DeepSeek-V4.1-Flash is a large-scale hybrid-architecture language model whose main model is about 551.566B parameters and totals roughly 748B, including an ~196.929B engram, ~14.225B DSpark/MTP and a ~0.485B vision encoder. Recent coverage highlights safetensors inspection, Artificial Analysis benchmark comparisons with Qwen and GPT‑6, and community calls to apply its KVCache and Engram ideas to let 30B-class models run in Q8/Q4 on 32GB VRAM (engram for 30B estimated at ~1/3–1/2, ~10–15 GB).
DeepSeek V4.1 Flash tops AA's new benchmarkArtificial Analysis added a private eval in Intelligence Index v4.3 replacing τ³; Astra had been scoring strongly and matched Fable, but community posts report DeepSeek V4.1 Flash took first on that eval; the index was changed twice in three days.
Hope for KVCache + Engram in Upcoming ModelsA Reddit post urges applying DeepSeek-V4.1-Flash’s KVCache and Engram ideas to medium/large models so 30B-class models can run on GPUs in Q8/Q4. The post gives approximate VRAM and Engram estimates, suggests Engram may be ~1/3–1/2 of model size (10–15 GB for 30B), and argues 32GB VRAM should suffice for many optimized models.
Defense of Artificial Analysis BenchmarksA r/LocalLLaMA post defends the Artificial Analysis aggregated benchmark, outlining its methodology and costs, and cites performance differences among Deepseek, Qwen, and GPT‑6 across subbenchmarks.
Deepseek V4.1 Flash is 748B total/552B baseInspection of the safetensors shows Deepseek V4.1 Flash’s main model is about 551.566B parameters (40 layers), with FFN experts totaling 543.582B and other parts 7.984B; additionally an engram of ~196.929B, DSpark/MTP ~14.225B and a vision encoder ~0.485B, summing to roughly 748B. Other reported sizes (e.g., 485B, 522B) stem from differing counting methods or misreads.
The A.I. Daily