DeepSeek V4.1 Flash 1017 VRAM requirements

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

DeepSeek V4.1 Flash 1017 needs 474 GB at Q4_K_M, 832 GB at Q8_0 and 1,565 GB at FP16 with 8,192 tokens of context and one request. Each total below is the weights plus the FP16 KV cache and the runtime overhead (0.5 GB plus 10%), and each row opens the calculator with that setting. What the GGUF names mean.

Fine-tuning DeepSeek V4.1 Flash 1017? DeepSeek V4.1 Flash 1017 VRAM for LoRA, QLoRA and full training.

DeepSeek V4.1 Flash 1017: what changed and how to size it

DeepSeek calls the October 17 checkpoint a minor update to V4.1 Flash. Its config.json, safetensors parameter counts and total weight-file size match the original release, so the same memory assumptions apply. The publisher reports Terminal-Bench 4.0 increasing from 31.2 to 42.3 and DeepSWE v1.1 from 74.2 to 77.0; these are its benchmark results, not ModelVRAM GPU measurements.

The published weight files total 510,296,708,312 bytes (475 GB). The quantization configuration specifies FP8 with FP4 experts, so a flat eight-bit multiplication of the 763.2B safetensors count overstates this package’s download size. All parameters are counted in the alternative-precision estimates; 16B active per decode token is a computation figure, not the total weight footprint.

The original model card describes a 20-layer causal encoder followed by a 20-layer decoder, with Engram lookup memory and DSpark speculative decoding. The released configuration combines a 128-token sliding window with shared, compressed global caches. The tables and calculator use a conservative independent-layer cache proxy, not a measured implementation of that layout; image-token processing and speculative-decoding buffers need additional runtime memory.

Adjust the published checkpoint’s precision, context and parallel requests

The release has no Jinja chat template. Use its reference prompt encoder or the linked protocol tooling. The minimal inference guide first converts Hugging Face weights into tensor-parallel rank files. From that release’s inference directory:

python convert.py --hf-ckpt-path /models/DeepSeek-V4.1-Flash-1017 --save-path /models/DeepSeek-V4.1-Flash-1017-TP8 --model-parallel 8 --expert-dtype fp4 --tokenizer-path /models/DeepSeek-V4.1-Flash-1017

Eight tensor-parallel ranks follow the reference conversion example; they are not a measured minimum GPU count. Read the inference guide for generation and multi-node commands. This checkpoint and its reference code use the MIT License. ModelVRAM has not run this model on a GPU.

DeepSeek V4.1 Flash 1017 at 8K, 32K, 128K and 1M (full) tokens of context

The tables use an independent-layer upper-bound proxy for its 40-layer shared, compressed cache. Each extra token of context adds 80 KB of FP16 cache per request. The last column is the smallest setup here, one card or a group, that holds it at Q4_K_M. How the KV cache works.

How fast DeepSeek V4.1 Flash 1017 writes on a multi-GPU group

Tokens per second for one request with 8,192 tokens of context, estimated from memory bandwidth and the parameters read per token. No single card or machine holds it at Q4_K_M or FP8 (the precisions below), so these rows are the GPU groups here that do, in tensor parallel with their combined bandwidth. A dash means it does not fit at that precision. Try other settings in the speed calculator.

Longest context on a multi-GPU group

No single card or machine holds it at Q4_K_M, Q8_0 or FP8 (the precisions below), so these rows use GPU groups: how many tokens of context DeepSeek V4.1 Flash 1017 fits on each, in one tensor-parallel group with one request, an FP16 KV cache and 0.5 GB left free. "Full" means the model's whole context window fits.

Model details

Parameters

763.2B (763,205,315,794); the full safetensors count; DeepSeek lists a 552B backbone plus 196B Engram memory, with 8B active during prefill and 16B during decode

Experts

384 routed experts, all loaded

Active per token

16B

Layers

The tables use an independent-layer upper-bound proxy for its 40-layer shared, compressed cache

Attention cache

1 KV heads × 512

Context length

1,048,576 tokens

Published weights

475 GB (5.3 bits/weight)

On Hugging Face

deepseek-ai/DeepSeek-V4.1-Flash-1017

Reported benchmarks

None published in the model card for GPQA, MMLU-Pro or LiveCodeBench.

DeepSeek V4.1 Flash 1017 next to similar-sized models

The three models here closest to it in parameter count, at Q4_K_M with 8,192 tokens of context.