DeepSeek V4.1 Flash — VRAM, speed & local setup
Written by Jakub Rusinowski · Last updated
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. Revision 2026-10-17: DeepSeek published DeepSeek-V4.1-Flash-1017, a minor update of these weights (same 763B-parameter checkpoint, 510 GB, MIT, 1M context) that improves reasoning, coding, agentic and visual benchmarks and shortens outputs; every memory figure on this page applies to both.
DeepSeek V4.1 Flash needs about 462 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.
VRAM and speed by quantization
Quoted against NVIDIA RTX 4090 (24 GB). Weights plus overhead only: this model's architecture is unpublished, so no KV cache is added.
| Quant | Memory | VRAM | Speed (est.) | Fit |
|---|---|---|---|---|
| Q2_K 2.63 bpw | 251.7 GB | — | Too big | |
| Q3_K_M 3.41 bpw | 326.1 GB | — | Too big | |
| Q4_K_M 4.83 bpw | 461.6 GB | — | Too big | |
| Q5_K_M 5.67 bpw | 541.7 GB | — | Too big | |
| Q6_K 6.56 bpw | 626.6 GB | — | Too big | |
| Q8_0 8.50 bpw | 811.7 GB | — | Too big | |
| F16 16.00 bpw | 1527.2 GB | — | Too big |
Black marker = usable memory on the NVIDIA RTX 4090 (24 GB). Estimates from the memory-bandwidth roofline on the methodology page. DeepSeek V4.1 Flash VRAM calculator →
Get DeepSeek V4.1 Flash running
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:
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How to run 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.
Specifications
Verified — Checked against the primary source — the model card or the vendor spec page — and corroborated by a second independent source.
- Parameters
- 552 Billion backbone (16B active) + 196B Engram memory
- Active parameters
- 16B per output token (decode) · 8B per input token (prefill)
- Context window
- 1,000,000
- Architecture
- Mixture-of-Experts, causal encoder-decoder (20 + 20 layers), 384 routed experts + 1 shared, 6 active; vision encoder
- Provider
- DeepSeek
- Licence
- MIT
- Availability
- Weights downloadable
- Practical deployment
- Multi-GPU server
- Specified at
- Q4_K_M
- System RAM
- 512 GB
- Record updated
- 2026-10-25
Commercial use permitted. No usage restrictions beyond attribution.
Limits and caveats
- 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.
Quality and use cases
Scores as published by the model’s authors or an independent evaluator — quality, not throughput, and not measured by us.
Independent evaluation — not vendor-claimed and not measured by us
| Benchmark | Score | Provenance |
|---|---|---|
| AA Intelligence Index v4.3.2 (Max) | 39 | independent · Artificial Analysis · snapshot 2026-10-13 |
Can I run DeepSeek V4.1 Flash on my GPU?
- DeepSeek V4.1 Flash on AMD Ryzen AI Max+ 395
- DeepSeek V4.1 Flash on Apple M1 Ultra
- DeepSeek V4.1 Flash on Apple M2 Max
- DeepSeek V4.1 Flash on Apple M2 Ultra
- DeepSeek V4.1 Flash on Apple M4 Max
- DeepSeek V4.1 Flash on Apple M5 Max
- DeepSeek V4.1 Flash on NVIDIA A100 80GB (PCIe)
- DeepSeek V4.1 Flash on NVIDIA DGX Spark
- DeepSeek V4.1 Flash on NVIDIA H100 80GB (PCIe)
- DeepSeek V4.1 Flash on NVIDIA RTX PRO 6000 Blackwell
DeepSeek V4.1 Flash — frequently asked questions
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.