01What it can do
- Massive native context up to ~1M tokens
- Efficient MoE: ~13B active out of 284B, reasonable throughput for its size
- Specialized in code and coding agents
- Permissive MIT license
- —~165 GB of VRAM in Q4: requires multiple GPUs or a lot of RAM
- —Community repack, not an official DeepSeek build
- —No Ollama tag — install via HuggingFace
04Install
Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.
02Required memory
Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.
What hardware do you need for DeepSeek V4 Flash Coder 284B-A13B (MoEspresso V2)?
To run DeepSeek V4 Flash Coder 284B-A13B (MoEspresso V2) locally with Q4 quantization, you need about 165 GB of VRAM. An option to compare: BOSGAME M5 128GB / 2TB (Ryzen AI Max+ 395) — this model exceeds this mini-PC's GPU capacity: choose a smaller model or suitable infrastructure.
Why this choice? Our complete guide on BOSGAME M5 128GB / 2TB (Ryzen AI Max+ 395) →
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03Expected speed
Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.