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DeepSeek V4.1 Flash 552B

DeepSeek V4.1 Flash 552B needs roughly 571.6 GB VRAM at Q4_K_M quantization (1236.5 GB at FP16). 0 GPUs we track can run it fully in VRAM at 8k context.

0 GPUs run this natively · 0 with CPU offload

DeepSeek552B params16B active (MoE)1024k contextMITCommercial use ok
Q4_K_M total
571.6 GB
at 8k context
KV cache, full context
2.3 GB
40 layers cache, ~37× fewer entries than tokens
Inputs
Text · Image
MIT, released September 2026
MoE routing
6 of 384 experts
16B of 552B active on decode
Prefill vs decode
8B / 16B active
asymmetric Causal Encoder-Decoder design
Modalities
Text + vision
first native image input in this line

DeepSeek V4.1 Flash 552B is a Mixture of Experts (MoE) model with 552B total parameters but only 16B active per token developed by DeepSeek. Released September 10, 2026 after a two-day API-only beta, DeepSeek describes this as the smallest model in a brand new architecture family, not a re-post-train of V4 Flash 0731 the way 0731 itself was a re-post-train of the April preview. The headline change is structural: DeepSeek calls it a Causal Encoder-Decoder design (its own term, unrelated to the bidirectional encoder-decoder pattern T5 popularized), splitting the 40 layers into 20 encoder and 20 decoder layers with an unusual asymmetry of 8B active parameters during prefill versus 16B during decode. It is a 552B-total, 384-expert mixture of experts routing 6 experts per token, with a 1M-token context window carried over from the V4 line. For the first time in this lineage the same checkpoint accepts image input natively, where V4 Pro and V4 Flash 0731 are both text only. DeepSeek's card also credits a new Engram conditional-memory mechanism, an SWA Bounded Replay technique, and a second-generation Compressed Sparse Attention (CSA2); independent verification of exactly how those mechanisms work was not available at launch, so treat the mechanism names as DeepSeek's own description rather than settled, third-party-confirmed fact.

To run DeepSeek V4.1 Flash 552B locally: The one clearly verified hardware story is the KV cache: DeepSeek's own figures put it at roughly a quarter of V4 Flash 0731's already-compressed cache, which this site's own math confirms works out to about 2.3GB at a full 1M-token context against V4 Flash 0731's roughly 9GB. The catch is the weights. V4 Flash 0731 has a real local story because its routed experts ship natively in a low-bit format with vetted community GGUF builds from about 82.5GB up; this model has no build this site has verified below its native release, about 510GB across 48 safetensors shards (which includes the 196B-parameter Engram lookup memory its card describes as sparsely accessed). That is the only figure this page's quantization table counts, so every ladder rung floors at that same size. Smaller builds do exist: NVIDIA's NVFP4 repository is 429.0GB, though stock vLLM cannot load that revision yet and the older revision it can load is 527.3GB, and community conversions run from about 169GB to 366GB, one of them streaming the Engram tables from SSD, but this site has not verified any of them. Add KV cache and overhead at a full 1M-token context and the native release totals about 574GB, past even a 512GB M3 Ultra Mac Studio, the largest single unified-memory machine this site tracks. Until one of those builds is verified, the API is the simplest way to run this model. As a MoE model, inference speed depends on active parameters (16B) rather than total size.

DeepSeek's own API changelog reports GPQA Diamond 90.9, Terminal-Bench 2.1 90.6, a Codeforces rating of 3471, and DeepSWE v1.1 74.2, all comfortably ahead of V4 Flash 0731's GPQA 91 and Terminal-Bench 82.7 on the coding and agentic side, while landing close on GPQA itself. DeepSeek states that it comprehensively surpassed V4 Pro on performance, cost, speed and total time, but has not published the side-by-side benchmark table that would let anyone verify the size of that gap, so read the claim as directional rather than quantified. No MMLU-Pro, SWE-bench, or LiveCodeBench figures were published at launch, and no independent lab had re-run any of these numbers on launch day.

DeepSeek V4.1 Flash VRAM at 1M context: about 2.3 GB of KV cache, not 86 GB

DeepSeek's card claims a KV cache roughly a quarter the size of DeepSeek V4 Flash 0731's, itself already a fraction of what a conventional attention stack would need at a million tokens. A second figure on the same card, an exact bytes-per-token count, implies a steeper reduction than that; the two don't agree, so this site uses the more conservative of the two claims rather than the more flattering one.

0255075100128k512k768k1024k2.3 GBV4.1 Flash85.9 GB1 entry/token
DeepSeek V4.1 Flash 552B: 40 of 40 layers cacheThe same 40-layer stack storing one entry per token

KV cache only, at FP16. Weights and activation overhead sit on top of these figures.

Filling the entire 1,048,576-token window costs about 2.3 GB of KV cache. The same 40-layer stack storing one entry per token would want about 85.9 GB instead, more than the weights of most models this site tracks. That 2.3 GB figure is also about a quarter of DeepSeek V4 Flash 0731's own already-small 9.2 GB at the same context length, matching DeepSeek's own comparative claim almost exactly. Context, in other words, is not the problem with this model. The weights are (see the verdict below).

VRAM at each quantization

Calculated at 8k context. Since KV cache scales linearly with context, longer sessions need more VRAM than shown here.

QuantWeightsKV cacheTotal
FP322208.0 GB0.02 GB2473.0 GB
BF161104.0 GB0.02 GB1236.5 GB
FP161104.0 GB0.02 GB1236.5 GB
Q8_0586.8 GB0.02 GB657.2 GB
Q6_K510.3 GB0.02 GB571.6 GB
Q5_K_M510.3 GB0.02 GB571.6 GB
Q4_K_Mrec510.3 GB0.02 GB571.6 GB
Q3_K_M510.3 GB0.02 GB571.6 GB
Q2_K510.3 GB0.02 GB571.6 GB
NVFP4cuda510.3 GB0.02 GB571.6 GB

Shown at 8k context with FP16 KV cache. NVFP4 needs a CUDA GPU to run. Toggle TurboQuant in the calculator to view compressed KV cache numbers.

Benchmarks

GPQA Diamond
90.9
Terminal-Bench 2.1
90.6

GPUs that run DeepSeek V4.1 Flash 552B natively (0)

No single GPU in our list fits this model at Q4 with 8k context. Browse all GPUs or try multi-GPU setups.

Cloud GPU Rental

Cheapest way to run DeepSeek V4.1 Flash 552B: 8× A100 80GB at Q6_K, ~54 tok/s, from $9.52/hr.

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Rent this setup on RunPod ↗ (+$5 signup credit)

Prices as of Oct 17, 2026. Rates change; check RunPod for current pricing.

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Notes

Released September 10, 2026 as, in DeepSeek's own words, "the smallest model in our new architecture family," after a two-day API-only beta. The headline change from DeepSeek V4 Flash 0731 is architectural, not just a re-post-train: DeepSeek calls the new design a Causal Encoder-Decoder (its own term, not the bidirectional encoder-decoder pattern T5 popularized), splitting the 40 layers into 20 encoder and 20 decoder layers with an asymmetric 8B active parameters during prefill versus 16B during decode. DeepSeek attributes a new "Engram" conditional-memory mechanism and "SWA Bounded Replay" to the design, on top of a second-generation Compressed Sparse Attention (CSA2) and a native FP4 KV cache format; independent technical verification of these specific mechanisms was not yet available at time of writing; DeepSeek's own card is the only source. What is independently corroborated (community discussion on the day of release, in addition to the HF card) is the headline outcome: roughly a 4x smaller KV cache than V4 Flash 0731's already-compressed one, which itself was already far smaller than a conventional dense-attention model's. On DeepSeek's own API changelog it scores GPQA Diamond 90.9, Terminal-Bench 2.1 90.6, a Codeforces rating of 3471, and DeepSWE v1.1 74.2, and DeepSeek states without publishing a side-by-side table that it "comprehensively surpassed" V4 Pro on performance, cost, speed, and total time. Native multimodal image input ships in the same checkpoint for the first time in this line (V4 Pro and V4 Flash are both text-only). The catch for local use is the weights. DeepSeek's own release is about 510GB, and that file includes the 196B-parameter Engram conditional memory its card says is accessed sparsely, which this site counts in full at every quantization level. Smaller builds exist as of 15 October 2026 but none is one this site has verified: NVIDIA's NVFP4 repository is 429.0GB in its current revision, though stock vLLM cannot load that revision yet (the older one it can load is 527.3GB), and community conversions run from about 169GB to 366GB (GGUF, MLX 2-bit and EXL3 builds; one community engine, DwarfStar, streams the Engram tables from SSD instead of holding them in memory). Even so, the API is still the simplest way to use this model today. Superseded by the DeepSeek V4.1 Flash 1017 build, which keeps this architecture and the same 510.3GB of weights unchanged and re-post-trains it for agentic work.

Hugging Face ↗Released 2026-09-10

Compare DeepSeek V4.1 Flash 552B with other models

Should you run DeepSeek V4.1 Flash locally, today?

Not yet, and not because of context length, which this model handles better than almost anything else this site tracks. The blocker is the weights: this page's quantization table counts DeepSeek's native release, so every rung from Q4_K_M down to Q2_K shows the same roughly 510 GB, the same way this site treats gpt-oss-120b's natively low-bit experts. Smaller builds do exist as of 15 October 2026 (NVIDIA's NVFP4 at 429.0 GB for Blackwell GPUs, and community conversions from about 169 GB to 366 GB), but this site has not verified any of them, and stock vLLM cannot yet load NVIDIA's current revision. Add KV cache and overhead at a full 1M-token context and the native release totals about 574 GB, past even a 512 GB Mac Studio M3 Ultra, the largest single unified-memory machine this site tracks; at a short 8k context it is barely smaller, about 572 GB, since the weights are the entire story here, not the context window. Until one of those builds is verified, the API is the simplest way to use this model, whatever hardware you own.

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Frequently asked questions

What are the VRAM requirements for DeepSeek V4.1 Flash 552B?
DeepSeek V4.1 Flash 552B requires approximately 571.6 GB of VRAM at Q4_K_M quantization, 657.2 GB at Q8, and 1236.5 GB at FP16. These numbers assume 8k context window; VRAM scales linearly with context length due to the KV cache.
How many parameters does DeepSeek V4.1 Flash 552B have?
DeepSeek V4.1 Flash 552B has 552 billion total parameters, but only 16 billion are active per token thanks to its Mixture of Experts (MoE) architecture. This makes inference significantly faster than the total parameter count suggests.
Can DeepSeek V4.1 Flash 552B run on a 16 GB GPU?
No. At Q4_K_M, DeepSeek V4.1 Flash 552B needs 571.6 GB of VRAM, more than 16 GB. You will need a multi-GPU server.
Can DeepSeek V4.1 Flash 552B run on a 24 GB GPU?
No. Even at Q4_K_M, DeepSeek V4.1 Flash 552B needs 571.6 GB. Consider a multi-GPU server with 572 GB+ of combined VRAM.
What is the smallest quantization for DeepSeek V4.1 Flash 552B that fits in 24 GB of VRAM?
DeepSeek V4.1 Flash 552B cannot fit in 24 GB of VRAM at any standard quantization level. The minimum needed is 571.6 GB at Q2_K.
What GPU do I need to run DeepSeek V4.1 Flash 552B locally?
You need a multi-GPU server. At Q4_K_M, DeepSeek V4.1 Flash 552B needs 571.6 GB VRAM, more than any single consumer GPU. That's roughly 8x 80 GB datacenter GPUs (H100, A100, or similar) pooled together.
Can I run DeepSeek V4.1 Flash 552B in the cloud?
Yes. The cheapest RunPod setup we found that holds DeepSeek V4.1 Flash 552B entirely in GPU memory at 8k context is 8× A100 80GB at Q6_K, at roughly 53.9 tokens per second and from $9.52 per hour for the whole pod. Prices as of Oct 17, 2026; RunPod rates change, so check their pricing page for the current figure.
How much VRAM does DeepSeek V4.1 Flash need?
More than any single machine this site tracks holds: the native ~510 GB release totals roughly 574 GB once KV cache and overhead are added at a full 1,048,576-token context, past even a 512 GB Mac Studio M3 Ultra. Every quantization level this page lists shows the same ~510 GB figure, because that native release is the only build this site has verified; NVIDIA's NVFP4 and several community conversions are smaller (see the Mac Studio question below), but none is one this site has checked.
Can DeepSeek V4.1 Flash run on a Mac Studio?
Not on this site's numbers. The native ~510 GB release doesn't fit even a 512 GB Mac Studio M3 Ultra once KV cache and overhead are counted (about 574 GB total at full context). Smaller community builds do exist as of 15 October 2026, including an MLX 2-bit of about 239 GB and GGUFs from about 169 GB to 366 GB (one engine streams the model's Engram lookup tables from SSD), and NVIDIA's own NVFP4 build is 429.0 GB but targets Blackwell GPUs, not Macs. This site has not verified the quality or speed of the community builds, so treat them as experiments rather than a supported route.
What changed between DeepSeek V4 Flash 0731 and DeepSeek V4.1 Flash?
The architecture itself, not just training. DeepSeek calls the new design a Causal Encoder-Decoder, splitting the 40 layers into 20 encoder and 20 decoder layers with an asymmetric 8B active parameters on prefill versus 16B on decode, replacing V4 Flash 0731's uniform 43-layer hybrid CSA/HCA stack. The KV cache shrank roughly 4x again on top of V4 Flash 0731's own compression, and the model gained native image input, which V4 Flash 0731 doesn't have. On DeepSeek's own reported benchmarks it also improved on Terminal-Bench 2.1 (82.7 to 90.6) and GPQA Diamond (91 to 90.9, essentially flat).
Does DeepSeek V4.1 Flash support image input?
Yes. DeepSeek's own release notes describe native multimodal visual understanding, and the model's published configuration includes a dedicated vision encoder in the same checkpoint. This is the first model in the DeepSeek V4 line to ship with image input; both V4 Pro and V4 Flash 0731 are text only.