--- title: "🧪 M4 Pro Blazes Past 125 Tokens! Offline Coding Agent Model flint Debuts: Local AI Lab" description: "Afraid to send proprietary code to the cloud, but still want an ultra-fast Coding Agent to help write tests? In this episode, we introduce RecursiveSI/flint, a 39.8B offline code model running at 125 tokens per second on M4 Pro, as well as the lightweight Three.js specialist ccore/Qwen3.5-4B-3D-Coder and the biomedical-focused Carbon-X. Which one is best for your local setup? We'll tell you in a moment!" canonical_url: "https://blog.markkulab.net/en/local-ai-lab/local-ai-lab-2026-10-26" author: "Mark Ku" author_url: "https://blog.markkulab.net/en/author/mark-ku" site: "Mark Ku's Tech Notes" date_published: "2026-10-26T02:00:00.000Z" category: "On-Premise AI Lab" tags: ["local-ai", "podcast", "地端模型", "lora", "開源模型", "flint", "Qwen3.5", "Threejs", "CarbonX", "生物資訊", "地端AI實驗室"] language: "en" license: "CC BY 4.0" license_url: "https://creativecommons.org/licenses/by/4.0/" attribution: "when reusing or quoting, credit the author and link back to the original" --- # 🧪 M4 Pro Blazes Past 125 Tokens! Offline Coding Agent Model flint Debuts: Local AI Lab Hesitant to upload proprietary code to the cloud, but still want a blazing-fast coding agent to help write tests? In this issue, we introduce RecursiveSI/flint, a 39.8B offline code model that clocks 125 tokens per second on an M4 Pro. We also cover ccore/Qwen3.5-4B-3D-Coder, a lightweight model specializing in Three.js, and Carbon-X, a dedicated biomedical model. Which one is the best fit for your local setup? Let's find out! --- ## Featured This Issue ### 1. RecursiveSI/flint: A 39.8B MoE Offline Coding Agent Model Designed Specifically for Local Deployments - **What is it**: According to the official README, this is a 39.8B Mixture of Experts (MoE) code generation model optimized for local hardware and licensed under Apache 2.0. It offers extremely fast inference speeds with no commercial restrictions, making it ideal for development teams that require strict data privacy where "data never leaves the local environment." - **Where can it be used**: - **Offline Coding Agent**: Software engineers working on planes, in basements, or in closed offline environments without internet access who need an AI assistant to quickly analyze local projects, auto-fix bugs, and write unit tests. - **Sensitive Proprietary Code Development**: Enterprise development teams handling core proprietary code who need a fully local AI assistant to protect intellectual property and security, preventing code from leaking to third-party APIs. - **Low-Latency Development for Individual Developers**: Individual engineers working on a MacBook or consumer-grade GPU who want a highly responsive smart assistant that quickly generates code, supports tool calling, and has minimal response times. - **Can you run it**: The official README notes that the full BF16 weight size is around 80 GB, which can run on a single B300 or H200 GPU. However, for local hardware, official benchmarks show it can reach over 125 tokens per second on an M4 Pro MacBook Pro, and over 150 tokens per second on a 64GB NVIDIA RTX Spark. Additionally, the official release includes an NVFP4 quantized version, which is better suited for consumer-grade hardware deployments. - **Current Traction**: As of now, this model has a trending score of 886 on Hugging Face, accumulating 8.5k downloads and 926 likes. Furthermore, two quantized or repackaged versions from the same family have already appeared on Hugging Face this week, showing that community interest is rapidly heating up. - **How it compares**: The creators point out that compared to other 30B to 40B class models, Flint is specifically designed for local hardware, offline operation, and strict data privacy as a coding agent. It achieves impressive scores of 54.9% on Terminal-Bench 2.1 and 89.7% on SWE-bench Pro v2. - **How to get it**: According to the official README, developers can use `huggingface_hub` to download the `RecursiveSI/flint` weights and deploy them using the official Docker and SGLang service packages, or load them via vLLM and enable the `glm47` parser. - **Link**: [RecursiveSI/flint](https://huggingface.co/RecursiveSI/flint) --- ### 2. ccore/Qwen3.5-4B-3D-Coder: An Ultra-Lightweight 4B Model Specializing in Three.js Interactive 3D Scene Generation - **What is it**: This is an ultra-lightweight, 4-billion-parameter (4B) model. It is an experimental version independently fine-tuned by CCORE based on Qwen3.5-4B. Licensed under Apache 2.0, it is specifically designed to generate interactive 3D code that can run directly in a web browser. - **Where can it be used**: - **Rapid Development of 3D Web Components**: Front-end web engineers who want to quickly generate Three.js HTML/JS code containing interactive 3D scenes (such as a rotating radar control tower or a 3D cherry blossom garden) without writing it from scratch. - **3D Voxel-Style Scene Iteration**: Indie game developers or web designers who want to quickly iterate and preview 3D voxel-style scene models on a standard laptop using simple text prompts. - **Local Lightweight Agent Experiments**: Developers looking to use lightweight agent frameworks (such as Hermes Agent or OpenClaw) locally to automate the generation of 3D web components, completely eliminating the need to call expensive, rate-limited cloud APIs. - **Can you run it**: Since it is a 4B parameter model, the hardware barrier to entry is very low. The official README provides llama.cpp deployment instructions for the `Q4_K_M` quantized version. Standard consumer PCs, laptops, and Macs can run it smoothly, making it perfect for individual developers to deploy on the fly. - **Current Traction**: It currently has a trending score of 113 on Hugging Face, with 1.3k downloads and 115 likes, which is quite popular for a niche, specialized model. - **How it compares**: The creators note that compared to the general-purpose Qwen3.5-4B model, this fine-tuned version is deeply optimized for generating interactive Three.js 3D scenes. It can directly output a single HTML file containing cdnjs references that runs immediately in a browser, saving developers from tedious package bundling steps. - **How to get it**: The official README explains that you can use `llama.cpp` to load `ccore/Qwen3.5-4B-3D-Coder:Q4_K_M`, and start a local OpenAI-compatible server using the command `llama serve -hf ccore/Qwen3.5-4B-3D-Coder:Q4_K_M`. - **Link**: [ccore/Qwen3.5-4B-3D-Coder](https://huggingface.co/ccore/Qwen3.5-4B-3D-Coder) --- ### 3. HuggingFaceBio/Carbon-X: A 14.8B Bioinformatics Model Designed for DNA Sequence Understanding and Generation - **What is it**: This is a 14.8B parameter model specifically designed for DNA sequence understanding and generation, licensed under Apache 2.0. It can comprehend both DNA sequences and natural language, serving as a powerful open-source tool at the intersection of biomedicine and computer science. - **Where can it be used**: - **Local Analysis of Sensitive Genomic Sequences**: Bioinformatics engineers or researchers analyzing sensitive patient genomic sequences who need to predict whether specific DNA mutations (variants) are benign or pathogenic, requiring strict local data processing to comply with medical regulations. - **Bidirectional Dialogue Between DNA and Natural Language**: Biomedical developers who need an AI that understands both DNA sequences and human language, allowing users to input raw DNA sequences directly in a chat and have the AI explain their biological functions in English or predict the next base pair. - **Can you run it**: The official model card indicates that its BF16 weights are approximately 25 GB, which can be loaded and run on a single 80 GB GPU (such as an H100 or H200). According to LLM Explorer, the required VRAM is around 29.7 GB. To lower the barrier to local deployment, the creators also provide a GGUF quantized version, making it easier for developers to run on consumer-grade hardware. - **Current Traction**: It currently has a trending score of 201 on Hugging Face, with 1.1k downloads and 210 likes. - **How it compares**: The creators state that compared to the baseline Qwen3-14B model, Carbon-X improved average scores by 27 percentage points across five DNA understanding and generation benchmarks. They also point out that its average performance is close to GPT-6.1 Sol, but at only 1/1800th of the inference cost. - **How to get it**: According to the official README, developers can load `HuggingFaceBio/Carbon-X` via Transformers. Note that because it uses a custom hybrid DNA tokenizer, you must specify `trust_remote_code=True` when loading. Alternatively, you can use vLLM for service deployment. - **Link**: [HuggingFaceBio/Carbon-X](https://huggingface.co/HuggingFaceBio/Carbon-X) --- ## Summary Today's selection clearly shows that local models are evolving in two main directions: extreme specialization and hardware-specific optimization. Whether it is Flint, optimized specifically for Mac M4, or the smaller models dedicated to 3D front-end development and DNA sequencing, they all demonstrate the immense potential of local deployment. If you happen to have an M4 chip or a consumer-grade GPU on hand, why not start playing with the lightweight 3D-Coder first to experience the magic of local AI? See you in the next issue of the Local AI Lab! --- ## About this article and its author Originally published on [Mark Ku's Tech Notes](https://blog.markkulab.net/en/local-ai-lab/local-ai-lab-2026-10-26) License: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) — when reusing or quoting, credit the author and link back to the original ### About the author **[Mark Ku](https://blog.markkulab.net/en/author/mark-ku)** — Software engineer - 10+ years as a software engineer - Built North-American e-commerce and AI SaaS subscription billing ### Free tools built by the author All of these are free to use: - [Free PDF Sign Tool](https://blog.markkulab.net/en/tools/pdf-sign): Online PDF sign tool — draw, type, or upload a signature, then drag, resize, and download. Everything runs in your browser; nothing is uploaded. - [VS Code Refactory](https://blog.markkulab.net/en/tools/refactory): Refactory is a VS Code refactoring extension: 34 actions plus a 37-rule code-smell inspection layer with a Code Health dashboard, across 18 languages, backed by 534 tests. It learns your repo's conventions: where interfaces live, where DI is registered, whether 'use client' belongs. It ranks files by git churn × complexity so you know what to fix first, and hands any smell to the Claude Code already on your machine. Free to use, and your source never leaves your computer. - [DB-Kit Database Manager](https://blog.markkulab.net/en/tools/db-kit): DB-Kit is a lightweight, cross-platform database manager built with Tauri + Rust + React. Manage MySQL, MariaDB, PostgreSQL, SQL Server, Oracle, SQLite, MongoDB, Redis, Kafka, Elasticsearch and RabbitMQ from one consistent interface: passwords encrypted in the OS keychain, SSH tunnels, full CRUD, a visual query builder, stacked multi-statement result sets, cross-connection data transfer, schema & data compare with sync SQL and schema snapshots, Excel / CSV import & export, visualized execution plans, ER diagrams, scheduled backups, SQL stress testing with p50–p99 latency percentiles, a 15-rule SQL review engine, Review & Run (AI review plus per-statement backups and an auto-generated rollback script), Kafka message browsing with monitoring & alerts, an SSH terminal with SFTP / FTP, Docker / Kubernetes, remote desktop (RDP / VNC / RustDesk), file / folder compare, stored procedure integration tests, six UI languages, a built-in AI assistant (a local CLI or any Anthropic / OpenAI-compatible API; natural-language SQL, AI review and tuning advice) and the dbk CLI. Free and open source (MIT), with installers for Windows, macOS and Linux. - [VS Code Super Mermaid](https://blog.markkulab.net/en/tools/super-mermaid): Super Mermaid is a VS Code extension for beautiful Mermaid diagrams out of the box: auto-colored live preview, mouse pan & zoom, high-res PNG / SVG export, 21 templates and multiple themes. Free and open source (MIT). - [React Super Mermaid](https://blog.markkulab.net/en/tools/react-super-mermaid): react-super-mermaid is an open-source React component library: render beautiful Mermaid diagrams with a single , with built-in colorful / sketch themes, pan & zoom, in-diagram search, and high-res SVG / PNG export. Lightweight, SSR-safe, fully typed. Free and open source (MIT). - [Jira / Confluence Super Mermaid](https://blog.markkulab.net/en/tools/jira-super-mermaid): An Atlassian Forge app: write Mermaid syntax directly inside a Jira issue or a Confluence page and get flowcharts, sequence diagrams, state machines and Gantt charts. 11 diagram types, SVG / PNG export, light and dark themes, full CJK support. Runs on Atlassian: your diagrams live in your own site and the app calls no third-party service. Free, coming soon to the Atlassian Marketplace. - [Mermaid Live Preview](https://blog.markkulab.net/en/tools/mermaid-preview): Write Mermaid in your browser, see it render instantly, and share the whole diagram as a single link. No sign-up, nothing uploaded to a server, and mermaid.live share links work as-is. - [React Intl Phone Number](https://blog.markkulab.net/en/tools/react-intl-phone-number): react-intl-phone-number is an open-source React component: framework-agnostic and antd-free, with E.164 in/out, a searchable flag / country-code dropdown, configurable validation levels (strict / mobile-strict / loose), themeable CSS, and i18n — phone logic powered by google-libphonenumber. Lightweight and fully typed. Free and open source (MIT). - [Uptime Kuma Cluster](https://blog.markkulab.net/en/tools/uptime-kuma-cluster): Turn single-node Uptime Kuma into a highly available cluster: OpenResty + Lua smart load balancing, shared MariaDB state, health checks and automatic failover, plus cluster-management REST APIs. One Docker Compose command to start. Free and open source (MIT). - [AI Podcast Cut](https://blog.markkulab.net/en/tools/ai-podcast-cut): Drop in a recording and it removes fillers and stutters, levels loudness segment by segment, and sends a second agent to review every cut. Cut points snap to word boundaries and zero crossings, every splice gets a fade, and sentence-end breaths are preserved. Desktop app for Windows, macOS and Linux. MIT licensed; the Windows installer bundles ffmpeg. - [AI Video Cut](https://blog.markkulab.net/en/tools/ai-video-cut): Pick something in your video: type it (face, license plate, phone screen, logo), click points or drag a box, or let Claude Code / Codex pick it. AI Video Cut tracks it frame by frame, then mosaics or blurs it, recolors it, pins stickers and text to it, swaps a screen or poster for your own image or video, or removes it using background that other frames actually captured. Plus sequence editing, local captions and an AI assistant. Free, open-source (MIT) Windows desktop app (macOS / Linux experimental) that runs locally, nothing uploaded. - [open-pos restaurant POS](https://blog.markkulab.net/en/tools/open-pos): One computer and one receipt printer is enough to open the shop. Your data lives on your own disk, no subscription, no lock-in, MIT licensed. Money is integer New Taiwan dollars with tax split by the statutory formula, so sales + tax always equals the total. Printing goes straight over ESC/POS on TCP 9100, with no vendor driver. Tauri + Rust + SQLite desktop app, v1.0 in development. - [.NET e-commerce API template](https://blog.markkulab.net/en/tools/dotnet-ecommerce-api): An e-commerce backend API template built on .NET 10: Controller → Service → Repository layering, Autofac convention-based registration, SqlSugar for MySQL / MariaDB and SQL Server, Redis cache and message queue, Hangfire jobs, JWT auth, and an extensible payment provider module (ECPay, Newebpay). Apache 2.0. - [Open Token Monitor team AI usage](https://blog.markkulab.net/en/tools/open-token-monitor): Collect token usage and equivalent cost from every employee's AI coding tools into a hub you host yourself: PostgreSQL, org roster, dashboards compared by company and department, and a reports API. Electron and Rust/Tauri clients upload every 30 minutes. MIT licensed, one Docker command. - [ai-security-monitor defensive security](https://blog.markkulab.net/en/tools/ai-security-monitor): ai-security-monitor is a single Rust binary for your computers and servers. It finds unusual connections, rates them safe / unknown / suspicious / dangerous against 26 public threat-intel sources, and checks firewalls, open ports, vulnerabilities and 60 hardening rules. Security Hub shows the whole fleet, and Claude Code / Codex explain it in plain words. MIT, free, self-hosted. ### Daily podcasts - [Mark's Tech Insights — Daily AI News](https://blog.markkulab.net/en/category/tech-news): Daily curated AI and tech trends. Catch the latest developments via audio summaries — covering AI applications, software architecture, DevOps, and engineering practice. — RSS: https://blog.markkulab.net/feed.xml - [AI股市蝦聊](https://blog.markkulab.net/en/category/ai-stock-chat): Every trading day, an AI-analyzed take on the Taiwan stock market, delivered as a two-host conversation covering the session and the next-day outlook. — RSS: https://blog.markkulab.net/ai-stock-chat/feed.xml - [開源好物日報](https://blog.markkulab.net/en/category/open-source-weekly): A daily two-host pick of free open-source tools surfaced from real Hacker News, GitHub, and Reddit buzz — what pain they solve and the fastest way to get started. — RSS: https://blog.markkulab.net/open-source-weekly/feed.xml - [AI 運動日報](https://blog.markkulab.net/en/category/sports-weekly): Two hosts talk NBA, MLB and world sport every day — scores, records and the stories behind them, from a Taiwanese fan perspective. — RSS: https://blog.markkulab.net/sports-weekly/feed.xml - [AI 國際新聞快報](https://blog.markkulab.net/en/category/world-news): A daily two-host briefing that makes sense of the past 24 hours in world news — geopolitics, the global economy, conflict and security, disasters and climate — from a Taiwanese perspective, neutral and fully sourced. — RSS: https://blog.markkulab.net/world-news/feed.xml - [地端 AI 實驗室](https://blog.markkulab.net/en/category/local-ai-lab): Every day, a two-host look at open-weight models and LoRAs you can actually run on your own machine: what they are for, whether your GPU can handle them, and how they compare — sourced from official model cards. — RSS: https://blog.markkulab.net/local-ai-lab/feed.xml - [AI 行銷日報](https://blog.markkulab.net/en/category/ai-marketing): Every day, two hosts break down the latest in AI for marketing, advertising, SEO/GEO and content marketing: practical, never sponsored, every story sourced. — RSS: https://blog.markkulab.net/ai-marketing/feed.xml - [AI 資安日報](https://blog.markkulab.net/en/category/ai-security): Every day, two hosts cover the latest where AI meets cybersecurity: LLM and AI agent vulnerabilities, prompt injection, model supply-chain risks and AI-powered attacks, from a defense-first angle with every story sourced. — RSS: https://blog.markkulab.net/ai-security/feed.xml ### Newsletter [Subscribe to the newsletter](https://blog.markkulab.net/en/subscribe) — Be the first to know about new posts. 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