unsloth v0.1.909-beta — DeepSeek-V4.1 & FLUX-3

The headline of this Unsloth release is local support for DeepSeek-V4.1-Flash-1017, which the release notes describe as an update to DeepSeek's V4.1-Flash with better reasoning, coding and agentic results and shorter outputs (GitHub). The useful detail is how the model fits on a single machine. According to the notes, it has 552B parameters plus another 196B in what the notes call its Engram table, with only about 16B of them active, and a 1M-token context window. With the Engram table left on SSD instead of in memory, Unsloth says the model runs in about 125 GB at 1-bit quantization or about 300 GB at 4-bit, and it publishes GGUF files and a guide for doing so.

For background on what that table is, earlier SemiAnalysis coverage describes Engram as learned lookups for recurring multi-token patterns that extend ordinary token embeddings, an approach it argues lowers the high-bandwidth memory a model needs. That is the part of the model Unsloth's setup keeps on disk. The same release also adds Black Forest Labs' FLUX.3 Image Klein image model, multi-GPU fine-tuning with FSDP2 in alpha, and a one-command MCP setup for coding agents, but the memory figures above are what determine whether this DeepSeek model is runnable on a given workstation.