@Saboo_Shubham_: OPEN SOURCE AI is killing it. DeepSeek v4 Flash is a quasi-frontier model with a massive 1M context window. It can LOCA…
Summary
The article highlights DeepSeek v4 Flash as a quasi-frontier open-source model with a 1M context window, noting its ability to run locally on a 128GB Mac using 2-bit quantization.
View Cached Full Text
Cached at: 05/09/26, 06:14 PM
OPEN SOURCE AI is killing it.
DeepSeek v4 Flash is a quasi-frontier model with a massive 1M context window.
It can LOCALLY on a 128GB Mac using specialized 2-bit quantization.
Asked my OpenClaw Engineering Agent Ross about the model and he’s impressed. https://t.co/wq9x62Mwty
Similar Articles
DeepSeek-V4-Flash 284B on 5.3GB of memory
A developer showcases Mference, a new inference engine that runs MoE models like DeepSeek-V4-Flash on just ~5.3GB of memory by streaming experts from SSD, with a native Mac app and OpenAI-compatible server.
@BrianRoemmele: Another day and another full frontier model running on your computer. Been teething DeepSeek V4 Flash on over 60 employ…
Brian Roemmele reports that DeepSeek V4 Flash (304B, 1M context) now runs locally on Apple Silicon via the ds4 engine, sharing GGUF quantized builds with a fresh imatrix. The Hugging Face repo provides installation instructions and notes that these files are ds4-specific, not for llama.cpp.
I CANNOT believe I've got DeepSeek-V4-Flash-0731, a frontier model, running on my home PC. Insane!
A user expresses astonishment at running DeepSeek-V4-Flash-0731, a frontier model, on a mid-range Windows PC with 24GB VRAM via quantization, highlighting rapid progress in local AI.
deepseek-ai/DeepSeek-V4-Flash-DSpark
DeepSeek releases V4 series of Mixture-of-Experts language models (Pro 1.6T/49B activated, Flash 284B/13B activated) supporting one-million-token context with hybrid attention and speculative decoding, claiming best open-source model performance.
deepseek-ai/DeepSeek-V4-Flash
DeepSeek releases DeepSeek-V4-Flash and DeepSeek-V4-Pro, new MoE language models supporting 1 million token contexts with improved efficiency and performance.