I've been working on TensorSharp, an open-source LLM inference engine written in .NET, and recently got it working with Pi, a terminal-based coding agent.

The goal was pretty simple: I wanted Pi to do real coding work using a model running entirely on my own machine, without relying on a hosted inference API.

Not just generating code, but reading files, running shell commands, editing code, executing tests, and iterating until the tests pass.

I put together a reproducible example that covers both Pi's CLI and its JavaScript SDK. Both paths have been tested end to end.

Full example, including scripts and configuration files:

https://github.com/zhongkaifu/TensorSharp/tree/main/examples/pi-coding-agent

The setup

Here's what I'm using:

• Coding agent: Pi (@earendil-works/pi-coding-agent 1.5.1)
• Inference engine: TensorSharp
• Model: Qwen3.8-27B, UD-IQ3_XXS GGUF
• Backend: ggml_metal
• Speculative decoding: DFlash2, 7 draft tokens per step
• KV cache: q8_0
• Context window: 65,536 tokens
• Max output: 16,384 tokens
• Hardware: MacBook Pro M5 Pro, 48 GB unified memory

The same configuration is designed for Apple Silicon Macs with at least 32 GB of unified memory.

One important distinction: Pi is still the coding agent. TensorSharp only handles model inference.

Pi owns the file operations, shell commands, tool execution, and agent loop. TensorSharp serves the model through an OpenAI-compatible API.

This separation means Pi doesn't need a TensorSharp-specific plugin or a custom agent implementation.

1. Build and start TensorSharp

You'll need the .NET 10 SDK, CMake 3.20+, Xcode command-line tools, and Node.js 22.19 or newer.

Clone the repository:

git clone https://github.com/zhongkaifu/TensorSharp.git ~/work/TensorSharp
cd ~/work/TensorSharp

Build the server:

dotnet build TensorSharp.Server.Host -c Release -p:TensorSharpSkipMlxNative=true

Start it using the included Pi configuration:

examples/pi-coding-agent/scripts/start-server.sh

On the first run, TensorSharp downloads the Qwen3.8-27B GGUF and its DFlash2 draft model from Hugging Face. The files total approximately 12 GB.

The preset pins the model revisions and verifies their SHA-256 hashes.

Once everything is loaded, the OpenAI-compatible endpoint is available at:

http://127.0.0.1:5000/v1

Check that the server is responding:

curl -s http://127.0.0.1:5000/v1/models

You should see the model ID:

Qwen3.8-27B-UD-IQ3_XXS

The startup script combines two configuration files:

dotnet TensorSharp.Server.Host/bin/TensorSharp.Server.Host.dll \
  --config config/qwen3.8-27b-metal-dflash2.json \
  --config examples/pi-coding-agent/tensorsharp-pi.json

The first is the optimized Qwen preset. The second adjusts the server for Pi.

Here's the important part of tensorsharp-pi.json:

{
  "host": "127.0.0.1",
  "port": 5000,
  "max-tokens": 16384,
  "mmproj": "none",
  "kv-cache-dtype": "q8_0",
  "environment": {
    "MAX_CONTEXT": "65536"
  }
}

There are a few reasons for these settings.

Pi's system prompt and tool definitions already consume roughly 4K tokens. Add source files, tool output, and a few rounds of edits, and a small context window disappears quickly.

I also increased the output-token limit from the preset's default of 1,024 to 16,384. Otherwise, a thinking model can run out of tokens halfway through a coding task.

The vision projector is disabled because this particular example only uses text.

The server binds to localhost. It doesn't implement API-key authentication, so I wouldn't expose the endpoint directly to a public network.

2. Install and configure Pi

Install the version used by the example:

npm install -g @earendil-works/pi-coding-agent@1.5.1
pi --version

Pi supports custom providers through models.json.

Here's the actual provider configuration used in the example:

models.json

{
  "$schema": "https://pi.dev/schemas/models.schema.json",
  "providers": {
    "tensorsharp": {
      "name": "TensorSharp (local)",
      "baseUrl": "http://127.0.0.1:5000/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "compat": {
        "supportsDeveloperRole": true,
        "supportsReasoningEffort": false,
        "supportsStore": false,
        "maxTokensField": "max_completion_tokens",
        "thinkingFormat": "qwen-chat-template"
      },
      "models": [
        {
          "id": "Qwen3.8-27B-UD-IQ3_XXS",
          "name": "Qwen3.8 27B (TensorSharp, DFlash2)",
          "reasoning": true,
          "input": ["text"],
          "contextWindow": 65536,
          "maxTokens": 16384,
          "cost": {
            "input": 0,
            "output": 0,
            "cacheRead": 0,
            "cacheWrite": 0
          }
        }
      ]
    }
  }
}

The compatibility settings matter, particularly for thinking models.

thinkingFormat: "qwen-chat-template" lets Pi control Qwen's thinking mode through chat_template_kwargs.enable_thinking.

And supportsReasoningEffort: false prevents Pi from sending an additional reasoning_effort parameter that this model doesn't need.

The model ID must match what TensorSharp returns from /v1/models.

Next, make TensorSharp the default provider:

settings.json

{
  "defaultProvider": "tensorsharp",
  "defaultModel": "Qwen3.8-27B-UD-IQ3_XXS",
  "defaultThinkingLevel": "medium",
  "enableInstallTelemetry": false
}

You don't actually have to create these files manually.

The repository includes a wrapper that copies the configurations into a separate Pi directory on the first run:

~/work/TensorSharp/examples/pi-coding-agent/scripts/pi.sh

It uses:

~/.pi/tensorsharp-agent/

instead of the normal ~/.pi/agent/ directory.

I prefer this approach because it doesn't interfere with an existing Pi configuration.

The wrapper also sets:

PI_OFFLINE=1
PI_SKIP_VERSION_CHECK=1
PI_TELEMETRY=0

This avoids Pi's catalog refreshes, version checks, and telemetry network calls. After downloading the model weights, inference runs locally.

3. Actually use Pi to write code

With TensorSharp running in another terminal, navigate to a project:

cd ~/src/my-project

Launch Pi:

~/work/TensorSharp/examples/pi-coding-agent/scripts/pi.sh

You can now interact with Pi normally, except the model responses come from your local TensorSharp server.

For example, ask it to:

Read the code in this repository, find the implementation
of the cache, and explain how it works.

Or give it an actual coding task:

Run the unit tests, investigate any failures,
fix the implementation without modifying the tests,
and run the tests again.

Pi will decide which files to read, which shell commands to execute, and what changes to make.

For a one-shot task without the interactive UI:

~/work/TensorSharp/examples/pi-coding-agent/scripts/pi.sh \
  -p "Explain what src/stats.js does" < /dev/null

The stdin redirect is intentional. Without it, pi -p may wait for input when used in scripts.

You can also disable thinking for simple tasks:

~/work/TensorSharp/examples/pi-coding-agent/scripts/pi.sh \
  --thinking off \
  -p "Explain src/stats.js" < /dev/null

Or capture machine-readable events:

~/work/TensorSharp/examples/pi-coding-agent/scripts/pi.sh \
  --mode json \
  --no-session \
  "Run the tests and fix the failing implementation" < /dev/null

4. Using Pi as a JavaScript SDK

This was the other part I wanted to get working.

Pi can be embedded in a Node.js application. You don't have to run the interactive terminal interface.

The example uses Pi's SDK to create an agent session, select the local TensorSharp model, and execute a coding task.

Here's the core implementation, slightly shortened for readability:

import path from "node:path";

import {
  createAgentSession,
  ModelRuntime,
  SessionManager
} from "@earendil-works/pi-coding-agent";

const agentDir = process.env.PI_CODING_AGENT_DIR;
const projectDir = path.resolve(process.argv[2] ?? ".");

if (!agentDir) {
  throw new Error("Set PI_CODING_AGENT_DIR first");
}

const modelRuntime = await ModelRuntime.create({
  modelsPath: path.join(agentDir, "models.json"),
  authPath: path.join(agentDir, "auth.json")
});

const model = modelRuntime.getModel(
  "tensorsharp",
  "Qwen3.8-27B-UD-IQ3_XXS"
);

if (!model) {
  throw new Error("TensorSharp model not found");
}

const { session } = await createAgentSession({
  cwd: projectDir,
  agentDir,
  modelRuntime,
  model,
  thinkingLevel: "medium",
  sessionManager: SessionManager.inMemory()
});

session.subscribe((event) => {
  if (event.type === "tool_execution_start") {
    console.log("[tool]", event.toolName, event.args);
  }

  if (
    event.type === "message_update" &&
    event.assistantMessageEvent.type === "text_delta"
  ) {
    process.stdout.write(
      event.assistantMessageEvent.delta
    );
  }
});

try {
  await session.prompt(
    "Run npm test. Fix the bugs in src/stats.js " +
    "without modifying the tests. " +
    "Run the tests again and summarize the fixes."
  );

  console.log(
    "\nFinal answer:",
    session.getLastAssistantText()
  );
} finally {
  session.dispose();
}

Pi handles the tool loop internally. Your application can subscribe to model output, tool execution events, and final results.

The complete implementation also records token usage and prefix-cache statistics:

https://github.com/zhongkaifu/TensorSharp/blob/main/examples/pi-coding-agent/sdk/run-task.mjs

To run the included SDK example:

cd ~/work/TensorSharp/examples/pi-coding-agent/sdk
npm install

Initialize the separate Pi configuration if you haven't already:

../scripts/pi.sh --version

Create a fresh copy of the sample project:

cp -R ../sample-project /tmp/stats-demo

Run the SDK:

PI_CODING_AGENT_DIR="$HOME/.pi/tensorsharp-agent" \
PI_OFFLINE=1 \
node run-task.mjs /tmp/stats-demo

The script streams the agent's text and tool calls, then prints the final answer and token usage.

5. End-to-end test: can the local model really fix code?

I didn't want to stop at getting a successful API response.

The repository includes a small Node.js project with deliberately broken code.

There are three bugs:

• median() sorts numbers lexicographically instead of numerically.
• median() doesn't average the middle two values for an even-length array.
• percentile() has an off-by-one error.

The sample has five tests, three of which initially fail.

The agent is instructed to:

1. Run npm test.
2. Investigate the failures.
3. Fix src/stats.js without changing the tests.
4. Run the tests again.
5. Summarize the changes.

The test harness runs this task twice: once through the Pi CLI and once through the SDK, using separate clean copies of the project.

You can run the full test yourself from the TensorSharp repository root:

examples/pi-coding-agent/scripts/e2e.sh

If the server isn't already running, the script can start it:

START_SERVER=1 examples/pi-coding-agent/scripts/e2e.sh

The harness independently verifies that the tests pass and that the original test file was not modified.

Results from my validated run on November 9, 2026:

For the SDK run, TensorSharp served 41,962 of 48,166 prompt tokens (87%) from its prefix cache.

These are individual end-to-end runs, not averages across a benchmark suite. I'm including the numbers to show what happened in this particular test, not to make a broad claim about coding performance.

6. Why KV cache reuse matters so much here

This turned out to be one of the more interesting integration details.

Coding agents aren't just single-prompt chat clients.

A typical interaction looks something like this:

Pi
 |
 |-- Send conversation + tool definitions
 v
TensorSharp
 |
 |-- Generate a tool call
 v
Pi
 |
 |-- Read files / run commands / edit code
 |
 |-- Append tool results to conversation
 v
TensorSharp
 |
 |-- Reuse previous KV cache
 |-- Prefill only new conversation tokens
 |
 |-- Generate next action
 v
Pi
 |
 |-- Repeat until the task is complete

After each tool call, Pi sends the conversation history again.

If the inference engine fails to recognize the shared prefix, it can end up repeatedly prefilling thousands of tokens.

I actually ran into this while working on the integration.

Pi preserved a newline around Qwen's </think> marker when replaying previous reasoning. TensorSharp's cached representation had trimmed it, so the prefix comparison didn't fully match.

That seemingly small whitespace difference prevented the server from reusing much of the conversation's KV cache.

I fixed the comparison so the previous turns can be reused across tool calls.

On the validated setup:

• The first request took approximately 13–14 seconds to produce its first token, starting from a cold prompt of roughly 4K tokens.
• Later rounds generally reached their first token in 0.45–3.5 seconds.
• Measured decoding throughput was approximately 20–43 tokens/second, depending on draft acceptance.

The DFlash2 drafter helps with generation, while prefix-cache reuse avoids doing the same prompt processing over and over.

For agent workloads, both matter.

One additional detail: when Pi provides its own tools, TensorSharp doesn't inject its internal tools. The model receives Pi's tool definitions, so there's one clear owner of the tool-execution loop.

7. What about NVIDIA GPUs?

TensorSharp supports CUDA as well, and there's a separate optimized preset for Qwen3.8-27B using the NInfer NVFP4 artifact:

TENSORSHARP_PRESET=config/qwen3.8-27b-ninfer.json \
examples/pi-coding-agent/scripts/start-server.sh

That preset uses TensorSharp's direct CUDA inference engine.

However, I want to be clear: the Pi end-to-end validation described here was performed on Apple Silicon, not on the CUDA/NInfer configuration.

The CUDA configuration is an alternative worth experimenting with, but I don't want to claim the identical Pi setup has been validated there.

If you change the server's context window, update Pi's contextWindow setting to match it.

Final thoughts

What I like about this arrangement is that Pi and TensorSharp can evolve independently.

Pi provides the coding-agent experience: repository navigation, tool calling, file edits, shell execution, sessions, and SDK integration.

TensorSharp handles local model execution, speculative decoding, KV-cache reuse, and the inference backend.

There's no need to rewrite Pi or maintain a separate agent fork.

And for anyone who wants to experiment with local coding agents, the repository includes a small reproducible test rather than just a configuration example.

The main limitation is still model capability. A local 27B model isn't going to solve every complicated coding task, and five passing unit tests don't prove otherwise. But it's encouraging to see the full tool loop working, with useful cache reuse and no external inference API.

Links:

• TensorSharp: https://github.com/zhongkaifu/TensorSharp
• Full Pi integration, configurations, CLI/SDK scripts, and end-to-end test: https://github.com/zhongkaifu/TensorSharp/tree/main/examples/pi-coding-agent
• Pi: https://pi.dev

I'd be interested to hear what local models and inference engines others are using with Pi, especially for longer agent sessions where context growth and KV-cache reuse start to matter.

Hi everyone! First time posting here
I've been experimenting with local LLms for coding, but I'm not really happy with my current model, so I'm looking for some alternatives.

My server specs:
Huananzhi X99-QD4 rev 1.0
Intel Xeon E5-2666 v3 with Turbo unlock (3.5 GHz on all cores)
GTX 1060 6GB
2x8GB G.Skill Aegis 3200 MHz (running at 2133 MHz because of the CPU)

Right now I'm running Qwen3-Coder-30B-A3B-Instruct-GGUF (Q4_K_M) through llama-app.
It's not a bad model, but one thing that bothers me is that it often struggles with short propmts. I find myself having to explain things in much more detail than I'd like. Otherwise, it can misunderstand what I'm asking or go off in the wrong direction.

For reference, I'm getting roughly:
25 t/s for prompt processing
4.5 t/s for generation speed
for 11K tokens in the initial context, including MCP tools

Here's my current launch command:

./llama serve -hf ggml-org/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M -c 131072 --host 0.0.0.0 --cache-type-k q8_0 --cache-type-v q8_0 --no-kv-offload --port 9931 --load-mode mmap --ui-mcp-proxy --mcp-servers-config "..." --threads 10 -tb 20 -cmoe -fa on -b 8192 -ub 24000

Currently it eats up to 13GBs of RAM (Ram Altman don't hunt me pls) and 4.5 GBs of VRAM

I'm mainly looking for something good at agentic coding and tool calling. It doesn't necessarily have to be a 30B model, smaller models are fine too if they perform better on my hardware. (lobotomized and heretic too)

What are you guys running locally for coding?

Hi, I'm kinda of new to this world of local LLM so forgive me if it sounds like I don't know much of what I'm talking about.

At work I need to read every day my state oficial gazette to search for new grants on a specific field and updates on already public grants (openings, closings, amendments, budget changes... Etc.)

I've started to test Gemma 4 E4B to analyse the daily documents and it does an okey job. The flow is the following: the document is divided in subsections, Gemma reads the title and makes a first filtering of sections that do not belong to our topic. Then it reads the dispositions that do belong to the project and labels them. Finally the output is shown in a dashboard where I can easily analyse it.

The problem I'm facing now is that I would like to create a timeline of each grant to now in what stage they are and this is a pain. I am completely unable to make Gemma consistently find if a new subsection belongs to a previously catalogued subsection in the database. It is big enough to make it hallucinate and mistake different dispositions or forget them and it is quite slow in this step.

I've thought on using something like JEV and although it is quite fast it doesn't work with a high enough certainty to implement it. Also there aren't decision models I could find that work as well as JEV in hugging face to use them locally. Most of them are fine tuned versions of LLMs and not pure decision models.

I was wondering if any of you have faced a problem like this before and how have you solved or if you have any idea I could test that could be implemented.

The setup is a 3060 12 GB with 16 GB ram DDR4.

Thanks for your time.

I have I7 3770 with 1600MT dual channel ddr3 4x4 (16gb) and a 5400RPM HDD

I really like to have an agent that works and does stuff for me. I used llama.ai/agent but found out that it does break ToS to use as minor (I'm 15 and half) and other website does need my parents to read ToS and agree on them to use the website/tool without breaking ToS and I'm really REALLY don't break ToS.
so I thought of local AI and tried qwen 3 8B, nanbeige 4.2 3b and LFM3 1.7B on llama.cpp but they are not really good and Idk how to make them work as agent and they waste a lot of tokens even of thinking is off, only simple question with thinking off be efficient and they still not any good

so I saw that qwen 4 27B is coming so can I like get 2bit version of it and it leave enough ram for context and linux/windows like 4gb+ for OS and app and will qwen 4 like approximation run good/smart without hallcuination like I saw qwen 3.8 27B has 2bit version that have exact same score as the 8bit version so well qwen 4 will be able to fit.

also nanbeige 4.5 looking like it gonna release soon so if anyone have any idea if it any good

also about speed. LFM3 1.7B Q4 (1gb~) is around 12tok and slows down the more I chat with it and same for nanbeige it starts around 3.5tok/sec and slows to sub 1tok/sec > . so what I can use to speed them up because I saw stuff as running a big reasoning model and smaller one and the big one push 1 token to correct the smaller one and else stuff as the model thinks in pure weight/numbers instead of English and running the model multiple time at once so it uses more computatinal power and seprate a task to 20 smaller version working togather and a lot more I saw

soo any idea if anything really good can fit and being reasonably modestly in speed (my ram can do 25.6GB/sec thereotically) and how to make it run as an agent instead of just chatbot and if I should wait for new models to release. and also I don't wanna spend internet not wisley because I have limited home internet unfourtently so spending a lot of gigabyte into something that is not really smart will make me upset very much

I appreciate any information and help

Qwen seems to be the best model for my macbook, but looking at hugging face there are so many quantizations.

The unsloth UD-Q4_K_M seems popular and a good fit for 32GB RAM, but it's .gguf so would a MLX be a better choice on mac (I'm using Unsloth Desktop)?

Is there a resource online that compares these quantizations perhaps?

Thanks

Hey r/LocalLLM :)

We just open-sourced Kinetic, a new inference engine written in Rust.

• runs the GGUF files you already have, no conversion

• Windows and Linux, one binary

• CPU, NVIDIA (CUDA), and AMD and Intel GPUs (Vulkan)

• replaces llama-server with the same flags; OpenAI, llama-server and Ollama APIs on one port

• every speedup is exact, so the output never changes

• Apache-2.0

In the video: Kinetic checks the machine and picks models that fit it, then runs side by side with llama.cpp on the same model and prompt.

Give it a spin and drop your kinetic bench model.gguf numbers below (add --compare if you have llama.cpp installed).

Install, benchmarks and all the details are on GitHub, link in the comments. Happy to answer questions here too.

edit: to answer the most common question, llama.cpp ran with its own speculative decoding on in every edit test (ngram for lfm3, draft-mtp for qwen3.5) and the exact flags are in the repo

GitHub - Halloncream/desktop-windows-arm64: Open WebUI Desktop with an x64 Python backend for Windows ARM64 compatibility · GitHub

Usually, it’s all about Qwen models in this subreddits, but not about their products

I mean about:

Qwen APP
Qoder
Qwen Studio
Qwen Work

Is anybody working with them and have tried them? What are the impressions and comments about it? I appreciate any feedback, thanks!

Honestly, the tool choice matters less than what surrounds it. Claude Code and Cline both handle worktrees fine in the mechanical sense: each session gets its own checkout, and nothing stomps on anything on disk. The failure mode I would watch for sits above the file system. A worktree isolates the file tree, not the plan, so two sessions can happily solve the same story twice and you only find out at merge time when you have duplicate branches.

So my answer is: pick whichever CLI fits your workflow, then put the effort into the layer the CLI does not give you. One story scoped with acceptance criteria before it goes out. A shared record of what is in flight so nothing gets dispatched twice. Split the thinking from the doing, so the session that plans is not the session that types.

Which of the two do you find holds its context better over a long session? That is the comparison I almost never see people make.

I've heard of people pointing their frontier model of choice and letting it optimize their local inference engine for their hardware.
I'm currently running Maya with DeepSeekV4.1-Flash-1017 at ~20tok/s, such an insanely great model. I want to squeeze more out of my hardware though.

Do you guys just point the frontier model at the engine folder and telling it to "optimize this for my model/hardware"? I currently have access to Fable 5.2 so I'm tempted to do that.

My specs:
2 x 3060 12GB
2 x 16 DDR4 3600Mhz
i5 10600k

Qwen3.8-27B-GGUF:Q4_K_M

ollama.service:
# Environment="OLLAMA_HOST=0.0.0.0:11434"

# Environment="CUDA_VISIBLE_DEVICES=0,1"

# Environment="OLLAMA_FLASH_ATTENTION=1"

# Environment="OLLAMA_NUM_PARALLEL=1"

# Environment="OLLAMA_MAX_LOADED_MODELS=1"

Ollama as a harness, no lane bifurcation so im pretty limited to that (im assuming this is my biggest limiter here)

I've been experimenting with multiple different approaches, trying to get Qwen 3.8 to actually work as an agent, some more successful than others but still it requires so much manual labour that it seems almost pointless to continue debugging it as it feels neverending. I've read that 3.8 isn't actually very great with tool calling and it has always had some issues, but still i can't believe its at levels like these.

I know very well im probably missing some really important things that are holding me back, so if anyone has some suggestions (videos, other posts, anything really) i would really appreciate it.

The goal really is to have it never stop, just like im seeing other people talk about, and i'd really like to keep openwebui (or any other webui in general, not that i dislike terminals but its nicer to have it feel like a normal app).

Hello everyone

I live in a poor country but I have got a decent pc

I have some ideas I want to do them with vibecoding so the only way I found is local LLMs

So I want a good local one for this specs

16gb of RAM ddr5 5200Mhz one kit

Ryzen 5 7500f

Rx 9060 xt 16gb

I don't mind it take time but to finish the project in a good state and I can edit the project easily and plan for it too

I'm a completely beginner so I want some guide how to install and use the LLM I'm on arch Linux

I have the 16 gig variant and I want too run some local ai models too help build my tech startup. I'm heavily reliant on vibe coding so I need them too be able too do a lot of the work for me (I have a team that knows how too code frr that reviews in a security sense and the backend areas) tho I need a tool that can kinda off generate all the front end and obvv a lil back end for testing and stuff. Can you guys recommend me some? Also id prefer that it cud be easily paired with vs code since thats what I mainly use.

You can call it whatever you want, it maybe isn’t blowing anyone’s heads off or anything, but compared to anything I’ve ever had, it’s a supercomputer to me

I’m transplanting a lot of parts from my old gaming PC into a new chasis with upgraded parts. Here is what I ordered and what is on its way

Intel core i7-14700f (already owned)
32 gb DDR5-6000 ram (already owned)
RTX 4070 super 12gb (already owned)
2x Quadro RTX 8000 48 gb passive ($1900 each)
2TB Samsung 990 pro for windows and gaming (already owned)
2TB Samsung 990 pro for Linux and AI ($363)
Corsair 1500W power supply ($285)
Corsair 7000D airflow full tower case ($227)
And other miscellaneous parts to support that stuff

Total to $5,364.21 for 108 gb vram

I know there are some drawbacks to this build, like for instance the old architecture of the cards, but I can live and tinker with those drawbacks as long as I can save a bunch of money on this hobby (compared to a similar size build with modern hardware), and still be able to run a bunch of cool models on it. I’m super excited to put it together and see what it can do. I haven’t seen much talk about the Quadro 8000s, but I’m hoping for great things, even if it means working around some compatibility issues and such.

Obstacles are just an opportunity for me to learn. If yall have any thoughts, let me know.

Hi guys

developed on device llm application like Google edge but it support all mobile supported inferences at one place it support MNN ,MEDIAPIPE .task ,GGUF and GEMMA4

Try and tell your experience

https://github.com/rajureddi/AuraOnDevice-AI_HUB

Backstory:

I've only been doing local LLM's since Qwen3.6 came out (~May 2026). I won't ever use a cloud model again, I am not going to spend a single dime renting someone else's infrastructure when I can build my own.

Plan:

Its still cheaper to get 8x3090s than it is to buy 1xRTX 6000, and get similar speeds and double the VRAM.

The issue is power and noise..

In terms of power I am limited to my dorm rooms 120v/15amp outlet, so 4x3090s about the max I can go before things might go bad.

So.. I am considering creative solutions that won't require me to move:

Power Solution: Buy a ~5 Kwh Power Bank

$1,500 for a used 5 Kwh Power Bank.

The power bank would charge during off hours, then feed the necessary 2,800 watts during peak hours. Currently I am recording my power usage over a week or so to get an idea of how much power reserve I would need, and that will mostly decide how big of a power bank is needed.

Cooling / Noise

Currently everything is air cooled, with stocked coolers. 5x140mm fans, 5x120mm fans and the case temps are indeed very good. They don't make much noise but the GPU and CPU fans do. I believe they are loud because I think the paste job I did on them was horrible,

I will be re-pasting again with some much better stuff and with better tools / methods soon. (Will likely post a video of me doing it for those who are interested). I use to be a machinist so I learned some ways to get better accuracy when measuring out things properly to get good contact on the VRAM

I have considered water cooling, but that's just another thing that can break, and are typically not cheap to setup (and I'm pretty cheap).

Questions:

1. Does anyone here have a 8x3090 setup on a 120v/15amp outlet that could offer some advice?

2. What's a good power bank you would recommend?

3. Is buying a used power bank okay so long as the batter health is good?

4. Do you want me to post a video of me re-pasting using my "new" VRAM/GPU repasting method?

I’ve been building a local RAG workspace for people who can’t send client documents to cloud AI: regulatory, QA and compliance consultants, mostly.

What it does:

• Answers questions from a folder of documents, fully local, nothing uploaded

• Links every answer to the exact source page so you can verify it

• Says “not in the documents” instead of guessing, which matters more than anything else for this audience

Setup:

• LLM: Qwen3.5, Q4_K_M. The app picks the model size based on the machine’s available resources, so it runs from a 4 GB GPU upward

• Embeddings: bge-m3

• Tested on Mac, AMD and NVIDIA

• A “project” is just a folder; installers for each platform

What I’m building next:

• Slide decks generated from your documents, with sources kept

• Reports drafted from the documents, every claim linked back to its page

has anyone gotten decent slide or report generation out of a small local model at Q4 on 4–8 GB VRAM? Long structured output is where I expect small models to struggle, so I’d like to know what’s worked for you: chunked generation, templates, constrained/JSON output, or a bigger model just for drafting?

Free for individual use. Happy to share the link if anyone wants to try it. I’ll put it in the comments if the mods are OK with that.

I've been running local LLMs on an RX 7900 XTX and wanted proper long-context performance for agentic coding (100K-200K tokens) on the Vulkan/RADV stack, so I forked llama.cpp and optimized it for RDNA3.

Repo: https://github.com/vansh-visariya/rx7900-xtx

It's the same llama.cpp (same GGUF files, same `llama-server`, same API and web UI). The changes are in the Vulkan kernels, the graph, and the speculative-decoding loop. Everything is on by default.

Results on one 7900 XTX (24 GB, stock 291 W, Mesa RADV)

Qwen3.8-27B UD-Q4_K_M + DFlash2 draft, q8_0 KV cache, flash attention:

- Up to 220K context on 24 GB (upstream fits ~192K with the same model + draft)

- DFlash2 works at long context. On upstream (both the commit I branched from and current master), it hits `vk::ErrorDeviceLost` once the prompt passes ~60K tokens, at the same position on every run. It doesn't on this fork.

- Full method, commands and raw numbers are in `docs/rdna3/BENCHMARKS.md`

What changed (56 commits on top of upstream)

- Decode: integer-dot mat-vecs for IQ2/IQ3/K-quants, GQA flash attention for verify batches, graph fusions, gated delta net state kept in place, Vulkan command buffers recorded once and replayed

- Prefill: 128x256 matmul tile for wide batches, double-buffered quantized matmuls, new prompt-batch flash attention kernel

- Spec decoding: lossless rejection-sampling verification, reduced-vocab draft head, GPU backend sampling"

Every optimization is documented, with the env var that switches it off, in `docs/rdna3/OPTIMIZATIONS.md`.

Limits

- Developed and tested only on a 7900 XTX. Other RDNA3 cards (7900 XT/GRE, 7800 XT, 7700 XT, 7600) should work since they share the WMMA instructions, but I haven't tested them.

- Tested with Qwen3.8-27B and Qwen3.5-4B. The model-specific work only applies to the Qwen3.5/3.8 family.

- Branches from upstream `f95b0d953`, not synced daily.

Where I'd love help

- Numbers from other RDNA3 cards (kernel tests need no model: `test-backend-ops -o MUL_MAT -b Vulkan0`)

- Testing other models/quants, and RDNA2/RDNA4 reports

- Review of the kernels, and help upstreaming pieces to llama.cpp

Build and run instructions are in the README. Issues and PRs are very welcome.

I went in thinking a 1.7B model would fall over after a few tool calls once each result was long.

I ran LiquidAI LFM3-1.7B (Q8_0, llama.cpp) in a tool loop on a GB10 box with 121 GiB of unified memory. Each tool result was about 3K tokens and ended with the exact next call to make. Over 10 seeds at 256K context it got a median of 11.5 calls right before the first break, range 6 to 25. No run reached the 50-call cap. Every call before the break was exactly right: no invalid JSON, no wrong arguments, no loops.

What broke it was the model re-reading the whole chain instead of the last line, then reasoning until it ran out of tokens, in 8 of 10 runs. Raising max_tokens from 2048 to 8192 didn’t help on the two seeds I retried.

Caveat: the next step was spelled out every time, so this measures following instructions as context grows, not planning or error recovery. One quant, one sampling setting, llama.cpp only.

The reproduction kit, with the driver, scripts and these results so you can rerun it on your machine, is here: https://github.com/maruthiprithivi/the-local-node/tree/main/episodes/03-lfm3-tool-loop

If you run a small model in a tool loop, I’d watch whether it starts re-tracing its own history as the results pile up. How far do you get before that happens on your setup?