Should You Run The Small Or Big DeepSeek V4.1?

DeepSeek V4.1 Flash locally: Q2 vs Q4 quantization on your Mac, speed, accuracy benchmarks, and which file actually fits in memory. DeepSeek has released one V4.1 model, Flash, and the two files most people can run at home are DwarfStar's Q2 and Q4 (DwarfStar is Salvatore Sanfilippo's free, open source engine). Both hold the same model; most of its weights are stored in 2 bits in Q2 and 4 bits in Q4. If your machine can hold Q4's main weights (about 294 GiB), run Q4; if not, run Q2 (about 152 GiB). The 189 GiB Engram table stays on your drive. Memory: only a 512 GB Mac holds Q4 fully on one machine, and Apple's US store lists that option as coming late October with no price. A 256 GB Mac holds Q2 fully; two linked 128 GB Macs or two DGX Sparks split it at about 81 GiB each; a single 128 GB Mac or DGX Spark streams Q2 from the SSD. Q4's download is about 519 GB and needs room to join its two halves, so plan on a 1 TB drive. LM Studio and Ollama list V4.1 Flash only as a cloud model, and llama.cpp support is still an open pull request. Speed: on the same 512 GB Mac Studio, kernelpool measured Q2 at 19.8 and Q4 at 18.6 tokens per second on a short chat, so Q2 is only about 6% faster when both fit. On a 128 GB MacBook Pro, where both files stream from the drive, two people measured 15.7 for Q2 and 10.2 for Q4, and a 10,000 token prompt takes two to four minutes to read versus about half a minute in memory. Accuracy: NVIDIA's own 4-bit version matches DeepSeek's original on GPQA Diamond (91.3 vs 91.0) and Terminal-Bench (82.2 vs 81.6). On DwarfStar's match-rate test (top next-token agreement with DeepSeek's replies, not correct answers; the ceiling is about 97%), Q4 scored 96.8% and Q2 90.1%, falling to about 94% and 85% on 64K and 96K token prompts. In the only side-by-side run on real questions (GPQA Diamond, SuperGPQA, AIME 2025), Q4 finished 20 of 20 and Q2 19 of 20; Q2's miss hit its 16,000 token thinking limit. Coding and long sessions are where 2-bit files have slipped, on thin evidence: diffbot's own 2-bit build (on NVIDIA cards, not DwarfStar's Q2) scored 91.5% on HumanEval against 96.3% for its 3-bit build, and one user reported Q2 looping at about 38K tokens where Q4 did not. Verdict: memory decides. With 512 GB run Q4; on anything smaller run Q2. For long documents and long coding or agent sessions, wait for the 512 GB Mac Studio or use DeepSeek's own service. The Stack did not run the model; every result shown is its owner's report. Sources and media credits: DwarfStar docs, tests and runs: github.com/antirez/ds4 (kernelpool, pull request #1073; gilbert-barajas, issue #1023; Argonaut Labs, issue #1151; MiklosPathy and kyuz0, pull request #1036) Model pages on Hugging Face: NVIDIA (DeepSeek-V4.1-Flash-NVFP4), diffbot, Lucebox; the linked Mac Studio report: antirez/deepseek-v4.1-flash-gguf discussion #8 Prices and availability: Apple Store (US), 5 October 2026 SSD install footage: FLU Film Productions, CC BY DGX Spark photo: Daniel Lu (dllu), Wikimedia Commons, CC BY-SA 4.0 llama.cpp marks: github.com/ggml-org/llama.brand Stock footage: K (Pexels), EnchantedStudios (Pixabay) Chapters: 0:00 Intro 2:15 Memory 5:28 Speed 8:47 Accuracy 11:51 Real Tasks 14:51 Conclusion Tools & resources mentioned: - DwarfStar: https://github.com/antirez/ds4 - DwarfStar V4.1 Flash GGUF files: https://huggingface.co/antirez/deepseek-v4.1-flash-gguf - DwarfStar model guide (MODELS.md): https://github.com/antirez/ds4/blob/main/docs/MODELS.md - Side-by-side runs (DwarfStar pull request #1073): https://github.com/antirez/ds4/pull/1073 - NVIDIA DeepSeek-V4.1-Flash-NVFP4 benchmarks: https://huggingface.co/nvidia/DeepSeek-V4.1-Flash-NVFP4 - diffbot 3-bit build: https://huggingface.co/diffbot/DeepSeek-V4.1-Flash-EXL3-3bpw-2x-RTX-PRO-6000 - llama.cpp V4.1 pull request: https://github.com/ggml-org/llama.cpp/pull/28696 - LM Studio - Ollama About The Stack The Stack helps you build with AI. Each video takes one tool, model, or workflow and shows how it works in a few focused minutes, with the real benchmarks and real costs. We go deep on Claude Code and Cursor for AI coding, AI agents and MCP servers, the open-source AI tools and GitHub repos most people miss, RAG and vector search, fine-tuning, and running local LLMs on your own machine with Ollama and LM Studio. We compare models like ChatGPT and Claude, test AI automation with Zapier, Make, and n8n, and flag the tools that actually ship. Subscribe for new breakdowns: https://www.youtube.com/@the-stack-ai?sub_confirmation=1 #deepseek #local ai #quantization