DeepSeek-V4-Flash-DSpark by DeepSeek: Open-Weight Model

Runs On

What it takes to serve DeepSeek-V4-Flash-DSpark (165.3B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

Precision	Weights	Memory needed	Cheapest setup	Per hour	Also fits
16-bit	330.5 GB	396.6 GB	2x MI325X (256 GB) Vultr	$4.00	2x MI355X $5.18 · 3x MI300X $5.55
8-bit	165.3 GB	198.3 GB	1x MI325X (256 GB) Vultr	$2.00	1x MI355X $2.59 · 2x MI300X $3.70
4-bit	82.6 GB	99.2 GB	1x MI300X (192 GB) Vultr	$1.85	1x MI325X $2.00 · 1x MI355X $2.59

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

SAVRN's Notes on DeepSeek-V4-Flash-DSpark

The publisher says it outright: DSpark is not a new model. It is the DeepSeek-V4-Flash checkpoint with a speculative decoding module attached, so you are evaluating a serving path, not fresh weights. Those weights run 165.3 billion parameters across 256 routed experts with 6 active per token. Precision picks the hardware: 16-bit needs 396.6 GB and two MI325X cards at $4.00 per hour, 8-bit needs 198.3 GB on one MI325X at $2.00, and 4-bit needs 99.2 GB on one MI300X at $1.85.

MIT covers it: commercial use, modification and redistribution with the notices kept. Two checks before buying cards. The memory figures are the floor; a request that uses the 1,048,576-token context adds cache on top, sized from the single key/value head at 512 dimensions. And the speculative decoding path is the point of this release, so confirm your serving stack runs the publisher's inference example.

SAVRN Research, 2026-09-18

Model Card

By DeepSeek, published under mit, revision 62af8fffb2f7.

DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence

Technical Report

Note: DeepSeek-V4-Flash-DSpark is not a new model. It is the same checkpoint with an additional speculative decoding module attached. A minimal inference example is available in the inference folder. For more details, refer to: https://github.com/deepseek-ai/DeepSpec

Introduction

We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models — DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSeek-V4-Flash with 284B parameters (13B activated) — both supporting a context length of one million tokens.

DeepSeek-V4 series incorporate several key upgrades in architecture and optimization:

Read the full model card (2,036 words)

Configuration

Architecture: DeepseekV4ForCausalLM
Context length (tokens): 1,048,576
Layers: 43
Hidden size: 4,096
Attention heads: 64
Key/value heads: 1
Head dimension: 512
Vocabulary size: 129,280
Routed experts: 256
Experts active per token: 6
Sliding window (tokens): 128
RoPE base: 10,000
Stored precision: bfloat16
Model type: deepseek_v4
Quantization: fp8

Identity and Version

Repository: deepseek-ai/DeepSeek-V4-Flash-DSpark
Publisher: DeepSeek
Task: Text generation
Modality: Text
Library: transformers
Parameters: 165.3B parameters
Languages: Not stated by the source
Revision: 62af8fffb2f7030cac4de2f0169f5b8d1101b646
First published: 2026-06-27
Last updated: 2026-07-04

Files and Weights

74 files, 166.9 GB in total. The weights are 48 files totalling 166.9 GB in safetensors.

Weights48 files · 166.9 GB

Configuration14 files · 5.7 MB

Tokenizer2 files · 6.4 MB

Documentation4 files · 24.5 KB

Other5 files · 8.7 KB

Repository1 file · 1.5 KB

Every file

File	Type	Size	SHA-256
model-00001-of-00048.safetensors	Weights	1.1 GB	517658661390
model-00002-of-00048.safetensors	Weights	3.6 GB	0821952bc12c
model-00003-of-00048.safetensors	Weights	3.6 GB	2bd2b020f81f
model-00004-of-00048.safetensors	Weights	3.6 GB	e6286ec182a2
model-00005-of-00048.safetensors	Weights	3.6 GB	021dd40791d0
model-00006-of-00048.safetensors	Weights	3.6 GB	3d96bfc9686d
model-00007-of-00048.safetensors	Weights	3.6 GB	4481b0c86e9f
model-00008-of-00048.safetensors	Weights	3.6 GB	e19559e75d35
model-00009-of-00048.safetensors	Weights	3.6 GB	ab44718d102e
model-00010-of-00048.safetensors	Weights	3.6 GB	743c16a35f1c
model-00011-of-00048.safetensors	Weights	3.6 GB	4fc2c700363b
model-00012-of-00048.safetensors	Weights	3.6 GB	5214de3c6ca8
model-00013-of-00048.safetensors	Weights	3.6 GB	d953f118488b
model-00014-of-00048.safetensors	Weights	3.6 GB	40c97b77151b
model-00015-of-00048.safetensors	Weights	3.6 GB	136cfaa14f36
model-00016-of-00048.safetensors	Weights	3.6 GB	6881f71ac5da
model-00017-of-00048.safetensors	Weights	3.6 GB	a1949b35de69
model-00018-of-00048.safetensors	Weights	3.6 GB	8288d4462015
model-00019-of-00048.safetensors	Weights	3.6 GB	25a4d2a2a4a0
model-00020-of-00048.safetensors	Weights	3.6 GB	0016a46d9f85
model-00021-of-00048.safetensors	Weights	3.6 GB	3f342f4120b9
model-00022-of-00048.safetensors	Weights	3.6 GB	0bf9870e1d44
model-00023-of-00048.safetensors	Weights	3.6 GB	215f51b475b8
model-00024-of-00048.safetensors	Weights	3.6 GB	a11f085685e7
model-00025-of-00048.safetensors	Weights	3.6 GB	1d2a9ec82956
model-00026-of-00048.safetensors	Weights	3.6 GB	aaec7eb32b16
model-00027-of-00048.safetensors	Weights	3.6 GB	63069bb430af
model-00028-of-00048.safetensors	Weights	3.6 GB	2a804253c04f
model-00029-of-00048.safetensors	Weights	3.6 GB	68dfa669a272
model-00030-of-00048.safetensors	Weights	3.6 GB	dd8433e27dff
model-00031-of-00048.safetensors	Weights	3.6 GB	6e7796ad3b05
model-00032-of-00048.safetensors	Weights	3.6 GB	86d3f021bec5
model-00033-of-00048.safetensors	Weights	3.6 GB	35a92fa5a3cc
model-00034-of-00048.safetensors	Weights	3.6 GB	368896471e8e
model-00035-of-00048.safetensors	Weights	3.6 GB	85c9a9af1ec5
model-00036-of-00048.safetensors	Weights	3.6 GB	0e22e7f41331
model-00037-of-00048.safetensors	Weights	3.6 GB	47f41e12ce98
model-00038-of-00048.safetensors	Weights	3.6 GB	cdbb8c36da34
model-00039-of-00048.safetensors	Weights	3.6 GB	03f3cc8ab1a7
model-00040-of-00048.safetensors	Weights	3.6 GB	3124105d3834
model-00041-of-00048.safetensors	Weights	3.6 GB	4eda737587c4
model-00042-of-00048.safetensors	Weights	3.6 GB	9ac23e2e32c4
model-00043-of-00048.safetensors	Weights	3.6 GB	8c629a864a9b
model-00044-of-00048.safetensors	Weights	3.6 GB	745476d81cb1
model-00045-of-00048.safetensors	Weights	1.1 GB	9a0fd242134e
model-00046-of-00048.safetensors	Weights	3.6 GB	14810f274692
model-00047-of-00048.safetensors	Weights	3.6 GB	7a44164698d9
model-00048-of-00048.safetensors	Weights	3.7 GB	a0bbb24f36d2
config.json	Configuration	1.9 KB	—
encoding/encoding_dsv4.py	Configuration	27.9 KB	—
encoding/test_encoding_dsv4.py	Configuration	3.7 KB	—
encoding/tests/test_input_1.json	Configuration	2.7 KB	—
encoding/tests/test_input_2.json	Configuration	526 B	—
encoding/tests/test_input_3.json	Configuration	4.5 KB	—
encoding/tests/test_input_4.json	Configuration	2.7 KB	—
generation_config.json	Configuration	170 B	—
inference/config.json	Configuration	1.2 KB	—
inference/convert.py	Configuration	6.6 KB	—
inference/generate.py	Configuration	5.8 KB	—
inference/kernel.py	Configuration	22.2 KB	—
inference/model.py	Configuration	45.1 KB	—
model.safetensors.index.json	Configuration	5.6 MB	—
LICENSE	Documentation	1.1 KB	—
README.md	Documentation	14.4 KB	—
encoding/README.md	Documentation	8.1 KB	—
inference/README.md	Documentation	951 B	—
encoding/tests/test_output_1.txt	Other	2.4 KB	—
encoding/tests/test_output_2.txt	Other	342 B	—
encoding/tests/test_output_3.txt	Other	3.3 KB	—
encoding/tests/test_output_4.txt	Other	2.6 KB	—
inference/requirements.txt	Other	92 B	—
.gitattributes	Repository	1.5 KB	—
tokenizer.json	Tokenizer	6.4 MB	—
tokenizer_config.json	Tokenizer	801 B	—

License and Download

License: mit
Access: Open weights, no gate
Download size: 166.9 GB

Download from DeepSeek

Released by DeepSeek through its official repository on Hugging Face. Read the license.

Built From

Described by arXiv:2606.19348

Memory Requirements

Precision	Weights in memory
As published	166.9 GB
16-bit	330.5 GB
8-bit	165.3 GB
4-bit	82.6 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Compare DeepSeek-V4-Flash-DSpark

Questions About DeepSeek-V4-Flash-DSpark

How much GPU memory does DeepSeek-V4-Flash-DSpark need?

About 396.6 GB at 16-bit and 99.2 GB at 4-bit: the weights (165.3B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run DeepSeek-V4-Flash-DSpark on?

At 16-bit, 2x MI325X from $4.00 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use DeepSeek-V4-Flash-DSpark commercially?

Yes. DeepSeek-V4-Flash-DSpark is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

What is DeepSeek-V4-Flash-DSpark's context length?

1,048,576 tokens, from the maximum position embeddings in its published configuration.

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