy!
2023 · github.com · its alternatives →
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
Aug 2026 · cactuscompute.com · its alternatives →
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Ollama▲235The easiest way to run large language models locally
2023 · github.com · its alternatives →
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Hi everyone, I'm kinda involved in some retrogaming and with some experiments I ran into the following question: "It would be possible to run transformer models bypassing the cpu/ram, connecting the gpu to the nvme?" This is the result of that question itself and some weekend vibecoding (it has the linked library repository in the readme as well), it seems to work, even on consumer gpus, it should work better on professional ones tho
Feb 2026 · github.com · its alternatives →
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We believe that AI should be fully open source and part of the collective knowledge. The original LLaMA code is GPL licensed which means any project using it must also be released under GPL. This "taints" any other code and prevents meaningful academic and commercial use. Lit-LLaMA solves that for good.
2023 · github.com · its alternatives →
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A "large" language model running on a microcontroller - maxbbraun/llama4micro
2023 · github.com · its alternatives →
- 18
Hey, recently I took inspiration from llama.cpp, ollama, and many other similar tools that enable inference of LLMs locally, and I just finished building a Llama inference engine for the 8B model in CUDA C. I recently wanted to explore my newly founded interest in CUDA programming and my passion for machine learning. This project only makes use of the native CUDA runtime api and cuda_fp16. The inference takes place in fp16, so it requires around 17-18GB of VRAM (~16GB for model params and some more for intermediary caches). It doesn’t use cuBLAS or any similar libraries since I wanted to be…
2025 · github.com · its alternatives →
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A simple mobile web app inspired by Fuzzy-Search/realtime-bakllava that uses llama.cpp server backend with multimodal mode to describe and narrate what the phone camera sees. I built this thing in a few hours using a single ChatGPT thread to generate most things for me and iterate on this project. Here's the workflow: https://chat.openai.com/share/ea84ec69-5617-45e8-8772-ac2dcf...
2023 · github.com · its alternatives →
- 21
A self-hosted, offline, ChatGPT-like chatbot. Powered by Llama 2. 100% private, with no data leaving your device. New: Code Llama support! - getumbrel/llama-gpt
2023 · github.com · its alternatives →
- 22
I discovered that in LLM inference, keys and values in the KV cache have very different quantization sensitivities. Keys need higher precision than values to maintain quality. I patched llama.cpp to enable different bit-widths for keys vs. values on Apple Silicon. The results are surprising: - K8V4 (8-bit keys, 4-bit values): 59% memory reduction with only 0.86% perplexity loss - K4V8 (4-bit keys, 8-bit values): 59% memory reduction but 6.06% perplexity loss - The configurations use the same number of bits, but K8V4 is 7× better for quality This means you can run LLMs with 2-3× longer…
2025 · github.com · its alternatives →
- 23
The Emotion Engine has 32 MB of RAM total, so the trick is streaming weights from CD-ROM one matrix at a time during the forward pass — only activations, KV cache and embeddings live in RAM. This means models bigger than the RAM can still run, they just read more from disc. Had to build a custom quantized format (PSNT), hack endianness, write a tokenizer pipeline, and most of the PS2 SDK from scratch (releasing that separately). The model itself is also custom — a 10M param Llama-style architecture I trained specifically for this. And it works. On real hardware.
Mar 2026 · github.com · its alternatives →
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