DeepSeek V4.1 Flash — Hardware Requirements & GPU Compatibility
VisionFunctions
DeepSeek-V4.1-Flash is DeepSeek's multimodal Mixture-of-Experts model, processing images and text together and generating text autoregressively. It uses a Causal Encoder-Decoder architecture with roughly 763 billion total parameters, but activates only about 8 billion during prefill and 16 billion during decode, keeping input-heavy agentic workloads efficient. The model supports a continuously adjustable reasoning-effort setting that trades inference cost for accuracy, and was trained with aggressive KV-cache compression aimed at long-context, tool-using agents. Context reaches up to one million tokens, and it is released under the MIT license. Given its size, local use calls for multi-GPU or server-class hardware, with roughly 439GB of memory needed even at 4-bit; most users will rely on a hosted API instead.
DeepSeek V4.1 Flash runs on a 512 GB+ Mac or a multi-GPU server
Best local pick → Mac Studio (M3 Ultra, 512GB) · Q4_K_M · 458.4 GB · ~40 tok/s
Macs from 256 GB (IQ2_XXS · ~64 tok/s)
Score 129.4#7 of 106 open#5 of 102 that fit ≤512 GBMIT
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
129.4
- 90% range 127.7–131.1
- #7 of 106 open models
- #5 of 102 that fit ≤512 GB
Run DeepSeek V4.1 Flash
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
vllm serve deepseek-ai/DeepSeek-V4.1-Flash --max-model-len 16384 --tensor-parallel-size 8Size the nodes from the table above
Download: DeepSeek-V4.1-Flash-Q4_K_M-00001-of-00011.gguf444.74 GB + 10 partsvcruz305
Ways to runOther ways
each opens step-by-stepRun in cloud
Rent 7× H100 SXM · Q4_K_M
- Estimated hourly price
- ~$18.83/hr
- est., whole server
- GPUs
- 7 GPUs
- 560 GB of VRAM
$5+ bonus for new users
How Much VRAM Does DeepSeek V4.1 Flash Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 210.2 GB | 210.6 GB | 2-bit · i-quant extreme compression — Importance-weighted 2-bit, extreme compression — significant quality loss | Runs on Instinct MI325X 256 GB | |
| 315.1 GB | 315.5 GB | 3-bit · i-quant extra small — Importance-weighted 3-bit, extra small | Runs on 5× H100 SXM 80 GB | |
| 324.7 GB | 325.1 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Runs on 5× H100 SXM 80 GB | |
| 372.4 GB | 372.8 GB | 3-bit medium — 3-bit medium quantization | Runs on 6× H100 SXM 80 GB | |
| 458.3 GB | 458.6 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Runs on 7× H100 SXM 80 GB | |
| 544.1 GB | 544.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Runs on 8× H100 SXM 80 GB | |
| 630.0 GB | 630.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Runs on 5× H200 SXM 141 GB | |
| 763.5 GB | 763.9 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Runs on 7× H200 SXM 141 GB | |
| 1526.7 GB | 1527.1 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Runs on 3 nodes of 8× H100 SXM |
Selected Q4_K_M
4-bit medium quantization458.4GBat 16K context
- Weights
- 457.9 GB
- KV cache
- 0.2 GB
- Overhead
- 0.3 GB
Server-class: ~458.4 GB at 16K context · about 7× H100 SXM 80 GB
Which Hardware Can Run DeepSeek V4.1 Flash?
Q4_K_M · 458.3 GBDeepSeek V4.1 Flash (Q4_K_M) requires 458.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 596+ GB is recommended. Using the full 1049K context window can add up to 13.4 GB, bringing total usage to 471.6 GB. No consumer GPU has enough memory. 1 device with unified memory can run DeepSeek V4.1 Flash, including Mac Studio (M3 Ultra, 512GB).
Needs server-class hardware
DeepSeek V4.1 Flash runs on a 512 GB+ Mac or a multi-GPU server · Q4_K_M needs ~458.4 GB
| Hardware | Quant | Memory | Speed | Context | Hardware page | |
|---|---|---|---|---|---|---|
| Mac 256 GB · Mac Studio (M3 Ultra, 256GB) | IQ2_XXS | 210.4 GB | ~64 tok/s | 1M ctx | Mac Studio → | IQ2_XXS · 210.4 GB · ~64 tok/s · 1M ctx |
| Mac 512 GB · Mac Studio (M3 Ultra, 512GB) | Q4_K_M | 458.4 GB | ~40 tok/s | 1M ctx | Mac Studio → | Q4_K_M · 458.4 GB · ~40 tok/s · 1M ctx |
| 7× H100 SXM 80 GB · 560 GB | Q4_K_M | 458.4 GB | — | 1M ctx | H100 SXM → | Q4_K_M · 458.4 GB · 1M ctx |
| 4× H200 SXM 141 GB · 564 GB | Q4_K_M | 458.4 GB | — | 1M ctx | H200 SXM → | Q4_K_M · 458.4 GB · 1M ctx |
| 3× B200 192 GB · 576 GB | Q4_K_M | 458.4 GB | — | 1M ctx | B200 → | Q4_K_M · 458.4 GB · 1M ctx |
Also: Mac Studio (M3 Ultra, 512GB) · NVIDIA DGX A100 640GB · NVIDIA DGX H100
By VRAM tier · typical GPUs and Macs at Q4_K_M
Consumer GPUs (up to 32 GB) and Macs up to 192 GB: too small for this model (needs ≥ 210.4 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
How good is DeepSeek V4.1 Flash?
The llmrun Score merges 25 public benchmark boards into a single figure, where 100 represents the average for open models.
Score vs VRAM in the ≤512 GB tier
- DeepSeek V4.1 Flash
- Other models
- Best Score for its VRAM
#5 of 102 that fit ≤512 GB
Each dot is a model at the best quant that fits ≤512 GB. The line steps up to the best Score available at each amount of VRAM; a dot below it is beaten by a model that needs no more memory.
Full ≤512 GB ranking →Benchmarks
The 6 boards that weigh most in the llmrun Score, of 33 with a result for this model; badges show who ran the test.
- GPQA Diamond
Epoch AI · Sep 2026Independent
Independent89.8%
#13/100 open
- LMArena Text
LMArena · Oct 2026Arena
Arena1476.2
#5/188 open
- LiveBench Coding
LiveBench · Jun 2026Independent
Independent80.0
#2/20 open
- LiveBench Reasoning
LiveBench · Jun 2026Independent
Independent86.7
#3/20 open
- AIME 2024/2025
Epoch AI · Sep 2026Independent
Independent98.3%
#4/88 open
- FrontierMath
Epoch AI · Sep 2026Independent
Independent67.4%
#5/19 open
In the ≤512 GB class
102 models fit ≤512 GBSame Score, only models that fit ≤512 GB at their best quant: this model's peers by memory need.
- #1
DeepSeek V4.1 Flash 1017DeepSeek
134.4
458.4 GB
- #2
MiMo V2.6 Pro RLXiaomi
133.3
499.3 GB
- #3
MiMo V2.6 Flash RLXiaomi
131.9
186.5 GB
- #4
GLM 5.3Z.ai
130.1
453.8 GB
- #6
Step 5 PreviewStepFun
128.0
407.5 GB
Alternatives to DeepSeek V4.1 Flash
DeepSeek V4 family · 13 models →Official models with an llmrun Score, each answering one question; sizes are each model's default quant (usually Q4_K_M) with a 16K-token context.
Best alternative at this size
DeepSeek V4.1 Flash 1017
DeepSeek
- 5 higher Score
Score 134.4 · 458.4 GB
Fits in less VRAM
GLM 5.3 Flash
Z.ai
- 5.3 lower Score
- 253.3 GB less VRAM
Score 124.1 · 205.2 GB
Other vendor
GLM 5.3
Z.ai
- 0.7 higher Score
- 4.7 GB less VRAM
Score 130.1 · 453.8 GB
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MiMo V2.6 Flash RLXiaomi | 131.9 | 186.5 GB | —needs > 24 GB | similar score |
| Step 5 PreviewStepFun | 128.0 | 407.5 GB | —needs > 24 GB | similar score |
| GLM 5.2Z.ai | 122.9 | 453.8 GB | —needs > 24 GB | similar score |
| Qwen3.8 27BAlibaba | 122.0 | 17.9 GB | ~38 | similar score |
| Beam 1.0 501B A23Breflection | 119.5 | 304 GB | —needs > 24 GB | similar score |
| GLM 5.1Z.ai | 117.8 | 454.1 GB | —needs > 24 GB | similar score |
| Muse Spark Open 816B A42BMeta | 126.8 | 551.9 GB | —needs > 24 GB | same size |
| GLM 5Z.ai | 111.8 | 454.1 GB | —needs > 24 GB | same size |
| DeepSeek V3.2DeepSeek | 110.4 | 412.7 GB | —needs > 24 GB | same size |
| NVIDIA Nemotron 3 Ultra 550B A55B BF16NVIDIA | 109.7 | 378.4 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- DeepSeek
- Family
- DeepSeek V4
- Parameters
- 763.2B
- Active parameters
- 16B per token
- Architecture
- DeepseekV41ForCausalLM
- Context Length
- 1,048,576 tokens
- Vocabulary Size
- 129,280
- Release Date
- 2026-09-10
- License
- MIT
Frequently Asked Questions
How much VRAM does DeepSeek V4.1 Flash need?
DeepSeek V4.1 Flash requires 458.3 GB of VRAM at Q4_K_M, or 1526.7 GB at BF16. Full 1049K context adds up to 13.4 GB (471.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 763.2B × 4.8 bits ÷ 8 = 457.9 GB
KV Cache + Overhead ≈ 0.4 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 ≈ 13.7 GB (at full 1049K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run DeepSeek V4.1 Flash?
No — DeepSeek V4.1 Flash requires at least 210.2 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for DeepSeek V4.1 Flash?
For DeepSeek V4.1 Flash, Q4_K_M (458.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (544.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 210.2 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run DeepSeek V4.1 Flash on a Mac?
Yes — Mac Studio (M3 Ultra, 256GB) and 1 other Mac can run DeepSeek V4.1 Flash, the smallest at IQ2_XXS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 458.3 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio (M3 Ultra, 512GB).
Can I run DeepSeek V4.1 Flash locally?
Yes — DeepSeek V4.1 Flash can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
What's the download size of DeepSeek V4.1 Flash?
At Q4_K_M, the download is about 457.92 GB. The full-precision BF16 version is 1526.41 GB. The smallest option (IQ2_XXS) is 209.88 GB.
Which GPUs can run DeepSeek V4.1 Flash?
No single consumer GPU has enough VRAM to run DeepSeek V4.1 Flash at Q4_K_M (458.3 GB). Multi-GPU or professional hardware is required.
Which devices can run DeepSeek V4.1 Flash?
1 device with unified memory can run DeepSeek V4.1 Flash at Q4_K_M (458.3 GB), including Mac Studio (M3 Ultra, 512GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.