DeepSeek-V4.1 Guide: VRAM Requirements

September 25, 2026September 27, 2026

About This Model

DeepSeek-V4.1-Flash is a large MoE model from DeepSeek. It has 552B backbone parameters and activates 8B–16B per token. The model card lists additional components such as “Engram" memory (196B parameters, looked up sparsely depending on the token), and Hugging Face lists 763.2B parameters in total. It supports contexts of up to one million tokens and is natively multimodal, having learned images and text together from the start of pre-training. It was trained from scratch on 45T tokens.

It also lets you set reasoning effort as an integer from 1 to 100, so you can trade speed and cost against accuracy for each use case.

What Makes It Stand Out

1. It uses far less memory for long contexts. The memory needed to hold the context (the KV cache) is 890 bytes per token—about 1/4 of the previous DeepSeek-V4-Flash and about 1/437 of the first DeepSeek model. This comes from a new attention design (CSA2) that shares and reuses KV across layers, combined with storing the KV cache itself in 4 bits. It is aimed at handling million-token contexts with practical amounts of memory, which is why the model card is subtitled “Pushing the Limits of KV Cache Compression."

2. On agentic tasks it stands alongside closed frontier models. An excerpt from the publisher’s comparison, at maximum reasoning effort:

• At roughly a third of the size, it beats the previous flagship V4-Pro (1.6T backbone) on most of these benchmarks.
• On Terminal-Bench 2.1, DeepSWE and AutomationBench, it matches or beats Claude Opus 5.0 and GPT-5.6 Sol.
• On harder tasks, though, the gap to Opus 5.0 is clear: 13 points on Terminal-Bench 3.0, 20 on 4.0, 17 on ProgramBench, 11 on NL2Repo-Bench, which builds whole repositories (64.0 vs 75.3), and nearly 20 on HLE without tools (36.8 vs 56.3). It is not the top model on the knowledge-heavy GPQA either. A fair reading: level with the frontier on well-known tasks, with a remaining gap on the hardest ones.

Running It Locally

• This is not a model for consumer GPUs. At 763.2B parameters in total, it needs hundreds of gigabytes of memory even when quantized. The KV-cache savings matter mainly when serving long contexts on large servers.
• This release does not include a Jinja-format chat template, which many tools rely on. Instead, the publisher provides a Python reference implementation (encoding) and a Rust library (deepseek-recipe); you will need those or wait for your inference engine to add support.
• The license is MIT, which allows commercial use.
• Articles in this family include an FP8 build from a third party (dealignai) with the safety tuning removed. Its weights differ from the official model; it is a separate model.

Source: deepseek-ai/DeepSeek-V4.1-Flash model card, as of 2026-09-25. Benchmark figures are as published by DeepSeek.

Our Coverage and Data

Everything Local Model Watch has published about the DeepSeek-V4.1 family: 2 article(s) covering the base model and its fine-tunes, plus converted builds we tracked after publication. Memory requirements below are computed by this site from file sizes, not quoted from model cards. Part of our model family index.

At a Glance

Hardware Requirements

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

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.

The publisher ships safetensors only, and llama.cpp’s registry does not list this architecture. llama.cpp would need to add support before these tools can run it. 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.

Quantized and Converted Variants

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.

Articles (the family’s own models first, then newest)

Repositories

• deepseek-ai/DeepSeek-V4.1-Flash
• dealignai/DeepSeek-V4.1-Flash-UNCENSORED-FP8

Last updated 2026-09-27 (JST). The explanation at the top of this page was written with the help of AI from the primary sources it cites. The tables and lists under “Our Coverage and Data" are assembled by code from our article log.