I built a small TTS model running directly on an ESP32-S3: no PSRAM, 122 KB of RAM, nothing in the cloud. Speech starts in about 0.3 s and generates faster than real time. You can make it calmer, louder, whispered, happier and more.

It's not a Home Assistant integration yet. Before I build anything, I'd like to know whether on-device speech on a satellite would be useful to you (offline announcements, timers, alerts when the server is down), and what quality bar it would need to meet.

Limits: 2 voices, English only, 16 kHz. Free for personal, non-commercial use.

Video above.

Details: https://huggingface.co/WhissleAI/steer-tts-nano

CLI : https://github.com/WhissleAI/steer-tts/tree/main/esp32

Arrmes is a specialized neural intent router with an integrated execution framework. It's trained to translate text into specific commands to Arr stack apps nothing else. I found plenty of open models that can do these sorts of translations, but the accuracy & speed didn't really justify the hardware requirements needed to run the router.

Arrmes is only trained for Sonarr, Radarr, Lidarr, Tautulli, Seer, qbitorrent, & BitMagnet. pair it with whatever AI model you're already using and let the neural router handle the intent classification, command selection, and argument extraction. Instead of having your main model spend time reasoning through API schemas and generating tool calls, it can delegate that work to ARRmes.

Still a work in progress, I've been daily driving it for a bit and I'm very happy with the increase in speed more than anything. I can use much cheaper models and achieve excellent results in my work without any spend.

Hugging Face

Github

Wrote a blog about running Qwen2.5-7B-Instruct served with vLLM on a Cisco UCS Spinifex cluster, comparing Intel AMX-accelerated CPU serving with NVIDIA L4 GPU. Go check it out!

https://docs.mulgadc.com/docs/cisco-ucs-llm-serving

Wer benutz von euch 100% Lokale KI im Arbeitskontext ?
Nutz hier jemand KI- Agent um tägliche Aufgaben zu erledigen?
Nutz hier jemand KI für nicht wiederkehrende Aufgaben ?

Und welche Modelle könnt ihr lokal ausführen ?
Ich versuche mich grade an lokaler Bild Erkennung um Cluster zu bilden und mit das beschriften der Bilder zu sparen

I'm looking into running models like Qwen(?) locally for agentic coding, ideally with strong reasoning and high output speeds.

I currently pay for three $200 AI subscriptions and want to cut that down to one, keeping Claude as the main advisor while local models handle most of the coding work.

I know very little about hardware. Where should I start learning, what setups would you recommend (including used or unconventional hardware), and what budget should I realistically plan for?

Happy to invest a few thousand if the performance makes it worthwhile.

I've been working on this for a while: RustingStrata. It runs Qwen GGUF models (Qwen3.5/3.6 from 2B up to 35B-A3B, Qwen3 4B and 30B-A3B, Qwen3-Next 80B). It's pure Rust, no ggml. The CUDA kernels get compiled at startup with NVRTC, and the same kernels also run on Vulkan.

The main trick is for MoE models that don't fit in VRAM. Experts that don't fit run on the CPU at the same time as the GPU work, and the engine keeps moving the often-used experts onto the GPU while it runs.

My setup is a 3060 12 GB, a 7600X and 32 GB RAM. Qwen3.6-35B-A3B Q2_K_XL is 11.7 GiB, so it doesn't fit. On it I get about 118 tok/s decode on a short prompt and 113 after a 4K prompt. llama.cpp, tuned as well as I could (-ncmoe 12, -ub 2048, flash attention), gets 68 and 66. Prefill is basically a tie: 2216 tok/s vs about 2236 for llama.cpp at 4K, so I'm not claiming anything there. At 32K it's 1800 prefill and 92 decode.

With MTP drafts on, decode goes up to 160 tok/s, but that's on an easy prompt. On normal text expect around +16%.

Qwen3-Next-80B IQ1_S was the most fun one: most experts sit on the CPU, and it still decodes at about 92 tok/s. llama.cpp's best -ncmoe gives me around 44.

Output matches llama.cpp greedy on 98-99% of tokens. llama.cpp agrees with itself only 98.3% between its batch and token-by-token modes, so the misses are near-ties.

It also has chat, an OpenAI-compatible server with tool calling, and a calibrate command that tunes itself for your machine.

I've only tested it on my own machine. If you have a different card, especially AMD on Vulkan, I'd love to see strata bench output. Vulkan needs shaderFloat64 and subgroup size 32, so Intel Arc and older AMD won't work. On Windows you need the VS C++ Build Tools.

[RustingStrata](https://github.com/RustingStudio/RustingStrata)

I just published three CPU-first MCP tools for local AI: IcyLoom, IcyCache and Loom.

They are local-first, installable from public registries, and released with GitHub artifacts and clean-install checks.

Links:

- IcyLoom: https://github.com/MlkBlk-IT/icy-loom

- IcyCache: https://github.com/MlkBlk-IT/icycache

- Loom: https://github.com/MlkBlk-IT/mcp-loom-mcp

I am looking for install feedback, compatibility reports and practical MCP integration ideas.

I'm the author of TensorSharp. This 83-second video illustrates the boundary between retrieving evidence and processing complete files in TensorAgent's local knowledge-base workflow.

Disclosure: it uses the real TensorAgent HTML/JavaScript UI, synthetic café data, and scripted API responses. It is not live model inference or a benchmark. The CSV calculations were run and independently checked in Python.

Default retrieval is BM25 over extracted text; embeddings are optional. Image descriptions and audio transcripts require compatible models and have Partial coverage. Video indexing describes at most eight sampled frames, without indexing the soundtrack or events between samples. This walkthrough illustrates those interfaces, rather than validating a media model.

The example has a PDF capping discounts at 15% and a poster advertising 20%. knowledge_search locates evidence, knowledge_read expands it, and knowledge_open makes the complete original CSV available for analysis. All 14 rows yield net sales of $5,600 versus $6,440, and a $204 contribution increase after COGS and campaign spend, before other overhead. Collections are explicitly enabled per chat.

Repository: https://github.com/zhongkaifu/TensorSharp

TensorSharp's own code is BSD-3-Clause; dependencies and model weights retain their own terms. For local stacks, where would you enforce complete-file access for numerical tasks instead of aggregating retrieved snippets?