Deepseek just did the impossible
Explains Deepseek V4.1 Flash architecture covering prefill versus decode, causal encoder decoder, global versus local cache, sliding window attention, CSA2, hierarchical sparse indexer, single pass mH
| Model profile | ||
|---|---|---|
| Model | Size | Approximate size |
| DeepSeek V4.1 FlashModel source: provider1,048,576 tokens max native context | 552Bactive 16B | 331.2 GB4-bit quantization |
| Compatible hardware Estimated speeds, not benchmark results: calculated from memory bandwidth and model size. Real results can differ significantly because there is no precise formula for deriving LLM generation speed from hardware specifications alone. | ||||
|---|---|---|---|---|
| Hardware | Memory | Bandwidth | vLLM | Estimated generation |
| DGX H200 | 1128 GBGPU HBM3e | 4800 GB/s | yes | 159 tok/s4-bit quantization |
| DGX Station | 748 GBCoherent Memory | 7100 GB/s | yes | 211 tok/s4-bit quantization |
| ET900N G3 | 748 GBCoherent Memory | 7100 GB/s | yes | 211 tok/s4-bit quantization |
| DGX B200 | 1440 GBGPU HBM3e | 8000 GB/s | yes | 229 tok/s4-bit quantization |
| GB200 NVL72 | 13400 GBGPU HBM3e | 8000 GB/s | yes | 229 tok/s4-bit quantization |
| GB300 NVL72 | 20000 GBGPU HBM3e | 8000 GB/s | yes | 229 tok/s4-bit quantization |
Explains Deepseek V4.1 Flash architecture covering prefill versus decode, causal encoder decoder, global versus local cache, sliding window attention, CSA2, hierarchical sparse indexer, single pass mH
Demonstrates DeepSeek V4.1 Flash hallucinations in agentic cybersecurity and coding, explains sparse attention's role in missed context, and shows methods to reduce errors using the AA Omniscience Hal
Explains DeepSeek V4.1 Flash using DeepSeek Sparse Attention and Compressed Sparse Attention 2 to limit reads to 640 entries per layer with an 890-byte KV cache per token via the lightning indexer.
Runs DeepSeek-V4.1-MLX-Q4i via Inferencer v2.2.8 on Mac Studio M3 Ultra 512, testing math, coding, logic, office apps, canvas animations, 3D simulations, games, and image generation.
Evaluates DeepSeek V4.1 Flash via browser OS tests, C++ skate and rally games, robot arm control, Subway FPS, Blender & Godot wrestling, 3D CAD models, and watch website design.
Tests DeepSeek V4.1 Flash by building a 3D Viewer, catching an ATC bug, and solving physics problems from scratch.
Tests DeepSeek V4.1 Flash on coding, 3D simulations, Three.js, games, agentic tasks and vision, noting speed of 300–400+ tokens per second and pricing against frontier models.