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DeepSeek / deepseek_v41_text

DeepSeek-V4.1-Flash

552B backbone parameters · 196B Engram, routing 6 of 384 experts per token.

DeepSeek's official card declares a 552B backbone and a separate 196B Engram component; it also reports 8B active parameters during prefill and 16B during decode. The 763.2B safetensors metadata total is not shown as a single logical model total because this checkpoint uses packed/fused FP4/FP8 tensors and auxiliary components. Open the official parameter note ↗

Open in the calculator →

Architecturepublished data
Quick answer
DeepSeek-V4.1-Flash needs about 449.4 GB at Q4_K_M — the format most people download — with 8,192 tokens of context. None of the 18 common devices listed below holds it entirely at this setting. No hardware for it? Rent a GPU that runs it, priced live.

Sized at 8,192 tokens of context with the whole model in device memory, across 18 common devices. Speeds are estimates, not benchmarks.

Weight-file size by format

FormatSizeRangeBasis
FP161510.6 GB1491.1 GB – 1530.2 GBreconstructed size
Q8_0802.7 GB792.3 GB – 813.1 GBreconstructed size
Q6_K619.8 GB611.8 GB – 627.8 GBreconstructed size
Q5_K_M531.6 GB524.7 GB – 538.5 GBreconstructed size
Q5_0519.6 GB512.8 GB – 526.3 GBreconstructed size
Q4_K_M448.6 GB442.8 GB – 454.4 GBreconstructed size
Q4_0425.3 GB419.8 GB – 430.8 GBreconstructed size
Q3_K_M360.5 GB332.4 GB – 388.6 GBreconstructed size
Q2_K266.8 GB254.0 GB – 279.6 GBreconstructed size

A file size is not the memory a run needs: the KV cache and the runtime reserve come on top, and the calculator adds both.

What the labels mean
published data
Read from a published source — a file's byte count, a model's configuration or a manufacturer's specification — or exact arithmetic on such values. Not a measurement on a machine.
reconstructed size
Weight size reconstructed from the pinned architecture, because no published file exists.
size range
Only a lower and an upper bound are claimed for this weight size.

Hardware ladder

Q4_K_M at 8,192 tokens · 0 of 18 devices hold it entirely
DeviceMemoryVerdictDecodeCalculator
NVIDIA GeForce RTX 5070449.4 GB of 12 GB40 layers on system RAM~4.4 tok/s with offloadOpen →
AMD Radeon™ RX 9070 XT449.4 GB of 16 GB39 layers on system RAM~4.5 tok/s with offloadOpen →
NVIDIA GeForce RTX 4080449.4 GB of 16 GB39 layers on system RAM~4.5 tok/s with offloadOpen →
NVIDIA GeForce RTX 5080449.4 GB of 16 GB39 layers on system RAM~4.5 tok/s with offloadOpen →
NVIDIA GeForce RTX 5070 Ti449.4 GB of 16 GB39 layers on system RAM~4.5 tok/s with offloadOpen →
NVIDIA GeForce RTX 5060 Ti 16GB449.4 GB of 16 GB39 layers on system RAM~4.5 tok/s with offloadOpen →
NVIDIA GeForce RTX 4090449.4 GB of 24 GB38 layers on system RAM~4.6 tok/s with offloadOpen →
AMD Radeon™ RX 7900 XTX449.4 GB of 24 GB38 layers on system RAM~4.6 tok/s with offloadOpen →
NVIDIA GeForce RTX 3090449.4 GB of 24 GB38 layers on system RAM~4.6 tok/s with offloadOpen →
NVIDIA GeForce RTX 5090449.4 GB of 32 GB38 layers on system RAM~4.6 tok/s with offloadOpen →
NVIDIA RTX 6000 Ada Generation449.4 GB of 48 GB36 layers on system RAM~4.8 tok/s with offloadOpen →
Apple M4 Pro449.4 GB of 64 GBdoes not fitnot calibratedOpen →
Apple M5 Pro449.4 GB of 64 GBdoes not fitnot calibratedOpen →
AMD Ryzen AI Max+ 395 with Radeon 8060S449.4 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M3 Max449.4 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M4 Max449.4 GB of 128 GBdoes not fitnot calibratedOpen →
NVIDIA DGX Spark449.4 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M2 Ultra449.4 GB of 192 GBdoes not fitnot calibratedOpen →

Decode is a calibrated estimate from the published calibration; capacity is the manufacturer's published ceiling, not guaranteed free memory. Fitting in memory is not the same as loading: whether the runtime and version you have supports this architecture and format on that machine has not been tested here. Offloaded rows assume there is enough system RAM for the overflow — the calculator checks that against the RAM you declare. Each “Open” link keeps this model, format and context.

Run it in the cloud

Rent a GPU that runs DeepSeek-V4.1-Flash

Every rentable machine that holds the whole model, from Vast.ai and RunPod, with this site's speed estimate for each and the provider's own price, read live. Cheapest first; sort by cost per token or speed instead, or switch the format.

Prices · 18:30 UTC, 22 Sept
6 configurations in stock · 5 out of stock
MachineSpeedPer hourPer M tokensWhereRent
2× B300540 GBcheapest~763 tok/sFaster than you read$15.78$5.74RunPodSecure CloudRent on RunPod
4× H200 NVL564 GB~950 tok/sFaster than you read$16.00$4.68Vast.aiMarketplace · 99.1% reliableRent on Vast.ai
8× RTX PRO 6000768 GB~707 tok/sFaster than you read$16.54$6.49Vast.aiMarketplace · 98.4% reliableRent on Vast.ai
4× H200564 GB~950 tok/sFaster than you read$18.36$5.37RunPodSecure CloudVast.ai $18.42/h Rent on RunPod
8× H100 PCIe640 GB~789 tok/sFaster than you read$19.16$6.74Vast.aiMarketplace · 98.3% reliableRent on Vast.ai
4× B200720 GBrecommendedbest valuefastest~1524 tok/sFaster than you read$25.00$4.56Vast.aiMarketplace · 99.6% reliableRent on Vast.ai
No GPU at all

Use DeepSeek-V4.1-Flash by the token

For one person chatting, that is about 14× cheaper than the best-value rented card above ($4.56 per million tokens). A rented GPU pays off when you keep it busy — many requests batched together, long agent runs — or when the data must stay on a machine you control, or the exact file you want is not served anywhere.

First time renting a GPU? How it works, in four steps
  1. Create an account and add credit. Both providers are prepaid: $10 is enough to try any card on this page for hours. A new RunPod account that arrives through a referral link gets a one-time $5 credit when it first adds $10.
  2. Pick the card and the count shown here, and a template: llama.cpp or vLLM for an OpenAI-compatible API, or one with a web chat if you only want to talk to the model. “How to launch it” below gives the exact image and command.
  3. Wait for the download. The weights are fetched on the machine; tens of gigabytes take a few minutes on a data-centre connection. You pay by the second from the moment it starts.
  4. Stop it when you are done. A running machine bills even when idle. A stopped one costs nothing per hour, though its disk is usually still billed until you delete it.
How to launch it

No Q4_K_M file of DeepSeek-V4.1-Flash is catalogued here, so there is no exact command to vouch for. Start the machine from the llama.cpp server image ghcr.io/ggml-org/llama.cpp:server-cuda and point -hf at a Q4_K_M build from the publisher or a quantizer you trust — search Hugging Face for one. Check its file size against the memory figure on this page before you rent.

Every machine holds the whole model at Q4_K_M and 8,192 tokens of context, no offload. Speeds are this site’s single-stream decode estimates; prices are what each provider’s own API quoted, on-demand, for the whole machine. Vast hosts below 98% measured reliability are left out.

Referral links Vast.ai, RunPod and Novita pay us a share of what you spend if you sign up through these buttons. It costs you nothing, and it never decides an order or a recommendation: both are computed from the live price and the speed, and options that pay us nothing are listed and recommended on the same terms. How we rank

Buying a card instead, or paying by the token? Compare a year of DeepSeek-V4.1-Flash three ways →

Under the hood

Architecture, read from the publisher’s file

The numbers every figure above is computed from, with the file they came from.

Architecture

✓ Architecture read from the published config.json

Retrieved 2026-09-11 at pinned commit dba1be0a40aa; the native packed/fused checkpoint metadata is not used as a logical parameter total.

Architecture
deepseek_v41_text
Layers
40
Hidden size
5,120
Attention heads
64
KV heads
1
Head dimension
512
Vocabulary
129,280
Context ceiling
1,048,576
Sliding window
128
RoPE theta
10,000
Experts
384
Experts / token
6
Expert width
2,304
Shared experts
1

Where the memory goes

tokenembedding× 40 decoder blocksGrouped-query attention64 query · 1 KV headswindow 128Routed experts6 of 384 per tokenall residentoutputprojectiongrows with contextcapacity ≠ traffic

Grouped-query attention shares each key/value head across 64 query heads, so the KV cache is 2% of what multi-head attention would need at the same context.

This is a multimodal checkpoint (vision); the diagram and memory sizing above cover the text decoder. Encoder/audio/image activations are not included in the KV or weight figures.

Open the exact official config.json ↗

More from DeepSeek

deepseek-coder-6.7b-instructdeepseek-coder-7b-instruct-v1.5DeepSeek-Coder-V2-Lite-InstructDeepSeek-R1DeepSeek-R1-0528DeepSeek-R1-0528-Qwen3-8BDeepSeek-R1-Distill-Llama-70BDeepSeek-R1-Distill-Llama-8B

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  -H "Authorization: Bearer $LLMB_KEY" -H "content-type: application/json" \
  -d '{"model":"deepseek-ai-deepseek-v4-1-flash","quantization":"Q4_K_M","context":8192,"hardware":"nvidia-geforce-rtx-4090"}'

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Citing this page

LLM Bottleneck. “DeepSeek-V4.1-Flash VRAM and hardware requirements.” Architecture from deepseek-ai/DeepSeek-V4.1-Flash at revision dba1be0a40aa, retrieved 2026-09-11. https://llmbottleneck.com/models/deepseek-ai-deepseek-v4-1-flash

Every figure above is either the published value or a reconstruction whose measured error is on the accuracy page.

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catalogue 2026-10-03models 327devices 135