antirez's DS4: Running DeepSeek V4 Flash with 1M Context on Mac Metal and DGX
Redis creator Salvatore Sanfilippo (antirez) just released a new project called DS4 on GitHub. The goal: get DeepSeek V4 Flash running with a 1M token context window on Apple Silicon (Metal) hardware. He also posted a video of it running on an NVIDIA DGX system.
What DS4 Does
DS4 leverages novel techniques to fit a 1M context window for DeepSeek V4 Flash on Mac Metal hardware (e.g., M-series chips). It's also been demonstrated on a DGX, suggesting it could work on high-end GPUs like the Pro 6000 at slightly smaller context windows with higher speed. There's speculation about future AMD support.
What's Included
- Server endpoints: The DS4 server already provides OpenAI and Anthropic-compatible API endpoints, making it easy to plug into agentic coding tools like Cursor, Continue.dev, or custom agents.
- GitHub repo: https://github.com/antirez/ds4/ — check the README for setup instructions, which likely involve compiling with Metal support and downloading the DeepSeek V4 Flash weights.
- Video demo: A few hours ago, antirez posted a video on X showing it running on a DGX: https://x.com/antirez/status/2053381973226184749
Who It's For
Developers with high-end Mac hardware (e.g., Mac Studio, MacBook Pro with M1 Max/Ultra or M2/M3) or NVIDIA GPUs who want to run a powerful local LLM with a very large context window for coding agents or research.
Community Call to Action
The Reddit poster encourages anyone with powerful hardware to check out the project and contribute — whether by testing, reporting bugs, or optimizing for AMD GPUs. The project is early stage, so community involvement could accelerate compatibility.
📖 Read the full source: r/LocalLLaMA
👀 See Also
OpenClaw memory loss fix using Mem0 plugin
OpenClaw agents experience memory loss due to context compaction rewriting files like MEMORY.md. The Mem0 plugin solves this by moving memory outside the context window with auto-recall and auto-capture features.
Exploiting LLM Hidden Agency Signal (Â) for Better Tool Calling
A developer discovered that LLMs have a linearly separable hidden state direction called  that predicts tool calls with AUC > 0.94. Using this signal to force tool calls improved Qwen3-1.7B performance from 26.7% to 85% (+58% gain) and reduced no-tool failures from 43% to 2.6%.
ScreenMind: Local-First AI Memory That Indexes Your Entire Computer Activity
ScreenMind captures your screen, meetings, and voice notes using Gemma 4 E2B locally via llama.cpp. Runs on 4GB+ VRAM with Q4 quantization. Search past activity, chat with history, and connect to Claude/Cursor via MCP.
Atlas: World Labs' Omni World Model for Spatial Intelligence
World Labs introduces Atlas, an omni world model that natively handles text, images, video, and 3D. It enables camera-controlled generation, spatial reconstruction, and space-time simulation.