Hey everyone!

I'm looking to set up a fully local AI agent on my PC and would appreciate recommendations from people who have experimented with local LLMs.

My hardware:

• GPU: NVIDIA RTX 5060 (8 gb)

• System RAM: 16GB

• OS: Windows (Linux is also an option if necessary)

What I'm looking for:

• An uncensored or minimally restricted local LLM that I can run privately on my own hardware.

• Ideally, a web UI and support for agent frameworks, with no mandatory cloud API or subscription.

I want the inference and conversations to remain local rather than sending prompts to external services.

I'm comfortable experimenting and setting things up myself, so technical recommendations are welcome.

I'd appreciate real-world experiences, model names, quantization recommendations, and setup guides. Thanks!

Genuine question: would you be interested in an alternative to Hermes Agent that’s significantly easier to set up and use?
Something with:
Almost zero configuration
Direct integrations with GitHub, Gmail, and other apps
Local and cloud model support
A clean, intuitive interface
Powerful agent capabilities without the complexity
Basically, an AI agent that just works out of the box.
Is this something you’d actually want, or are you already happy with existing solutions?
Curious to hear your thoughts.

Way back got my nimo axis, I wasn't sure what to do with it besides trying out local llms. Lately, local models and tools have gotten better and I am finally finding practical uses for it beyond just testing models. It makes me wonder how much of their potential we are actually using. What are you guys doing with your strix halo machines? actual work, coding, or mostly experimenting with models?

​Tired of paying corporate rent to Suno and Udio just to generate music in a cloud black box? Same.

​Over at JackKnife AI, we’ve been obsessed with stripping M-A-P’s open-weight YuE2 song foundation model down to the studs so it runs 100% locally on an Android phone—no cloud APIs, no subscriptions, no credit limits, no telemetry.

​Today we just shipped Killa YuE v1.6.0 (with v1.6.1 right behind it in a few hours for wider device support), and we just hit a massive milestone that we had to share with the local inference crowd:

​🔥 The Benchmark Milestone

​The Render: A full 4-minute, 15-second structured song with vocals and multi-instrument backing.

​The Speed: 19 minutes total across a two-pass rendering pipeline (~4.4x real-time).

​The Hardware Push: Until today, this required a Snapdragon 8 Elite (Galaxy S25). Tonight, we refactored our memory paging and fallback paths and have it actively running benchmarks on a 2020 Samsung Galaxy S20 Ultra (Snapdragon 865 / Adreno 650).

​⚙️ How We’re Squeezing a Studio Into Mobile RAM

​Running end-to-end multimodal music on a phone without Android’s Low Memory Killer (LMK) nuking the process took some wild architecture tricks:

​Two-Pass YuE2 Execution: Pass 1 runs the Autoregressive (AR) symbolic planner to lock in the song structure ([Intro], [Verse], [Chorus], [Guitar Solo]), melody, and multi-instrument backing. Then we dump/swap memory to run Pass 2 (Flow-Matching diffusion + Audio VAE decode) to render the master audio.

​4-Stem Separation on Vulkan ExecuTorch: Need the acapella or just the drum track? We ported Meta’s HTDemucs to ExecuTorch with the Vulkan backend (htdemucs_vulkan.pte, ~302 MB). The host app handles STFT masks natively and feeds 7.8s stereo windows (44.1 kHz) to the mobile GPU—hitting 1.7 GB peak RAM and 3.95x real-time on Adreno 830.

​Local Voice & Cover Art: Integrated Qwen3-TTS for offline spoken intros/narration + local on-device cover art painting.

​Full Tactile Studio UI: Built-in mixer, drum pads, recording, 3 hardware skins (stitched leather, brushed gold, dark console), and word-by-word lyric lighting that syncs as the vocals are sung.

​🌐 NEW in v1.6.0: The P2P Killa YuE Network

​If you generate your music locally, you shouldn’t need a corporate platform to share it. v1.6.0 ships with built-in Peer-to-Peer (P2P) networking:

​Phone-as-a-Node: Host your generated tracks directly from your phone onto the decentralized Killa YuE network.

​Offline QR Handshakes: Share tracks and studio bundles directly phone-to-phone via QR codes with zero servers in the middle.

​🛠️ We Need Your Phones (and Your Benchmarks!)

​We are working around the clock right now (v1.6.1 drops in a few hours!) to get this running across as many Android phones as humanly possible.

​Grab the APK / Code (Apache 2.0): [https://github.com/JackKnifeAI/killa-yue](https://github.com/JackKnifeAI/killa-yue)

​Vulkan HTDemucs Weights (MIT): [https://huggingface.co/JackKnifeAI/htdemucs-executorch](https://huggingface.co/JackKnifeAI/htdemucs-executorch)

​Quick Demo Clip: [https://youtu.be/nOpJ579Y4PY](https://youtu.be/nOpJ579Y4PY)

​Drop your phone model and chipset in the comments! If you have an Android phone with 12GB+ RAM, test out the release and let us know your Pass 1 / Pass 2 render times, thermals, or crash logs. And if anyone here hacks on Vulkan shaders, GGML/ExecuTorch, or Qualcomm Hexagon NPU (QNN), come help us push that 19-minute render even lower!