DeepSeek-R1-Distill-Qwen-14B

Language DeepSeek Partly verified Released Feb 5, 2025
  • In Input modalities: Text Out Output modalities: Text
  • Reasoning
  • Open weights

Prices

1 offering · prices as listed
Alibaba Cloud Model Studio (opens in a new tab) Official Available on this channel since Feb 5, 2025 China · CNY Context: 32.77KReasoning: YesTool use: —Structured output: —Max output: 16.38KInput modalities: TextOutput modalities: TextMax input: 32.77K deepseek-r1-distill-qwen-14b
Conditions/ 1M tokensInputOutput
Standard no longer listed since Oct 11, 2026 ¥1.00¥3.00

Prices from 阿里云百炼:模型价格 (opens in a new tab) (checked Oct 8, 2026)

Standard: the price a request with no conditions pays (the headline price elsewhere on the site).

Details

Tool use
—
Structured output
—
Family
deepseek-r-distill-qwen
Model ID

Not stated on DeepSeek’s pages: max input, reasoning efforts, reasoning can be off, max thinking budget, built-in tools, knowledge cutoff, aliases.

  • Parameters 14.8B
  • Dense
  • Native precision BF16
  • Layers 48
  • KV cache per token 192 KB
  • Config positions 128K (config.json max_position_embeddings)
  • API context 32K (maker’s API)
My hardware GeForce RTX 4090 24 GB · 1008 GB/s · 64 GB DDR5 dual channel · 89.6 GB/s
On your hardware · GeForce RTX 4090
Runs well Q8_0 (automatic) 8K context 29–44 tok/sOne GPU
WeightsKV cacheOverheadUsable
Needs
≈ 16.8 GB (weights 14.6 + KV cache 1.5 + overhead 0.7)
Usable
21.6 GB (90% of its memory)
Generation speed
29–44 tok/s
Longest context
32K (the model’s limit)
First token
about 0.48–0.72 s for a 2K prompt
PrecisionWeightsNeeds at 8KOn your hardwareLongest context
BF16 (native)maker’s file27.5 GB≈ 30.4Needs system memory 4–6 tok/s—
Q8_014.6 GB≈ 16.8Runs well 29–44 tok/s32K
FP813.8 GB≈ 15.9Runs well 31–46 tok/s32K
Q6_K11.3 GB≈ 13.4Runs well 37–55 tok/s32K
Q5_K_M9.8 GB≈ 11.8Runs well 42–62 tok/s32K
Q5_09.5 GB≈ 11.5Runs well 43–64 tok/s32K
Q4_K_M8.4 GB≈ 10.4Runs well 48–71 tok/s32K
Q4_07.7 GB≈ 9.7Runs well 51–76 tok/s32K
IQ4_XS7.3 GB≈ 9.3Runs well 53–80 tok/s32K
Q3_K_Mquality drops6.7 GB≈ 8.7Runs well 57–86 tok/s32K
IQ3_XXSquality drops5.3 GB≈ 7.3Runs well 69–104 tok/s32K
Q2_Kquality drops5.2 GB≈ 7.2Runs well 70–106 tok/s32K
IQ2_XXSquality drops3.5 GB≈ 5.5Runs well 93–140 tok/s32K

Click a row to choose a precision. “Maker’s file” sizes are the maker’s own files; others are parameters × bits per weight.

Compared with other hardware

Each at the highest precision it runs. Click one for details.

Smallest that runs it (Q4_K_M, 8K): GPU GeForce RTX 5070; unified memory Apple M5 · 16 GB.

Run commands

The maker publishes no files at this precision. Choose its native precision or one of its own builds, or quantise it yourself with llama.cpp.

Memory to fine-tune
Full≈ 223.0 GBDoesn’t fit your hardware
LoRA≈ 30.8 GBDoesn’t fit your hardware
QLoRA≈ 11.0 GBFits your hardware

Batch 1, 2,048-token sequences, gradient checkpointing; LoRA and QLoRA train rank-16 adapters on the attention projections.

Estimates; facts from the maker’s Hugging Face repositories, as of 2026-11-06. How it’s estimated

Cited scores

From third-party leaderboards, not measured by us
  • Epoch AI · GPQA Diamond 44.7%

    #141 of 168 listed models · ±2.86

  • Epoch AI · MATH Level 5 87.1%

    #17 of 64 listed models · ±0.75

  • Epoch AI · OTIS Mock AIME 2024–2025 50.6%

    #112 of 159 listed models · ±5.98

  • Epoch AI, 'Capabilities & benchmarking'. Published online at epoch.ai. Retrieved from 'https://epoch.ai/benchmarks'. Licensed CC BY 4.0. · License · as of Oct 18 – Nov 4, 2026
  • changes we made
  1. Now priced on Alibaba Cloud Model Studio, China Input ¥1.00 · Output ¥3.00
  2. Context window 128K → 32.77K

Sources

DeepSeek’s official policies: data use, privacy, terms →

Some values on this page are still waiting to be checked against official pages: a filled dot is verified, a hollow one is not yet (with or without a source on file). How we verify data

DeepSeek · China · deepseek.com

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