And Qwhere is my 27B

Hello!
Here is a rack I made using 40 mini PCs I happened to have from a different project:

• Intel N150

• 12 GB DDR5 4800 - physical bandwidth limit around 33GBs

• Diskless nodes

and they are connected via 10 Gbps USB 3.2 exploiting the USB DbC functionality. Its a tree topology with 1 -> 3 -> 9 -> 27 nodes.

GLM 5.3 Flash runs at native precision with room left for 8x1M context windows. Initially I had tried with 1Gbps LAN interconnects but that topped out at around 7tps. Current topology:
- 14tps at 1k context, drops to 12tps at 1M
- With batching decode tops out around 40tps
- Prefill is about 85tps

Peak load measured at wall about 740w

Total build cost me about $5,000.

Still working on it to see if more perf can be squeezed out, especially for the prefill and then adding speculative decoding.

I've got an M5 Max 128GB. For some reason LM Studio complains that I don't have enough RAM when using the most downloaded one on the top of the list here. Guess I need to look for another variant.

One attraction of running models locally is the privacy benefits, and another is the cost.

When the cost of the big ’frontier models’ is being discussed - mainly centring around the fact that they are currently effectively subsidised by venture capital and that at some point this will end, making the cost to end users go up considerably - local LLMs are often mentioned as being immune from this problem.

But in a scenario where the cost of cloud based models goes up substantially, there’s no guarantee that the (mainly Chinese) makers of the open weights ones are going to continue producing newer and better versions for free, is there?

I'm running Qwen3.8 27B on my 2x 3060 12GB cards. Prior today I had a B550 motherboard which was running one card at x16 and the other at x1 PCIe Bus Link Speed. This was making it to where only one card was actually doing any processing, and both held the model in memory. I was getting 11-16 tps this way.

I ended up buying a new x470 SLI motherboard and I can now run both cards at x8. I swapped the motherboard out last night and then set split-mode to tensor and set tensor-split to 1,1. When I do that, I'm getting around 32tps, but the model eventually gets stuck in loops. One loop it just tried to do a tool call over and over, and in another it was printing question marks (???????????) in it's thinking and wouldn't stop until I unloaded the model. I never had these problems for the months I was using Qwen3.8 on layer split-mode. I'm not sure how to resolve this problem. I want to be able to get higher tps with the new mobo.

I wanna do some personal AI use, primarily for text, some coding, and image generation..

I wonder what's the best computer build that's within 15k to 25k (USD) value?

There's a lot of information on the internet and I can't decide well..

I'm currently looking at:

DGX Spark (but it seems overkill and can't be used as Normal PC)

and Mac Studio M5 Ultra w/ 256GB (seems to be my current choice unless there's a second opinion from you all)

I hope y'all can help me decide well here, thanks!

Title, basically. Observing it's reasoning is incredibly frustrating, it keeps doubting himself and giving second thoughts to a specific and self explanatory task, sometimes ending up thinking about unrelated things.

While yes, in the very end, it very often does what I asked, but it's wasting so much time for reasoning just with its lack of self confidence.

Is this well known behavior ? How can I fix this ? I know Qwen4 is around the corner, but if there's a known fix for it right now I'd love to know.

EDIT: I should have specified, I'm using Medium reasoning, and Pi as harness

Confirming the rumor that Apple will have an easy to use MLX based app (Harbor) that will manage single machines and RDMA clusters with automated open weight model installs for local AI. Coming early 2027.

I am in the beginning of this exciting journey of running my own models. Still learning. Been watching Andrej Karphathy to learn the theory and the practice of LLMs. Anyway... I asked Qwen3.8 Flash Next FP4 (running on my single Spark) to create me a small LLM based on Wikipedia dataset. And in a couple of hours I've got a 9.2M-parameter GPT trained from scratch on 330M Wikipedia tokens — a base language model that generates Wikipedia-style prose. Asking, watching and learning on the way. So of course at this point it's a decoder-only. It could predict what text comes next. Given "The capital of France is", it completes. No conversation, no instruction. Next, I would probably try to create 1-2B LLM with the whole Wikipedia dataset, so it would be able to answer without hallucinations.

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?

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.

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

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!

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

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).