llmrun
DeepSeekDeepSeek V4DeepseekV41ForCausalLM

DeepSeek V4.1 Flash 1017 — Hardware Requirements & GPU Compatibility

VisionFunctions

DeepSeek-V4.1-Flash-1017 is a minor update to DeepSeek-V4.1-Flash, the multimodal mixture-of-experts model from DeepSeek that takes images and text and generates text. It has about 763 billion total parameters with roughly 16 billion active per token. DeepSeek says the update improves reasoning, coding, agentic tasks, and visual benchmarks while cutting task execution time and output length. A Q4 build still needs roughly 435 GB of memory, so local use means a multi-GPU server or a very large unified-memory machine. The recommended context window is 1 million tokens, and the model is released under the MIT license, which permits commercial use. It was published on October 17, 2026. Its card reports 42.3 on Terminal-Bench 4.0 against 31.2 for the earlier V4.1-Flash, and 77.0 on DeepSWE v1.1 against 74.2. There is no Jinja chat template; DeepSeek ships a Python encoding reference and a separate Rust toolkit for building prompts.

763.2B params · MoE16B active1049K contextMIT398.2K downloads5.7K likes1.4M quant downloads

DeepSeek V4.1 Flash 1017 runs on a 512 GB+ Mac or a multi-GPU server

Best local pick → Mac Studio M5 Ultra (512 GB) · Q4_K_M · 458.4 GB · ~52 tok/s

Macs from256 GB IQ2_XS · ~74 tok/s

Score 134.5#3 of 107 open#2 of 103 that fit ≤512 GBMIT

Not set up for it?

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

SOn your NVIDIA DGX B300: Q4_K_M fits · 1845.6 GB free · ~320 tok/s · up to 1M contextNVIDIA DGX B300 page →

llmrun Composite Score →

134.5

Benchmarks · tier chart ↓
Among 107 open modelsHow the Composite Score is built

Run DeepSeek V4.1 Flash 1017

Best fit for your NVIDIA DGX B300. 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 for your NVIDIA DGX B300

Ollama
one command · needs Ollama installed
$ollama run hf.co/mrfakename/DeepSeek-V4.1-Flash-1017-GGUF:Q4_K_M

Q4_K_M fits fully · 1845.6 GB free · 16K context

Download: Q4_K_M/DeepSeek-V4.1-Flash-1017-Q4_K_M-00001-of-00010.gguf444.74 GB + 9 partsmrfakename

install, OS, other quants

Ways to runOther ways

each opens step-by-step

Run 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
Deployon RunPodRent H100 SXM$2.69/hr per GPU (opens in new tab)

$5+ bonus for new users

Compare GPUs →

How Much VRAM Does DeepSeek V4.1 Flash 1017 Need?

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

QuantizationVRAM+32KOn your device
210.2 GB210.6 GB2-bit · i-quant extreme compression — Importance-weighted 2-bit, extreme compression — significant quality lossFits: 1M context · ~328 tok/s
229.3 GB229.7 GB2-bit · i-quant extra small — Importance-weighted 2-bit, extra smallFits: 1M context · ~320 tok/s
324.7 GB325.1 GB2-bit · K-quant — 2-bit quantization with K-quant improvementsFits: 1M context · ~350 tok/s
372.4 GB372.8 GB3-bit medium — 3-bit medium quantizationFits: 1M context · ~339 tok/s
458.3 GB458.6 GB4-bit medium — 4-bit medium quantization — most popular sweet spotFits: 1M context · ~320 tok/s
544.1 GB544.5 GB5-bit medium — 5-bit medium quantization — good quality/size tradeoffFits: 1M context · ~303 tok/s
630.0 GB630.4 GB6-bit, very good — 6-bit quantization, very good qualityFits: 1M context · ~353 tok/s
763.5 GB763.9 GB8-bit, near-lossless — 8-bit quantization, near-losslessFits: 1M context · ~337 tok/s
est.calculated, no published file yet

Selected Q4_K_M

4-bit medium quantization

458.4GBat 16K context

Weights
457.9 GB
KV cache
0.2 GB
Overhead
0.3 GB

Fits NVIDIA DGX B300 · 1845.6 GB free · ~320 tok/s

≥50 tok/s needs ≥829 GB/s · M5 Ultra ~52

Which Hardware Can Run DeepSeek V4.1 Flash 1017?

Q4_K_M · 458.3 GB

DeepSeek V4.1 Flash 1017 (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. 2 devices with unified memory can run DeepSeek V4.1 Flash 1017, including Mac Studio M5 Ultra (512 GB).

Needs server-class hardware

DeepSeek V4.1 Flash 1017 runs on a 512 GB+ Mac or a multi-GPU server · Q4_K_M needs ~458.4 GB

Hardware that runs this model
HardwareQuantMemorySpeedContextHardware page
Mac 256 GB · Mac Studio M5 Ultra (256 GB)IQ2_XS229.5 GB~74 tok/s1M ctxMac Studio M5 Ultra →IQ2_XS · 229.5 GB · ~74 tok/s · 1M ctx
Mac 512 GB · Mac Studio M5 Ultra (512 GB)Q4_K_M458.4 GB~52 tok/s1M ctxMac Studio M5 Ultra →Q4_K_M · 458.4 GB · ~52 tok/s · 1M ctx
7× H100 SXM 80 GB · 560 GBQ4_K_M458.4 GB—1M ctxH100 SXM →Q4_K_M · 458.4 GB · 1M ctx
4× H200 SXM 141 GB · 564 GBQ4_K_M458.4 GB—1M ctxH200 SXM →Q4_K_M · 458.4 GB · 1M ctx
3× B200 192 GB · 576 GBQ4_K_M458.4 GB—1M ctxB200 →Q4_K_M · 458.4 GB · 1M ctx

Also: AMD Instinct MI355X Platform (8x MI355X) · Mac Studio (M3 Ultra, 512GB) · Mac Studio M5 Ultra (512 GB) · NVIDIA DGX A100 640GB · NVIDIA DGX B200 · NVIDIA DGX B300 · NVIDIA DGX H100 · NVIDIA DGX H200 · NVIDIA GB200 NVL72 · NVIDIA GB300 NVL72

≤ 20736 GB tier →

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 1017?

The llmrun Score merges 17 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 1017
  • Other models
  • Best Score for its VRAM

#2 of 103 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 23 with a result for this model; badges show who ran the test.

  • GPQA Diamond

    Epoch AI · Oct 2026Independent

    93.2%

    #1/100 open

    Independent
  • AIME 2024/2025

    Epoch AI · Oct 2026Independent

    99.4%

    #1/88 open

    Independent
  • FrontierMath

    Epoch AI · Oct 2026Independent

    71.2%

    #2/19 open

    Independent
  • LMArena WebDev

    LMArena · Nov 2026Arena

    1663.7

    #2/58 open

    Arena
  • ARC-AGI-2

    Epoch AI · Oct 2026Independent

    78.1%

    #2/23 open

    Independent
  • SciCode

    Epoch AI · Oct 2026Lab-reported

    55.9%

    #7/74 open

    Lab-reported
All 23 DeepSeek V4.1 Flash 1017 benchmarks

In the ≤512 GB class

103 models fit ≤512 GB

Same Score, only models that fit ≤512 GB at their best quant: this model's peers by memory need.

  1. #1

    Inkling 2 SmallThinking Machines

    134.7

    359.1 GB

  2. #2

    DeepSeek V4.1 Flash 1017DeepSeek

    134.5

    458.4 GB

  3. #3

    MiMo V2.6 Pro RLXiaomi

    133.5

    499.3 GB

  4. #4

    MiMo V2.6 Flash RLXiaomi

    131.9

    186.5 GB

  5. #5

    GLM 5.3Z.ai

    130.2

    453.8 GB

  6. #6

    DeepSeek V4.1 FlashDeepSeek

    129.4

    458.4 GB

Full ≤512 GB ranking →

Alternatives to DeepSeek V4.1 Flash 1017

DeepSeek V4 family · 15 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.

Similar models

10
ModelScoreVRAMWhy it is similar
MiMo V2.6 Flash RLXiaomi131.9186.5 GBsimilar score
DeepSeek V4.1 FlashDeepSeek129.4458.4 GBsimilar score
Step 5 PreviewStepFun128.0407.5 GBsimilar score
GLM 5.2Z.ai122.9453.8 GBsimilar score
Qwen3.8 27BAlibaba121.917.9 GBsimilar score
Beam 1.0 501B A23Breflection119.5304 GBsimilar score
Muse Spark Open 816B A42BMeta126.9551.9 GBsame size
GLM 5.1Z.ai117.8454.1 GBsame size
GLM 5Z.ai111.8454.1 GBsame size
DeepSeek V3.2DeepSeek110.4412.7 GBsame size
Fine-tunes & variants(4)

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
2026-10-17
License
MIT

Resources

Frequently Asked Questions

How much VRAM does DeepSeek V4.1 Flash 1017 need?

DeepSeek V4.1 Flash 1017 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

458.3 GB
471.6 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run DeepSeek V4.1 Flash 1017?

No — DeepSeek V4.1 Flash 1017 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 1017?

For DeepSeek V4.1 Flash 1017, 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

IQ2_XXS
210.2 GB
Q2_K_S
305.6 GB
Q3_K_M
372.4 GB
Q4_K_M ★
458.3 GB
Q5_K_M
544.1 GB
BF16
1526.7 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run DeepSeek V4.1 Flash 1017 on a Mac?

Yes — Mac Studio M5 Ultra (256 GB) and 3 other Macs can run DeepSeek V4.1 Flash 1017, 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 458.3 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio M5 Ultra (512 GB).

Can I run DeepSeek V4.1 Flash 1017 locally?

Yes — DeepSeek V4.1 Flash 1017 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 1017?

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 1017?

No single consumer GPU has enough VRAM to run DeepSeek V4.1 Flash 1017 at Q4_K_M (458.3 GB). Multi-GPU or professional hardware is required.

Which devices can run DeepSeek V4.1 Flash 1017?

2 devices with unified memory can run DeepSeek V4.1 Flash 1017 at Q4_K_M (458.3 GB), including Mac Studio (M3 Ultra, 512GB), Mac Studio M5 Ultra (512 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.