DeepSeek V4.1 Flash 1017 — Hardware Requirements & GPU Compatibility
Vision
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.
DeepSeek V4.1 Flash 1017 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_XS · ~61 tok/s)
Score 133.2#1 of 101 open#1 of 98 that fit ≤512 GBMIT
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
133.2
- 90% range 130.6–135.8
- #1 of 101 open models
- #1 of 98 that fit ≤512 GB
Run DeepSeek V4.1 Flash 1017
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve deepseek-ai/DeepSeek-V4.1-Flash-1017 --max-model-len 16384 --tensor-parallel-size 8Size the nodes from the table above
Download: Q4_K_M/DeepSeek-V4.1-Flash-1017-Q4_K_M-00001-of-00010.gguf444.74 GB + 9 partsmrfakename
Other 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
How Much VRAM Does DeepSeek V4.1 Flash 1017 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 | |
| 229.3 GB | 229.7 GB | 2-bit · i-quant extra small — Importance-weighted 2-bit, extra small | Runs on Instinct MI325X 256 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 |
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 1017?
Q4_K_M · 458.3 GBDeepSeek 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. 1 device with unified memory can run DeepSeek V4.1 Flash 1017, including Mac Studio (M3 Ultra, 512GB).
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 | Quant | Memory | Speed | Context | Hardware page | |
|---|---|---|---|---|---|---|
| Mac 256 GB · Mac Studio (M3 Ultra, 256GB) | IQ2_XS | 229.5 GB | ~61 tok/s | 1M ctx | Mac Studio → | IQ2_XS · 229.5 GB · ~61 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 1017?
The llmrun Score merges 10 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
#1 of 98 that fit ≤512 GB
Each dot is a model at the best quant that fits ≤512 GB. The line joins the models with the best Score for their 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 15 with a result for this model; badges show who ran the test.
- GPQA Diamond
Epoch AI · Oct 2026Independent
Independent93.2%
#1/99 open
- AIME 2024/2025
Epoch AI · Oct 2026Independent
Independent99.4%
#1/87 open
- FrontierMath
Epoch AI · Oct 2026Independent
Independent71.2%
#2/18 open
- LMArena WebDev
LMArena · Oct 2026Arena
Arena1665.0
#1/55 open
- SimpleQA
Epoch AI · Oct 2026Independent
Independent42.5%
#6/18 open
- FrontierMath Tier 4
Epoch AI · Oct 2026Independent
Independent46.3%
#1/14 open
In the ≤512 GB class
98 models fit ≤512 GBSame Score, only models that fit ≤512 GB at their best quant: this model's peers by memory need.
- #2
MiMo V2.6 Pro RLXiaomi
133.2
499.3 GB
- #3
MiMo V2.6 Flash RLXiaomi
131.9
186.5 GB
- #4
GLM 5.3Z.ai
130.1
453.8 GB
- #5
DeepSeek V4.1 FlashDeepSeek
129.4
458.4 GB
- #6
Step 5 PreviewStepFun
128.0
407.5 GB
Alternatives to DeepSeek V4.1 Flash 1017
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.
Runner-up at this size
GLM 5.3
Z.ai
- 3.1 lower Score
- 4.7 GB less VRAM
Score 130.1 · 453.8 GB
Fits in less VRAM
GLM 5.3 Flash
Z.ai
- 9.1 lower Score
- 253.3 GB less VRAM
Score 124.1 · 205.2 GB
Other vendor
Step 5 Preview
StepFun
- 5.2 lower Score
- 51 GB less VRAM
Score 128.0 · 407.5 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 |
| DeepSeek V4.1 FlashDeepSeek | 129.4 | 458.4 GB | —needs > 24 GB | similar score |
| GLM 5.2Z.ai | 122.9 | 453.8 GB | —needs > 24 GB | similar score |
| Qwen3.8 27BAlibaba | 121.9 | 17.9 GB | ~38 | similar score |
| Muse Spark Open 816B A42BMeta | 125.6 | 551.9 GB | —needs > 24 GB | same size |
| GLM 5.1Z.ai | 117.8 | 454.1 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 | 109.6 | 378.4 GB | —needs > 24 GB | same size |
| DeepSeek V3.2 ExpDeepSeek | 109.0 | 412.7 GB | —needs > 24 GB | same size |
Fine-tunes & variants(2)
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
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
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
★ Recommended — best balance of quality and VRAM usage.
Can I run DeepSeek V4.1 Flash 1017 on a Mac?
Yes — Mac Studio (M3 Ultra, 256GB) and 1 other Mac 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 (M3 Ultra, 512GB).
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?
1 device with unified memory can run DeepSeek V4.1 Flash 1017 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.