nowfound

AI · February 24, 2026

LA

L88 – A Local RAG System on 8GB VRAM (Need Architecture Feedback)

Hey everyone, I’ve been working on a project called L88 — a local RAG system that I initially focused on UI/UX for, so the retrieval and model architecture still need proper refinement. Repo: https://github.com/Hundred-Trillion/L88-Full I’m running this on 8GB VRAM and a strong CPU (128GB RAM). Embeddings and preprocessing run on CPU, and the main model runs on GPU. One limitation I ran into is that my evaluator and generator LLM ended up being the same model due to compute constraints, which defeats the purpose of evaluation. I’d really appreciate feedback on:…

L88 – A Local RAG System on 8GB VRAM (Need Architecture Feedback) launched on February 24, 2026 with 12 votes, #935 of 16,627 launches that month and more than 90% of that year's launches.

Alternativestop 6% of February 2026

In plain words

L88 is a local retrieval-augmented generation system designed to run on modest hardware, using 8GB of GPU VRAM with CPU-based embeddings and preprocessing. It is for developers interested in building RAG systems on resource-constrained machines. The project emphasizes UI/UX but acknowledges that its retrieval and model architecture require refinement, including resolving the limitation where the same model handles both evaluation and generation tasks.

written from the facts on this page · September 2026

From the sources

In the maker’s words, at launch

Hey everyone, I’ve been working on a project called L88 — a local RAG system that I initially focused on UI/UX for, so the retrieval and model architecture still need proper refinement. Repo: https://github.com/Hundred-Trillion/L88-Full I’m running this on 8GB VRAM and a strong CPU (128GB…

Does a similar job

all alternatives →
  • Find the best local LLM for your hardware, ranked by benchmarksMay 2026 · github.com · ▲283

    Find the local LLM that actually runs and performs best on your hardware. Ranked by real, recency-aware benchmarks, not parameter count. One command, run it instantly. - Andyyyy64/whichllm

  • Fine-tune an 8B model on a 4 GB laptop GPUAug 2026 · github.com · ▲139

    Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU. - MakazhanAlpamys/Soup

  • 80% faster, 50% less memory, 0% loss of accuracy Llama finetuning2023 · github.com · ▲385

    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…

  • WM
    We made glhf.chat – run almost any open-source LLM, including 405B2024 · glhf.chat · ▲161

    Try it out! https://glhf.chat/ Hey HN! We’ve been working for the past few months on a website to let you easily run (almost) any open-source LLM on autoscaling GPU clusters. It’s free for now while we figure out how to price it, but we expect to be cheaper than most GPU offerings since we can run the models multi-tenant. Unlike Together AI, Fireworks, etc, we’ll run any model that the open-source vLLM project supports: we don’t have a hardcoded list. If you want a specific model or finetune, you don’t have to ask us for it: you can just paste the Hugging Face link in and…

  • Run Qwen3-Next-80B on 8GB GPU at 1tok/2s throughput2025 · github.com · ▲123

    Contribute to Mega4alik/ollm development by creating an account on GitHub.

  • LocalScore – Local LLM Benchmark2025 · localscore.ai · ▲124

    Hey Folks! I've been building an open source benchmark for measuring local LLM performance on your own hardware. The benchmarking tool is a CLI written on top of Llamafile to allow for portability across different hardware setups and operating systems. The website is a database of results from the benchmark, allowing you to explore the performance of different models and hardware configurations. Please give it a try! Any feedback and contribution is much appreciated. I'd love for this to serve as a helpful resource for the local AI community. For more check out: - Website:…

More ai this month

the category →
  • Video model that turns script into viral social media videos

    AI · 19d ago · spira.ai

  • The browser where Claude Code and Codex work beside you

    AI · 19d ago · rill.love

  • Kolibri is Aleph Alpha

    AI · 20d ago · tej.as

  • Paintings by AI models: programs run through a simulation of oil paint.

    AI · 22d ago · stillwet.art

  • Beam.win▲378

    Run AI agents on one Mac and use its windows on another

    AI · 5d ago · beam.win

  • Route requests to the right LLM for cost, latency & quality

    AI · 23d ago · fastrouter.ai

Source: Show HN post ↗ · launch text quoted in part

Launched alongside, February 2026

the whole month →
  • Rork Max▲1,428

    Best AI for iOS apps. Website that replaces Xcode

    Life & fun · Feb 2026 · rork.com

  • happycapy▲1,365

    The agent-native computer, for the rest of us

    AI · Feb 2026 · happycapy.ai

  • SuperX▲897

    All-in-one growth OS for serious 𝕏 creators

    AI · Feb 2026 · superx.so

  • KiloClaw▲868

    Hosted OpenClaw. No Mac mini required.

    Dev tools · Feb 2026 · kilo.ai

  • Feel closer and have more fun with your partner every day!

    AI · Feb 2026 · lovon.app

  • Hello my name is Ben Ward for the past 3 years I have been remastering the financial game Wall Street Raider created by Michael Jenkins originally on DOS in 1986. It has been a rough journey but I finally see the light at the end of the tunnel. I just recently redid the website and thought maybe the full story of how this project came to be would interest you all. Thank you for reading.

    Work · Feb 2026 · wallstreetraider.com