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dealignaiDeepSeek V4DeepseekV4ForCausalLM

DeepSeek V4 Flash JANG CRACK — Hardware Requirements & GPU Compatibility

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DeepSeek V4 Flash JANG CRACK is a 33.5B-parameter open language model from dealignai in the DeepSeek V4 family. It supports a context window of up to 1,048,576 tokens. At Q4_K_M it needs about 20.42 GB of VRAM — see which GPUs and Macs can run it below.

33.5B params · dense1049K contextMIT3.6K downloads12 likes
B

Decent on a 24 GB GPU

Runs from16 GB Q2_K · 128KBest on24 GB Q4_K_M · ~32 tok/s · 256KMacs from24 GB Q4_K_M · ~11 tok/s

MIT

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

Run DeepSeek V4 Flash JANG CRACK

One recommended way. Every other method, OS and quant is in the step-by-step guide.

Recommended

vLLM
one command · needs vLLM installed
$vllm serve dealignai/DeepSeek-V4-Flash-JANG-CRACK --max-model-len 16384

vLLM repo with its own quantisation

Download: Weights on Hugging Face

install, OS, other quants

Other ways

each opens step-by-step

Run in cloud

Fits on RTX PRO 4500 (32 GB) (11 GB headroom) · Q4_K_M

Generation speed
~29 tok/s
generation speed
Cost per 1M output tokens
$3.31
per 1M output tokens
Deployon RunPodBest optionfrom $0.34/hr (opens in new tab)
Compare GPUs →
or

How Much VRAM Does DeepSeek V4 Flash JANG CRACK Need?

Select a quantization level; the listed hardware adheres to it.

QuantizationVRAM+32KOn RTX 4090 (reference)
14.6 GB14.9 GB2-bit · K-quant — 2-bit quantization with K-quant improvementsFits: 512K context · ~45 tok/s
16.6 GB17 GB3-bit medium — 3-bit medium quantizationFits: 512K context · ~39 tok/s
20.4 GB20.8 GB4-bit medium — 4-bit medium quantization — most popular sweet spotFits: 256K context · ~32 tok/s
24.2 GB24.5 GB5-bit medium — 5-bit medium quantization — good quality/size tradeoffDoes not fit: needs 24.3 GB
27.9 GB28.3 GB6-bit, very good — 6-bit quantization, very good qualityDoes not fit: needs 28.1 GB
33.8 GB34.2 GB8-bit, near-lossless — 8-bit quantization, near-losslessDoes not fit: needs 34 GB
67.3 GB67.6 GB16-bit · bfloat — Brain floating point 16 — preferred for trainingDoes not fit: needs 67.5 GB
est.calculated, no published file yet

Selected Q4_K_M

4-bit medium quantization

20.6GBat 16K context

Weights
20.1 GB
KV cache
0.2 GB
Overhead
0.3 GB

Fits RTX 4090 (reference) · 3.4 GB free · ~32 tok/s

≥50 tok/s needs ≥1.57 TB/s · RTX 5090 ~57

Which Hardware Can Run DeepSeek V4 Flash JANG CRACK?

Q4_K_M · 20.4 GB

DeepSeek V4 Flash JANG CRACK (Q4_K_M) requires 20.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 27+ GB is recommended. Using the full 1049K context window can add up to 11.5 GB, bringing total usage to 31.9 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run DeepSeek V4 Flash JANG CRACK, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M1 Max (32 GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M
8–12 GB · RTX 4060, RTX 3060 12GBDoesn't fit — smallest quant needs 14.7 GB8 GB tier →12 GB tier →
16 GB · RTX 5060 Ti 16GBQ2_K · 14.7 GB · ~20 tok/s · 128K16 GB tier →
24 GB · RTX 4090referenceQ4_K_M · 20.6 GB · ~32 tok/s · 256K24 GB tier →
Mac 32 GB · Mac Mini M4Q4_K_M · 20.6 GB · ~4 tok/s · 512KMacs →
Mac 64 GB · Mac Studio M4 MaxQ4_K_M · 20.6 GB · ~19 tok/s · 1MMacs →

Alternatives to DeepSeek V4 Flash JANG CRACK

DeepSeek V4 family · 12 models →

Similar models

6
ModelScoreVRAMtok/sWhy it is similar
MiniCPM V 4.7 35B A3BOpenBMB—22.1 GB~169same size
Llm Jp 4.1 32B A3b Thinkingllm-jp—20.1 GB~188same size
Llm Jp 4.1 33B Thinkingllm-jp—22.5 GB~35same size
Agnes 3.0 FlashAgnes-AI—22.1 GB~35same size
Ornith 1.5 35B A3Bornith-ai—22 GB~151same size
NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA—19.3 GB~197same size

Specifications

Publisher
dealignai
Family
DeepSeek V4
Parameters
33.5B
Architecture
DeepseekV4ForCausalLM
Context Length
1,048,576 tokens
Vocabulary Size
129,280
Release Date
2026-05-23
License
MIT

Resources

Frequently Asked Questions

How much VRAM does DeepSeek V4 Flash JANG CRACK need?

DeepSeek V4 Flash JANG CRACK requires 20.4 GB of VRAM at Q4_K_M, or 67.3 GB at BF16. Full 1049K context adds up to 11.5 GB (31.9 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 33.5B × 4.8 bits ÷ 8 = 20.1 GB

KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)

Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.

KV Cache + Overhead ≈ 11.8 GB (at full 1049K context)

VRAM usage by quantization

20.4 GB
31.9 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 4090 run DeepSeek V4 Flash JANG CRACK?

Yes, at Q4_K_M (20.4 GB) or lower. Higher quantizations like Q5_K_M (24.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for DeepSeek V4 Flash JANG CRACK?

For DeepSeek V4 Flash JANG CRACK, Q4_K_M (20.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (24.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 14.6 GB.

VRAM requirement by quantization

Q2_K
14.6 GB
Q4_K_M ★
20.4 GB
Q5_K_M
24.2 GB
Q6_K
27.9 GB
Q8_0
33.8 GB
BF16
67.3 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run DeepSeek V4 Flash JANG CRACK on a Mac?

Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run DeepSeek V4 Flash JANG CRACK. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 20.4 GB of usable unified memory (RAM minus macOS overhead).

Can I run DeepSeek V4 Flash JANG CRACK locally?

Yes — DeepSeek V4 Flash JANG CRACK can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is DeepSeek V4 Flash JANG CRACK?

At Q4_K_M, DeepSeek V4 Flash JANG CRACK can reach ~235 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~32 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: NVIDIA B200 → 8000 ÷ 20.4 × 0.65 = ~255 tok/s

Estimated speed at Q4_K_M (20.4 GB)

~255 tok/s
~32 tok/s
~255 tok/s
~235 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of DeepSeek V4 Flash JANG CRACK?

At Q4_K_M, the download is about 20.09 GB. The full-precision BF16 version is 66.98 GB. The smallest option (Q2_K) is 14.23 GB.

Which GPUs can run DeepSeek V4 Flash JANG CRACK?

7 consumer GPUs can run DeepSeek V4 Flash JANG CRACK at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU has plenty of headroom for comfortable inference.

Which devices can run DeepSeek V4 Flash JANG CRACK?

122 devices with unified memory can run DeepSeek V4 Flash JANG CRACK at Q4_K_M (20.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.