DeepSeek V4 Flash Vision Exp — Hardware Requirements & GPU Compatibility
Vision
DeepSeek V4 Flash Vision Exp is an experimental multimodal model from DeepSeek, a mixture-of-experts design with roughly 304.6 billion total parameters, only a small fraction of which are active per token. It extends the text-only DeepSeek V4 Flash line with image understanding, built for agentic tasks, reasoning, and tool use. Only the active parameters are computed per token, keeping decoding fast, but all weights must fit in memory, making this a server-class model best run across multiple GPUs or a hosted endpoint. The model supports a 1,048,576 token context window, suited to very long documents or codebases. It is released under the MIT license, one of the most permissive open licenses, with no restrictions on commercial use. Published in August 2026 as an experimental release, it brings image input to the Flash line.
DeepSeek V4 Flash Vision Exp runs on a 192 GB+ Mac or a datacenter GPU
Best local pick → Mac Pro M2 Ultra (192 GB) · Q4_K_M · 183.3 GB · ~40 tok/s
Macs from 96 GB (IQ2_XS · ~60 tok/s)
MIT
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run DeepSeek V4 Flash Vision Exp
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/unsloth/DeepSeek-V4-Flash-Vision-Exp-GGUFQ4_K_M runs on one B200 192 GB · 16K context
Download: DeepSeek-V4-Flash-Vision-Exp-Q4_K_M.gguf182.79 GBunsloth
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on B200 (192 GB) (8 GB headroom) · Q4_K_M
- Generation speed
- ~242 tok/s
- generation speed
- Cost per 1M output tokens
- $6.86
- per 1M output tokens
How Much VRAM Does DeepSeek V4 Flash Vision Exp Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 84.1 GB | 84.4 GB | 2-bit · i-quant extreme compression — Importance-weighted 2-bit, extreme compression — significant quality loss | Runs on RTX PRO 6000 Blackwell Max-Q Workstation Edition 96 GB | |
| 91.7 GB | 92.1 GB | 2-bit · i-quant extra small — Importance-weighted 2-bit, extra small | Runs on RTX PRO 6000 Blackwell Max-Q Workstation Edition 96 GB | |
| 129.8 GB | 130.1 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Runs on H200 NVL 141 GB | |
| 148.8 GB | 149.2 GB | 3-bit medium — 3-bit medium quantization | Runs on B200 192 GB | |
| 183.1 GB | 183.5 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Runs on B200 192 GB | |
| 217.4 GB | 217.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Runs on Instinct MI325X 256 GB | |
| 251.7 GB | 252 GB | 6-bit, very good — 6-bit quantization, very good quality | Runs on Instinct MI325X 256 GB | |
| 305.0 GB | 305.3 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Runs on 5× H100 SXM 80 GB |
Selected Q4_K_M
4-bit medium quantization183.3GBat 16K context
- Weights
- 182.8 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Server-class: ~183.3 GB at 16K context · one B200 192 GB
Or .
Which Hardware Can Run DeepSeek V4 Flash Vision Exp?
Q4_K_M · 183.1 GBDeepSeek V4 Flash Vision Exp (Q4_K_M) requires 183.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 239+ GB is recommended. Using the full 1049K context window can add up to 11.5 GB, bringing total usage to 194.6 GB. No consumer GPU has enough memory. 4 devices with unified memory can run DeepSeek V4 Flash Vision Exp, including Mac Studio (M3 Ultra, 512GB), Mac Studio (M3 Ultra, 256GB).
128–144 GB
Q4_K_M · best fit Q2_K_S (122.3 GB)
4 GPUs · 2 Mac classes
GH200 Grace Hopper Superchip~233Simulate output on GH200 Grace Hopper SuperchipH200 NVL~231Simulate output on H200 NVLH200 SXM~231Simulate output on H200 SXMM2 Ultra*128 GB~51Simulate output on Mac Pro M2 Ultra (128 GB)M5 Max128 GB~44Simulate output on Mac Studio M5 Max (128 GB)192+ GB
fits · 8.7 GB free · up to 512K ctx
6 GPUs · 1 Mac class · 2 device families
B200~242Simulate output on B200B300~242Simulate output on B300Instinct MI350X~95Simulate output on Instinct MI350XM3 Ultra*256–512 GB~41Simulate output on Mac Studio (M3 Ultra, 256GB)8x H100 SXM5~346Simulate output on NVIDIA DGX H1008x A100 SXM4~310Simulate output on NVIDIA DGX A100 640GB
By VRAM tier · typical GPUs and Macs at Q4_K_M
Consumer GPUs (up to 32 GB) and Macs up to 64 GB: too small for this model (needs ≥ 84.3 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
How good is DeepSeek V4 Flash Vision Exp?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
The 6 boards that weigh most in the llmrun Score, of 10 with a result for this model; badges show who ran the test.
- LiveBench Coding
LiveBench · Jun 2026Independent
Independent68.2
#19/20 open
- LiveBench Reasoning
LiveBench · Jun 2026Independent
Independent85.4
#8/20 open
- LiveBench Math
LiveBench · Jun 2026Independent
Independent87.8
#10/20 open
- LiveBench 2026 Coding
LiveBench · Oct 2026Independent
Independent37.6
#18/19 open
- LiveBench 2026 Long-Horizon
LiveBench · Oct 2026Independent
Independent39.0
#5/19 open
- LiveBench 2026 Design
LiveBench · Oct 2026Independent
Independent70.7
#10/19 open
Alternatives to DeepSeek V4 Flash Vision Exp
DeepSeek V4 family · 13 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| GLM 5.3 FlashZ.ai | 124.1 | 205.2 GB | —needs > 24 GB | same size |
| ERNIE 5.5 Flash BaseBaidu | — | 573 GB | —needs > 24 GB | same size |
| IQuest Q1IQuestLab | — | 195.4 GB | —needs > 24 GB | same size |
| MiMo V2.6 Flash MOPDXiaomi | — | 186.5 GB | —needs > 24 GB | same size |
| Hy3Tencent | — | 182.3 GB | —needs > 24 GB | same size |
| Step3StepFun | — | 219.1 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- DeepSeek
- Family
- DeepSeek V4
- Parameters
- 304.6B
- Active parameters
- 15.2B per token
- Architecture
- DeepseekV4ForCausalLM
- Context Length
- 1,048,576 tokens
- Vocabulary Size
- 129,280
- Release Date
- 2026-08-31
- License
- MIT
Frequently Asked Questions
How much VRAM does DeepSeek V4 Flash Vision Exp need?
DeepSeek V4 Flash Vision Exp requires 183.1 GB of VRAM at Q4_K_M, or 609.6 GB at BF16. Full 1049K context adds up to 11.5 GB (194.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 304.6B × 4.8 bits ÷ 8 = 182.8 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
Can NVIDIA GeForce RTX 5090 run DeepSeek V4 Flash Vision Exp?
No — DeepSeek V4 Flash Vision Exp requires at least 84.1 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for DeepSeek V4 Flash Vision Exp?
For DeepSeek V4 Flash Vision Exp, Q4_K_M (183.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (217.4 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 84.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run DeepSeek V4 Flash Vision Exp on a Mac?
Yes — Mac Studio (M3 Ultra, 96GB) and 20 other Macs can run DeepSeek V4 Flash Vision Exp, the smallest at IQ2_XS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 183.1 GB of usable unified memory (RAM minus macOS overhead), from Mac Pro M2 Ultra (192 GB).
Can I run DeepSeek V4 Flash Vision Exp locally?
Yes — DeepSeek V4 Flash Vision Exp can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is DeepSeek V4 Flash Vision Exp?
At Q4_K_M, DeepSeek V4 Flash Vision Exp can reach ~95 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 9.2 GB active ÷ (8000 × 0.65) = 1.76 ms, plus 43 layers × 0.055 ms = 2.37 ms, so 1000 ÷ 4.13 ms = ~242 tok/s
Estimated speed at Q4_K_M (183.1 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of DeepSeek V4 Flash Vision Exp?
At Q4_K_M, the download is about 182.79 GB. The full-precision BF16 version is 609.29 GB. The smallest option (IQ2_XXS) is 83.78 GB.
Which GPUs can run DeepSeek V4 Flash Vision Exp?
No single consumer GPU has enough VRAM to run DeepSeek V4 Flash Vision Exp at Q4_K_M (183.1 GB). Multi-GPU or professional hardware is required.
Which devices can run DeepSeek V4 Flash Vision Exp?
4 devices with unified memory can run DeepSeek V4 Flash Vision Exp at Q4_K_M (183.1 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.