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DeepSeek / deepseek_v4

DeepSeek-V4-Flash

284B total · 13B active parameters, routing 6 of 256 experts per token.

The official DeepSeek V4 card declares 284B total and 13B activated parameters. The native FP4/FP8 checkpoint's safetensors element total is kept as source metadata but is not used as an unqualified logical count. Open the official parameter note ↗

Open in the calculator →

Architecturepublished data
Quick answer
DeepSeek-V4-Flash needs about 175.9 GB at Q4_K_M — the format most people download — with 8,192 tokens of context. It fits entirely on 1 of 18 common devices, starting with the Apple M2 Ultra (192 GB). What else fits in 192 GB. 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.

Find the Apple M2 Ultra, the smallest card listed here that holds it: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

Weight-file size by format

FormatSizeRangeBasis
FP16579.8 GB566.0 GB – 593.6 GBreconstructed size
Q8_0308.1 GB300.8 GB – 315.4 GBreconstructed size
Q6_K237.9 GB232.3 GB – 243.6 GBreconstructed size
Q5_K_M205.5 GB200.6 GB – 210.4 GBreconstructed size
Q5_0199.5 GB194.8 GB – 204.2 GBreconstructed size
Q4_K_M175.0 GB170.8 GB – 179.1 GBreconstructed size
Q4_0163.3 GB159.5 GB – 167.2 GBreconstructed size
Q3_K_M142.8 GB130.2 GB – 155.5 GBreconstructed size
Q2_K104.9 GB98.8 GB – 111.1 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 · 1 of 18 devices hold it entirely
DeviceMemoryVerdictDecodeCalculator
NVIDIA GeForce RTX 5070175.9 GB of 12 GB41 layers on system RAM~5.6 tok/s with offloadOpen →
AMD Radeon™ RX 9070 XT175.9 GB of 16 GB40 layers on system RAM~5.7 tok/s with offloadOpen →
NVIDIA GeForce RTX 4080175.9 GB of 16 GB40 layers on system RAM~5.7 tok/s with offloadOpen →
NVIDIA GeForce RTX 5080175.9 GB of 16 GB40 layers on system RAM~5.7 tok/s with offloadOpen →
NVIDIA GeForce RTX 5070 Ti175.9 GB of 16 GB40 layers on system RAM~5.7 tok/s with offloadOpen →
NVIDIA GeForce RTX 5060 Ti 16GB175.9 GB of 16 GB40 layers on system RAM~5.7 tok/s with offloadOpen →
NVIDIA GeForce RTX 4090175.9 GB of 24 GB38 layers on system RAM~6.0 tok/s with offloadOpen →
AMD Radeon™ RX 7900 XTX175.9 GB of 24 GB38 layers on system RAM~6.0 tok/s with offloadOpen →
NVIDIA GeForce RTX 3090175.9 GB of 24 GB38 layers on system RAM~6.0 tok/s with offloadOpen →
NVIDIA GeForce RTX 5090175.9 GB of 32 GB36 layers on system RAM~6.4 tok/s with offloadOpen →
NVIDIA RTX 6000 Ada Generation175.9 GB of 48 GB32 layers on system RAM~7.0 tok/s with offloadOpen →
Apple M4 Pro175.9 GB of 64 GBdoes not fitnot calibratedOpen →
Apple M5 Pro175.9 GB of 64 GBdoes not fitnot calibratedOpen →
AMD Ryzen AI Max+ 395 with Radeon 8060S175.9 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M3 Max175.9 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M4 Max175.9 GB of 128 GBdoes not fitnot calibratedOpen →
NVIDIA DGX Spark175.9 GB of 128 GBdoes not fitnot calibratedOpen →
Apple M2 Ultra175.9 GB of 192 GBfits~48 tok/sOpen →

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-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
10 configurations in stock · 10 out of stock
MachineSpeedPer hourPer M tokensWhereRent
4× L40S192 GBcheapest~203 tok/sFaster than you read$1.87$2.55Vast.aiMarketplace · 99.2% reliableRent on Vast.ai
2× RTX PRO 6000192 GB~214 tok/sFaster than you read$2.27$2.94Vast.aiMarketplace · 98.8% reliableRent on Vast.ai
4× RTX 6000 Ada192 GB~225 tok/sFaster than you read$3.00$3.69Vast.aiMarketplace · 98.5% reliableRent on Vast.ai
4× RTX PRO 5000192 GB~316 tok/sFaster than you read$3.28$2.88RunPodCommunity CloudRent on RunPod
8× RTX 4090192 GBrecommendedbest value~458 tok/sFaster than you read$4.06$2.46Vast.aiMarketplace · 99.2% reliableRent on Vast.ai
2× H200 NVL282 GB~574 tok/sFaster than you read$7.20$3.48Vast.aiMarketplace · 98.7% reliableRent on Vast.ai
1× B300270 GB~465 tok/sFaster than you read$7.89$4.71RunPodSecure CloudRent on RunPod
4× H100 SXM320 GBfastest~787 tok/sFaster than you read$9.07$3.20Vast.aiMarketplace · 99.9% reliableRunPod $13.96/h Rent on Vast.ai

No GPU at all

Use DeepSeek-V4-Flash by the token

For one person chatting, that is about 25× cheaper than the best-value rented card above ($2.46 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-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-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 2026-09-01 at pinned commit 60d8d70770c6; the native packed/fused checkpoint metadata is not used as a logical parameter total.

Architecture
deepseek_v4
Layers
43
Hidden size
4,096
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
256
Experts / token
6
Expert width
2,048
Shared experts
1

Where the memory goes

tokenembedding× 43 decoder blocksGrouped-query attention64 query · 1 KV headswindow 128Routed experts6 of 256 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.

Open the exact official config.json ↗

DeepSeek-V4-Flash, card by card

One page per device: whether DeepSeek-V4-Flash fits, at which formats, and how fast.

All 30Fewer

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Use this answer in your own product

As JSON, from the API
curl -s https://llmbottleneck.com/v1/analyze \
  -H "Authorization: Bearer $LLMB_KEY" -H "content-type: application/json" \
  -d '{"model":"deepseek-ai-deepseek-v4-flash","quantization":"Q4_K_M","context":8192,"hardware":"nvidia-geforce-rtx-4090"}'

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On your page, as a widget
<script src="https://llmbottleneck.com/widget.js"
  data-model="deepseek-ai-deepseek-v4-flash" data-quantization="Q4_K_M"
  data-hardware="nvidia-geforce-rtx-4090" data-context="8192"></script>

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Citing this page

LLM Bottleneck. “DeepSeek-V4-Flash VRAM and hardware requirements.” Architecture from deepseek-ai/DeepSeek-V4-Flash at revision 60d8d70770c6, retrieved 2026-09-01. https://llmbottleneck.com/models/deepseek-ai-deepseek-v4-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