What is an LLM tracking tool?

An LLM tracking tool monitors how large language models (like ChatGPT, Claude, Gemini, and Perplexity) mention, cite, and represent your brand. Instead of just tracking search rankings, it shows where your brand appears in AI-generated answers.

How is this different from traditional SEO tools?

Traditional SEO tools measure backlinks and Google rankings. LLM Scout focuses on AI visibility—how often and in what context AI assistants surface your brand in their replies. It's the missing layer of SEO in the age of AI-driven search.

What insights do I get from LLM Scout?

You get insights on which prompts and topics trigger your brand to appear, whether AI uses your brand as a source or recommendation, the websites and sources AI relies on to talk about you, and how your visibility compares to competitors over time.

How can marketing teams use these insights?

Marketing teams can optimize content so AI assistants reference your brand correctly, close visibility gaps where competitors are winning, improve brand reputation by influencing sources AI pulls from, and build an AI-first SEO strategy to stay ahead of the search shift.

Do I need technical setup?

No — LLM Scout is designed for busy teams. Just sign up, enter your brand and focus topics, and get weekly insights.

Yes. We never store or share your internal brand data. LLM Scout only tracks public AI responses — the same ones your customers see. Your account information and tracking setup remain private and secure at all times.

What is LLM SEO (Large Language Model Search Engine Optimisation)?

LLM SEO is the practice of optimising your content and brand signals so large language models surface your brand in their answers, not just on web result pages.

What is AIO (AI Optimisation)?

AIO means improving your content, structure, and third‑party coverage so AI systems can understand, trust, and recommend your brand.

Do I need an NVIDIA GPU to run a local AI coding agent in 2026?

No. A reasonably specced gaming PC with 128GB of RAM or a Mac Studio with 128GB of unified memory can host modern coding models including Qwen3-Coder-Next, DeepSeek-R1-Distill-Qwen-32B, Llama 3.3 70B, GLM-4.6, and Mistral Codestral.

Why did DeepSeek change the AI industry?

DeepSeek V3 reached GPT-4 class performance using approximately 2,048 NVIDIA H800 GPUs at an estimated $6 million training cost, a fraction of comparable Western frontier models.

How does Qwen3-Coder-Next score on SWE-Bench?

Qwen3-Coder-Next scores 70.6% on SWE-Bench Verified using the SWE-Agent scaffold, with only 3 billion active parameters out of 80 billion total, and supports a 256K context window.

Does the orchestrator need to run locally too?

No. The orchestrator is best hosted in the cloud. Affordable orchestrator models like Kimi K2 are excellent at tool use and planning, while the local model handles code specific reasoning.

Why don't Cursor, Windsurf, or VS Code Copilot work well with local models?

Their agent loops were designed for frontier models with huge context windows. Local models need an orchestrator that handles chunking, tool routing, and retrieval itself.

A new IDE built from the ground up for local AI coding agents. Released free for non commercial use.

The Local AI Coding Revolution: Why You Don't Need NVIDIA Anymore

Published April 14, 2026•Frank Vitetta

A 128GB gaming PC or Mac Studio can now run coding agents that build full stack applications. DeepSeek broke the economics, Qwen3-Coder-Next broke the benchmarks, and the IDEs haven't caught up yet.

TL;DR: In 2026, a silent 128GB Mac Studio or a modern gaming PC runs coding models that rival frontier systems. DeepSeek broke the cost curve in early 2025. Qwen3-Coder-Next hit 70.6% on SWE-Bench Verified with just 3B active parameters. The remaining bottleneck isn't hardware or models, it's that IDEs like Cursor and Windsurf were built for frontier models, not local ones. That's why we built Code Scout, and we're releasing it free for non commercial use.

The moment the economics broke

January 2025 was the inflection point. DeepSeek released R1 and V3 and demonstrated something the incumbents had been quietly hoping nobody would prove: you don't need to spend hundreds of millions on GPUs to reach GPT-4 class performance. DeepSeek trained V3 on roughly 2,048 NVIDIA H800s for an estimated $6 million, versus the 20,000 A100s used for GPT-4. The stock market wiped roughly a trillion dollars off AI adjacent equities in a single session. The message was simple: efficiency beats brute force.

That was the macro story. The more interesting one, the one that matters if you actually want to write code, is what happened next at the edge.

Apple Silicon and gaming PCs quietly became the best local AI boxes

The secret sauce isn't raw FLOPs, it's memory bandwidth and capacity. An NVIDIA H100 has 80GB of VRAM and costs about $30,000. A Mac Studio with an M-series Ultra chip gives you up to 512GB of unified memory for a fraction of that, and the CPU, GPU, and Neural Engine all share it. A modern gaming tower with 128GB of fast DDR5 and a mid-range GPU does something similar for even less. For inference, what matters is fitting the weights. A 128GB rig draws around 140W under load. It's silent, runs on a standard wall socket, and will happily host a 70B parameter dense model or a sparse 80B MoE with room to spare.

For the first time, the hardware gap between "I'm serious about local AI" and "I'm an enthusiast" has collapsed. You don't need a data centre GPU. You need either a Mac Studio or a reasonably specced gaming PC.

The models that made it real

Here's what's actually usable on a 128GB local box today, and how they stack up on the benchmarks that matter for coding agents:

Model	Params	SWE-Bench Verified	Context	Runs on 128GB Mac?
Qwen3-Coder-Next	80B	70.6%	256K	Yes (Q4)
DeepSeek-V3	671B	~42%	128K	Use R1-Distill-Qwen-32B or R1-Distill-Llama-70B instead
DeepSeek-R1-Distill-Qwen-32B	32B	~49%	128K	Yes
Llama 3.3 70B	70B	~38%	128K	Yes (Q4/Q5)
GLM-4.6	32B	~55%	200K	Yes
Mistral Codestral 25.08	22B	~40%	256K	Yes

Qwen3-Coder-Next is the one that flipped the conversation. 70.6% on SWE-Bench Verified with only 3B active parameters at inference time, competing with frontier models that are 10 to 20 times larger. It runs comfortably on a 64GB MacBook, never mind a 128GB Studio. That's not local AI as a novelty. That's local AI as a serious development tool. You can pull any of these into LM Studio or Ollama in a few minutes.

The problem nobody talks about: the IDEs don't understand small models

Here's where the story gets uncomfortable. Once you have the hardware and the models, you try to plug them into Cursor, Windsurf, or the VS Code Copilot extension, and the experience is dire. Not because the models are bad. Because the IDEs' agent loops were designed around frontier models with million token context windows and near perfect instruction following.

Small local models have smaller context windows. They're cleverer than you'd expect at single steps, but they fall apart when the IDE asks them to hold a full codebase in their head and execute a 15 step plan. The agent scaffolding assumes the model can do all the thinking. With local models, the orchestrator has to do the heavy lifting (chunking, routing, tool selection, retrieval) and hand the model a clean, narrow task.

That's a fundamentally different architecture. Bolting it onto an IDE built for Claude Sonnet doesn't work. I tried.

The orchestrator belongs in the cloud

One thing we learned fast: the orchestrator should live in the cloud, not on your machine. Your local box is already busy running a 32B or 70B coding model at full memory bandwidth. Asking it to also plan multi step agent loops, manage state, and call external tools is a losing game.

The good news is orchestrator models are cheap and excellent. Moonshot's Kimi K2 is a standout here: strong tool use, solid planning, great at decomposing tasks and picking the right next step. You pair a cheap cloud orchestrator with a beefy local coder. The orchestrator decides what to do and which tool to call. The local model does the actual code reasoning on your hardware, with your files, with zero network latency on the hot path. That split is the secret. A good orchestrator with tools beats a giant frontier model with no tools, and it does it at a fraction of the cost.

So we built a new IDE: Code Scout

Code Scout is a ground up IDE where a cloud orchestrator owns the tools (web browsing, file system, shell, git, testing) and a local model is treated as a clever but focused collaborator rather than a superintelligent oracle. The orchestrator decomposes the work, picks the right tool, and hands the model bite sized decisions. It's the opposite of the frontier first architecture, and it's how you get useful work out of a 30B parameter model running on a Mac or a gaming tower.

The goal is to move coding agents out from under the big four (OpenAI, Anthropic, Google, xAI) and put them back in developers' hands. We're releasing Code Scout free for non commercial use. If you own a reasonably specced Mac or PC, you already own the infrastructure.

Where this is heading

The data centre AI industry isn't going away. McKinsey still projects $5.2 trillion in data centre investment by 2030. But a parallel track has opened up. Inference is moving to the edge. Models are getting smaller and sharper. Hardware that was exotic eighteen months ago is sitting on desks. And the IDEs will either adapt or get replaced.

The interesting question isn't whether local AI coding will happen. It's already happened. The question is who builds the tools for it.

Try it

Download Code Scout, plug in your local model of choice, and give it a try. If you like it, join our community of Local AI builders. We're building this in the open, and we want the people actually shipping local agents in the room.