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). :)
I've built a locally running Perplexity clone launched on April 3, 2024 with 669 votes, #25 of 4,347 launches that month and more than 99% of that year's launches. The site was still online in September 2026, 2 years on.
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
From the sources
LLocalSearch is a completely locally running search aggregator using LLM Agents. The user can ask a question and the system will use a chain of LLMs to find the answer. The user can see the progress of the agents and the final answer. No OpenAI or Google API keys are needed. - nilsherzig/LLocalSearch
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 a similar job
all alternatives →
Perplexity Labs2025 · perplexity.ai · ▲638We built a search engine interface on top of OpenAI GPT 3.5 and Microsoft Bing that summarizes and cites top search results in response to natural language questions. By using search results, the AI is able to reference recent news and provide citations for specific facts. Our interface offers concise answers, without having to click through links, scroll past irrelevant content, or read ads. No login is required; no personal data is collected. We believe in the power of combining the intuitive UI of web search with the intelligence of large language models. The search engine does indexing…
- 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…
80% faster, 50% less memory, 0% loss of accuracy Llama finetuning2023 · github.com · ▲385Hi 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…
Find the best local LLM for your hardware, ranked by benchmarksMay 2026 · github.com · ▲283Find 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
SelfHostLLM2025 · selfhostllm.org · ▲134A simple calculator that estimates how many concurrent requests your GPU can handle for a given LLM, with shareable results.
50+ LLMs on 2 GPUs with 2-Second Swapping? We built AI-Native Runtime2025 · github.com · ▲5We'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…
Source: Show HN post ↗
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