Qwen2.5-VL-32B-Instruct

Language Alibaba Qwen Partly verified
  • Open weights

Prices

1 offering · prices as listed
Alibaba Cloud Model Studio (opens in a new tab) Official China · CNY qwen2.5-vl-32b-instruct
Conditions/ 1M tokensInputOutput
Standard ¥8.00¥24.00

Prices from Token-based deployment, LoRA deployment, pay-as-you-go deployment-Alibaba Cloud Model Studio(Model Studio)-阿里云帮助中心 (opens in a new tab) (checked Oct 2, 2026)

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

Details

Reasoning
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Tool use
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Structured output
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Licence
apache-2.0
Family
qwen-vl
Model ID

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

Run locally

Weights: Qwen/Qwen2.5-VL-32B-Instruct
  • Parameters 33.5B
  • Dense
  • Native precision BF16
  • Layers 64
  • KV cache per token 256 KB
  • Config positions 125K (config.json max_position_embeddings)
  • API context 977K (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 IQ4_XS (automatic) 8K context 25–38 tok/sOne GPU
WeightsKV cacheOverheadUsable
Needs
≈ 19.4 GB (weights 16.6 + KV cache 2.0 + overhead 0.8)
Usable
21.6 GB (90% of its memory)
Generation speed
25–38 tok/s
Longest context
17K (longer doesn’t fit)
First token
about 1.08–1.62 s for a 2K prompt
PrecisionWeightsNeeds at 8KOn your hardwareLongest context
BF16 (native)maker’s file63.6 GB≈ 68.8Doesn’t fit—
Q8_033.1 GB≈ 36.8Needs system memory 2–4 tok/s—
FP831.2 GB≈ 34.7Needs system memory 3–4 tok/s—
Q6_K25.6 GB≈ 28.9Needs system memory 5–7 tok/s—
Q5_K_M22.2 GB≈ 25.3Needs system memory 8–12 tok/s—
Q5_021.4 GB≈ 24.5Needs system memory 9–13 tok/s—
Q4_K_M18.9 GB≈ 21.9Needs system memory 21–32 tok/s—
Q4_017.5 GB≈ 20.4Runs well 24–36 tok/s13K
INT4 (AWQ / GPTQ)maker’s file19.3 GB≈ 22.2Needs system memory 16–25 tok/s—
IQ4_XS16.6 GB≈ 19.4Runs well 25–38 tok/s17K
Q3_K_Mquality drops15.2 GB≈ 18.0Runs well 27–41 tok/s22K
IQ3_XXSquality drops11.9 GB≈ 14.5Runs well 34–51 tok/s36K
Q2_Kquality drops11.7 GB≈ 14.3Runs well 34–51 tok/s37K
IQ2_XXSquality drops8.0 GB≈ 10.5Runs well 47–70 tok/s52K

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 5090; unified memory Apple M5 Max (32-core GPU) · 36 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≈ 501.7 GBDoesn’t fit your hardware
LoRA≈ 66.1 GBDoesn’t fit your hardware
QLoRA≈ 21.3 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
  • LMArena Vision 1,152

    #73 of 89 listed models · CI 1,137–1,168 · 1,490 votes

  • LMArena (Arena), leaderboard-dataset on Hugging Face, licensed CC BY 4.0 · License · as of Oct 19, 2026
  • changes we made
  1. New language model

Sources

Alibaba Qwen’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

Alibaba Qwen · China · qwen.ai

Snapshots, the same family and other models from this provider