DeepSeek552B (16B active)Q4_K_M 下约 462 GB 显存

DeepSeek V4.1 Flash — 显存、速度与本地部署

作者： Jakub Rusinowski · 最后更新：

A 552B-backbone Mixture-of-Experts with a causal encoder-decoder: 8B parameters active per input token, 16B per output token, plus a 196B-parameter Engram memory table and native image input. 1M-token context, MIT licence. The released checkpoint is about 510 GB and DeepSeek's reference code serves it across eight GPUs; mainline local runtimes do not load it yet.

DeepSeek V4.1 Flash 在 Q4_K_M 下约需 462 GB 显存——量化权重加框架开销，不含 KV 缓存。在 Apple Silicon 上，这部分来自统一内存。

逻辑95
创意90
编程96

按量化级别的显存与速度

计算基准：NVIDIA RTX 4090 (24 GB)。仅含权重与开销：该模型架构未公开，因此未计入 KV 缓存。

量化显存速度（估算）适配
Q2_K
2.63 bpw
251.7 GB—放不下
Q3_K_M
3.41 bpw
326.1 GB—放不下
Q4_K_M
4.83 bpw
461.6 GB—放不下
Q5_K_M
5.67 bpw
541.7 GB—放不下
Q6_K
6.56 bpw
626.6 GB—放不下
Q8_0
8.50 bpw
811.7 GB—放不下
F16
16.00 bpw
1527.2 GB—放不下

黑色标记 = NVIDIA RTX 4090 (24 GB) 上的可用显存。 估算来自内存带宽屋顶线模型，详见 方法说明页. DeepSeek V4.1 Flash 显存计算器 →

运行 DeepSeek V4.1 Flash

DeepSeek V4.1 Flash is not practical on a single consumer GPU: it needs about 461.6 GB at Q4_K_M, more than any single device in our catalogue holds with headroom. How multi-GPU inference works, or rent a multi-GPU server by the hour:

立即云端部署 RunPod

或在 Vast.ai 比较

作为亚马逊联盟成员，我们从符合条件的购买中获得收入。云 GPU 链接为推荐链接——我们可能获得佣金，您无需额外付费。

如何运行 DeepSeek V4.1 Flash

No local Ollama tag. Ollama lists deepseek-v4.1-flash only as a cloud-hosted ':cloud' tag, which runs on Ollama's servers, not your machine.

Hugging Face 上的权重： deepseek-ai/DeepSeek-V4.1-Flash ↗

规格

Verified — Checked against the primary source — the model card or the vendor spec page — and corroborated by a second independent source.

参数量
552 Billion backbone (16B active) + 196B Engram memory
Active parameters
16B per output token (decode) · 8B per input token (prefill)
上下文窗口
1,000,000
架构
Mixture-of-Experts, causal encoder-decoder (20 + 20 layers), 384 routed experts + 1 shared, 6 active; vision encoder
提供商
DeepSeek
许可证
MIT
Availability
Weights downloadable
Practical deployment
Multi-GPU server
规格量化
Q4_K_M
系统内存
512 GB
记录更新于
2026-10-13
许可证MIT允许商业使用

Commercial use permitted. No usage restrictions beyond attribution.

限制与注意事项

  • The memory figure includes the 196B-parameter Engram table, which DeepSeek's released inference code keeps in GPU memory. A runtime that served Engram from system RAM would need much less GPU memory; none does yet.
  • The KV-cache figure is an estimate: the model's compressed sparse attention (890 bytes per token, per the model card) is not modelled exactly here, so the cache is over-stated, which is negligible next to the weights.

质量与使用场景

评分由模型作者或独立评测方发布——衡量质量而非吞吐量，并非我们实测。

最适合reasoningcodingagentslong contextvision

Independent evaluation — not vendor-claimed and not measured by us

基准分数来源
AA Intelligence Index v4.3.2 (Max)39independent · Artificial Analysis · snapshot 2026-10-13

我的 GPU 能运行 DeepSeek V4.1 Flash 吗？

DeepSeek V4.1 Flash — 常见问题

How much VRAM does DeepSeek V4.1 Flash need?

About 462 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.

Does DeepSeek V4.1 Flash run on an RTX 4090 (24 GB)?

No. DeepSeek V4.1 Flash needs about 462 GB at Q4_K_M, more than a single RTX 4090's 24 GB. It needs a larger card, several GPUs, or Apple Silicon with enough unified memory — or it runs with part of the weights offloaded to system RAM, which is much slower.

How do I run DeepSeek V4.1 Flash locally?

No local Ollama tag. Ollama lists deepseek-v4.1-flash only as a cloud-hosted ':cloud' tag, which runs on Ollama's servers, not your machine. Running the published tag would send your prompts to a hosted GPU rather than your own machine.

What other sizes does DeepSeek V4.1 come in?

DeepSeek V4.1 Flash (462 GB), DeepSeek V4.1 (967 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.