DeepSeek-V4.1-Flash 552B Multimodal MoE Model: ~570GB Memory

September 10, 2026November 3, 2026

At a Glance

Values determined by this site’s code when the information was collected. Dates are JST.

What we checked ourselves

• The same Japanese text takes about as many tokens as with the Qwen3 tokenizer.

Details and conditions are in “Our Own Measurements” below.

Overview

deepseek-ai has released “DeepSeek-V4.1-Flash", a multimodal Mixture-of-Experts (MoE) model with a 552B backbone parameter count, on Hugging Face. It supports a context length of up to 1 million tokens, natively processes images and text, and generates text autoregressively.

Specifications

• Parameter Count: 552B backbone (active parameters: 8B during prefill / 16B during decode)
• Architecture: MoE (Causal Encoder-Decoder, 1 shared expert, 384 routed experts, 6 routed experts activated per layer)
• Context Length: Up to 1 million tokens
• License: mit

Performance

Comparison tables with base models and frontier models as presented in the model card and technical report are shown below.

Next, a restricted comparison table with frontier models (at maximum inference effort settings) is shown below.

According to the evaluation results published by the authors, DeepSeek-V4.1-Flash achieves the highest scores in its scale category and among comparisons in coding fields (HumanEval, BigCodeBench), competitive programming (Codeforces), and agentic tasks (such as Terminal-Bench and DeepSWE v1.1). While it also demonstrates high scores in general knowledge and reasoning like MMLU-Pro, it falls behind DeepSeek-V4-Flash and DeepSeek-V4-Pro in some items such as long-context tasks (LongBench-V2) and multilingual math (MGSM).

Strengths and Use Cases

• Multimodal Support: Multimodal tasks that process images and text simultaneously (DocVQA, RefCOCO, etc.)
• Coding and Agent Tasks: Extremely high performance in Python tasks involving multiple library calls, as well as terminal operation and software engineering agents
• Long-Context Processing: Supports context lengths up to 1 million tokens, improving cost efficiency through KV cache compression technologies (such as CSA2 and FP4 main KV cache)
• Inference Effort Control: Supports a “continuously controllable reasoning effort" setting that allows adjusting inference cost and accuracy using an integer from 1 to 100

Hardware Requirements

Estimated requirements (calculated by Local Model Watch) — 763.2B parameters

Inference engine support (architecture name matched against each project’s own model registry in its source code, checked 2026-11-03): llama.cpp: registered, vLLM: registered, MLX (mlx-lm): registered.

Memory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model’s authors. Compare with other models in our VRAM quick reference. What the quantization names mean: glossary.

Can You Run It Locally?

Not usable in Ollama, LM Studio and llama.cpp yet — we have found no GGUF build.

The publisher ships safetensors only. However, llama.cpp’s registry does list this architecture, so conversion to GGUF is possible and the model will run once someone publishes a converted build. 46 converted build(s) from other uploaders exist. Today it can be run with transformers or vLLM, using the memory figures in the table above.

License — mit (Commercial use allowed): Permits commercial use, modification and redistribution, provided the copyright notice and license text are retained.

Compiled by this site’s code from the published formats, converted builds we have found, and each engine’s own model registry. “Not found" means we have not seen such a build, not that none exists. License summaries are not legal advice — check the publisher’s original terms before relying on them.

Our Own Measurements

Values we measured ourselves on our server (no GPU) by actually reading and running this model’s files — not figures copied from the model card. How we measure, and the results for every model: Our Measurements.

Japanese Token Efficiency

The same Japanese text takes about as many tokens as with the Qwen3 tokenizer.

Counted with the tokenizer.json of deepseek-ai/DeepSeek-V4.1-Flash on a fixed text we wrote ourselves (876 Japanese characters across news, conversation, technical docs, a formal email, travel writing and a recipe) and its English translation. Fewer tokens mean more Japanese fits in the context window.

Recent Models in the Same Size Class

Models with over 40B parameters that Local Model Watch covered recently, listed by code from our article log for comparison. VRAM tiers are this site’s estimates; licenses are as stated on the model cards.

How to Get It

• Distribution Format: safetensors / transformers
• How to Get: Available for download from the Hugging Face repository (deepseek-ai/DeepSeek-V4.1-Flash). No license agreement is required for access (gated: false).

Quantized and Converted Variants

In addition, 46 converted build(s) from other uploaders exist on Hugging Face; this site lists only builds from the model’s publisher or established quantization maintainers.

This section is appended automatically by Local Model Watch when a converted build of this model appears after publication. Memory figures are estimated from the size of the distributed files. What the quantization names mean: glossary.

Related Articles

• DeepSeek-V4-Pro-0813 Text Generation Model: ~998GB Memory, GGUF Builds

What to Read Next

• Find models by VRAM (This model does not fit even in 80GB; its smallest build needs about 570GB) → VRAM quick reference, including models over 80GB
• Explore the same model family → DeepSeek-V4.1 family overview (1 articles, 1 converted builds)
• Engines that run this model → vLLM
• What FP8 mean and where to get this model → NVFP4 / MXFP4 format guide and models / Quantization and model-format glossary
• Learn about the publisher → DeepSeek: models, licenses and articles

Sources

• https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash

Update History

• 2026-09-17: Added converted builds to “Quantized and Converted Variants”: nvidia/DeepSeek-V4.1-Flash-NVFP4
• 2026-09-24: Rebuilt the article: Added a “can you run it locally?" verdict from the distribution formats, converted builds, engine registries, license terms and measured compression
• 2026-09-25: Changed the title to show what the article covers (VRAM requirements, file list, etc.).
• 2026-09-28: Added our own measurements: Japanese token efficiency.