DeepSeek-V4.1-Flash-1017 VRAM and RAM requirements: can you run it locally?

DeepSeek-V4.1-Flash-1017 is DeepSeek's October 2026 update to DeepSeek-V4.1-Flash. Its Hugging Face repo was created on October 17, 2026, it is MIT licensed, and Hugging Face counts 763,205,315,794 parameters in the official checkpoint, which is 510.3 GB of FP8 weights with FP4 experts.^[1] DeepSeek describes the model as 552B backbone parameters plus 196B Engram "conditional memory" parameters, a mixture-of-experts model that activates 8B parameters per token during prefill and 16B during decode, with a context of up to 1,048,576 tokens.^[2][3] DeepSeek calls 1017 a minor update, and its config.json is identical to V4.1-Flash's (checked October 23, 2026).^[3]

It is also the model people are asking about this week. DeepSeek's launch post on X has more than 1,600 replies, Unsloth's "can now run locally" post has more than 300,000 views, and "how much memory does it need to run locally?" is a common reply under both (checked October 23, 2026).

Why it needs less memory than its size suggests

Most of a big model has to sit in RAM or VRAM while it runs. DeepSeek-V4.1-Flash is different in one way: its Engram n-gram table (the 196B Engram parameters) is only looked up a few rows at a time, so llama.cpp can leave it on an SSD and read rows on demand through mmap.^[5] Unsloth keeps that table in its own file in every quant, and the table alone is 110.6 GB to 202.8 GB depending on its precision.^[6] What's left after taking the table out is the part that really needs memory.

You still need the full download on disk, and a fast NVMe SSD, because the table is read during generation.^[5]

Memory needed at each quantization

There is no Q4_K_M or Q8_0 build of this model. These are Unsloth's dynamic GGUFs, with the exact file sizes on Hugging Face (shards added up, 1 GB = 1,000,000,000 bytes), largest first.^[4] "RAM + VRAM" is the download minus the Engram shard, which matches the "Rest GB" minimum on Unsloth's guide.^[5]

The chart below shows the full download sizes.^[4]

Unsloth's own hardware guide, which adds "roughly 5–10 GB" on top of these numbers, is 128–145 GB for 1-bit, 176–205 GB for 2-bit, 256–290 GB for 3-bit, 305 GB for 4-bit and 315 GB for 8-bit (RAM + VRAM, or unified memory, with Engram on SSD).^[5] UD-Q4_K_XL and UD-Q8_K_XL differ by only 7.7 GB because the experts stay in their original MXFP4 format in both.^[5]

Which quant should you pick? Unsloth measured how closely each quant matches DeepSeek's reference outputs. UD-IQ1_M matches the original's top token 87.1% of the time (wiki1100 test), UD-Q2_K_XL 91.5%, UD-IQ3_XXS 94.6% and UD-Q4_K_XL 98.2%.^[5] UD-IQ1_M costs 17.4 GB more than UD-IQ1_S and is clearly better, so pick it if you can.^[5]

Context length adds very little memory (the KV cache)

This is the model's other trick. DeepSeek's model card gives a global KV cache of 890 bytes per token, using FP4 KV and compressed sparse attention, about a quarter of DeepSeek-V4-Flash's.^[2] That is why we don't use our usual 2 × layers × KV heads × head size formula here.

Even the full 1,048,576-token context needs under 1 GB of KV cache, so context length barely changes which machine you need. The engine's working buffers and Unsloth's "5–10 GB" margin matter more.^[5] This figure is DeepSeek's number for its own design. Engines that don't implement the FP4 cache may use more.

Which Macs, PCs and GPUs it fits on

Each cell compares the RAM + VRAM part of each quant (Engram on SSD) plus an 8K-token KV cache with the machine's total memory.^{[9][10][11][12][13][14]}

Column headers show the weights that must sit in RAM/VRAM with the Engram table left on SSD (download size minus the Engram shard), plus an 8,192-token KV cache at 890 bytes per token. GPU + RAM rows add both memories together, which is how llama.cpp offloading uses them.

How to read it: "Fits" means under 90% of memory. "Tight" means 90–100%: it may load, but the OS, the engine or a longer context can push it over. "No" means it won't fit. The 90% line is our own rule of thumb, not a vendor figure. Rows like "RTX 5090 + 128 GB RAM" add the GPU's VRAM to system RAM, which is how llama.cpp splits a model, but layers in system RAM run much slower. On a Mac, macOS needs some of the unified memory too, so treat Mac rows as optimistic.

Our recommendations

These follow from the tables above.

One real-world report (not our test): a developer ran it on a workstation with one RTX 5090 (32 GB) and 128 GB of DDR5, with a 262K context, at about 21–25 tokens per second, using the Project Maya engine and its own 340 GB 2–3-bit quant. The card's VRAM peaked at 28.7 GiB, and parts of the model were read from the NVMe SSD during generation.^[17]

How it compares with other big open models

4-bit file sizes and KV cache per token come from each model's Hugging Face GGUF repo and model card, the same data as our comparison table.^[16]

Its full 4-bit download is about 4× Qwen3.8 Flash Next's, but with the Engram table on SSD its 1-bit build (122.4 GB) is close to Qwen3.8 Flash Next's 4-bit file (119.6 GB).^[5][16]

How to run it

Not in standard llama.cpp or Ollama yet. Support is in llama.cpp pull request #30817, which was still an open draft when we checked on October 23, 2026.^[7] Unsloth says to use that PR or Unsloth Desktop.^[4] Ollama only lists cloud tags (deepseek-v4.1-flash:1017-cloud), which run on Ollama's servers, not your machine.^[8]

With the llama.cpp branch, download one quant folder (for example --include "*UD-Q2_K_XL*"), point --model at the first shard, and keep --load-mode mmap, --lazy-mode on and --override-tensor "per_layer_token_embd.weight=CPU" so the Engram table stays on SSD.^[5] DeepSeek recommends temperature 1.0 and top-p 1.0, and allowing at least 256K output tokens for long tasks.^[3][5]

An optional DSpark speculative-decoding drafter (7.98 GB at Q8_0) speeds up decoding by about 1.3–1.5×, according to Unsloth, but needs that much extra memory.^[5][6]

Rent a GPU for this model

If you don't own 128 GB or more, renting is the realistic way to try it. On-demand prices listed on Runpod's pricing page (checked October 23, 2026):^[15]

Prices checked on 2026-10-23. They change often, so confirm on the provider's page. Multi-GPU rows are Runpod's listed per-GPU price times the GPU count. 2× H200 (282 GB) holds UD-Q2_K_XL with Engram in system RAM; 4× H200 or 2× B300 can hold the whole UD-Q4_K_XL download in VRAM.

See all GPU rental prices for more providers and multi-GPU setups.

Where to rent or buy

Frequently asked questions

Can I run DeepSeek-V4.1-Flash-1017 on an RTX 4090 or RTX 5090?

Not on the GPU alone. Even the 1-bit build needs about 122.4 GB of RAM + VRAM with the Engram table on SSD, and these cards have 24 GB and 32 GB.^[5][9] With 128 GB of system RAM next to a 32 GB card, llama.cpp can split UD-IQ1_M (139.8 GB) across both, at much lower speed than an all-GPU setup.

How much RAM do I need for DeepSeek-V4.1-Flash-1017?

At least about 122 GB of RAM + VRAM for 1-bit, 200 GB for 2-bit (UD-Q2_K_XL), 233–282 GB for 3-bit and 298 GB for 4-bit, with the Engram table left on SSD.^[5] Unsloth adds 5–10 GB on top of these for runtime and context.^[5]

Can a Mac run DeepSeek-V4.1-Flash-1017?

Yes, with enough unified memory. Apple sells MacBook Pro with up to 128 GB and Mac Studio with 256 GB or 512 GB.^[13][14] A 128 GB Mac is tight with UD-IQ1_S, a 256 GB Mac Studio runs UD-Q2_K_XL, and a 512 GB Mac Studio runs UD-Q4_K_XL.^[5]

How much disk space does it need?

The full download of the quant you pick, from 233.0 GB (UD-IQ1_S) to 508.4 GB (UD-Q8_K_XL), on a fast NVMe SSD, because the Engram table is read from disk during generation.^[4][5]

Does the 1M-token context need a lot of memory?

No. At DeepSeek's 890 bytes per token, 1,048,576 tokens of KV cache is about 0.93 GB.^[2]

Can I run it with Ollama?

Not locally yet. Ollama's deepseek-v4.1-flash tags are cloud-only (checked October 23, 2026).^[8]

How we calculated

• Download sizes are the exact GGUF shard sizes on Hugging Face, added up per quant, checked October 23, 2026.^[4] The official checkpoint size is the sum of its 48 safetensors files.^[1]
• RAM + VRAM needed is the download minus the Engram table shard. Our numbers match Unsloth's published "Rest GB" figures.^[5][6]
• KV cache uses DeepSeek's published 890 bytes per token.^[2]
• Device memory is from NVIDIA and Apple spec pages. We compare in decimal GB, so the results lean conservative.
• Rental prices are the listed on-demand Runpod rates from our GPU price data, checked October 23, 2026. Multi-GPU prices are the per-GPU price times the count.^[15]
• Not included: engine working buffers, the OS and display, and the page cache that holds Engram rows. These vary, so leave headroom.

Sources

All sources checked on 2026-10-23.

1. Hugging Face, deepseek-ai/DeepSeek-V4.1-Flash-1017 (parameter count, license, repo date, checkpoint files). https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017
2. DeepSeek-V4.1-Flash model card (552B backbone, 196B Engram, 8B/16B activated, 890 bytes per token KV cache). https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash
3. DeepSeek-V4.1-Flash-1017 config.json and model card (1,048,576-token context, recommended settings). https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017/blob/main/config.json
4. Hugging Face, unsloth/DeepSeek-V4.1-Flash-1017-GGUF file list. https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF/tree/main
5. Unsloth, DeepSeek-V4.1-Flash: Run Locally Guide (memory requirements, Rest GB, quant accuracy, run flags). https://unsloth.ai/docs/models/deepseek-v4.1-flash
6. Unsloth, Engram tables and DSpark drafter READMEs. https://huggingface.co/unsloth/DeepSeek-V4.1-Flash-1017-GGUF/blob/main/engram/README.md
7. llama.cpp pull request #30817, "model : add DeepSeek-V4.1-Flash (deepseek41)". https://github.com/ggml-org/llama.cpp/pull/30817
8. Ollama, deepseek-v4.1-flash tags. https://ollama.com/library/deepseek-v4.1-flash/tags
9. NVIDIA, GeForce RTX 5090 specs (32 GB). https://www.nvidia.com/en-us/geforce/graphics-cards/50-series/rtx-5090/
10. NVIDIA, RTX PRO 6000 Blackwell (96 GB). https://www.nvidia.com/en-us/products/workstations/professional-desktop-gpus/rtx-pro-6000/
11. NVIDIA, DGX Spark (128 GB unified memory). https://www.nvidia.com/en-us/products/workstations/dgx-spark/
12. NVIDIA, H200 (141 GB). https://www.nvidia.com/en-us/data-center/h200/
13. Apple, Mac Studio tech specs (256 GB and 512 GB unified memory). https://www.apple.com/mac-studio/specs/
14. Apple, MacBook Pro tech specs (up to 128 GB unified memory). https://www.apple.com/macbook-pro/specs/
15. Runpod, GPU pricing. https://www.runpod.io/pricing
16. VRAMNeeded comparison table (sources listed per model). https://vramneeded.com/compare/
17. Seppe Gadeyne, "Run DeepSeek-V4.1-Flash (748B) locally with Project Maya on one RTX 5090", DEV Community, October 21, 2026. https://dev.to/seppegadeyne/run-deepseek-v41-flash-748b-locally-with-project-maya-on-one-rtx-5090-4i4a