I've built a locally running Perplexity clone
The video demo runs a 7b Model on a normal gaming GPU. I think it already works quite well (accounting for the limited hardware power). :)
In plain words
LLocalSearch is a free, open-source search aggregator that runs locally on consumer gaming hardware. It uses a 7-billion parameter language model to function as a Perplexity alternative, aggregating search results through LLM agents without requiring cloud services. Designed for users who want private, offline search capabilities, it demonstrates functional performance on standard gaming GPUs despite the computational constraints of local operation.
written from the facts on this page · September 2026
Pricing, as stated on its site
Free — LLocalSearch is a free, open-source locally running search aggregator using LLM Agents. No pricing model is associated with the product itself; the pricing page shown is GitHub's platform pricing, not
checked September 2026 · prices change
Does the same job
all alternatives →- WMWe 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…
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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…
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This is another one of my automate-my-life projects - I'm constantly asking the same question to different AIs since there's always the hope of getting a better answer somewhere else. Maybe ChatGPT's answer is too short, so I ask Perplexity. But I realize that's hallucinated, so I try Gemini. That answer sounds right, but I cross-reference with Claude just to make sure. This doesn't really apply to math/coding (where o1 or Gemini can probably one-shot an excellent response), but more to online search, where information is more fluid and there's no "right" search engine + text…
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We've built InferX, a specialized runtime environment that fundamentally changes how LLMs are served. The core problem we solve is the latency bottleneck in AI inference, especially with large models. Current systems waste resources or suffer from painfully slow cold starts. InferX's AI-native architecture, with its "snapshot" technology, enables: * *Sub-2s cold starts:* Spin up models instantly. * *High density:* Serve more LLMs on the same GPUs. * *Optimal efficiency:* Maximize GPU utilization. This isn't just another API; it's a new execution layer designed from the ground up for the…
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Hey HN, I've been using LLM-powered search engines like Perplexity a lot recently for question answering but had two major qualms: 1. Too much text: I'm already tired of wading through LLM-generated meaningless filler when debugging my code. The last thing I need is more of it when I'm just trying to find a simple answer. 2. The Reddit factor: I've realized that half my search queries have "Reddit" appended to them, especially for experiential questions or recommendations. This led to Just the Answer. Just the Answer uses GPT-4o to generate a unique search query for each message that is…
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