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Qwen2.5 Coder 32B Instruct AWQ

Runs as your own dedicated GPU deployment, ready to launch from the wizard. Data stays in Europe.

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings...

Start for free ← All models
Qwen/Qwen2.5-Coder-32B-Instruct-AWQ vLLM ready
text->text · Qwen · sovereign EU
Runs on EU infrastructure operated by European companies; US marketplace capacity is never part of this chain. Full chain
33B
Parameters
33K
Context window
24GB
Minimum VRAM
POST /api/v1/chat/completions On request

Specifications

Parameters 33B
Context window 32,768 tokens
Minimum VRAM 24 GB
Architecture Qwen2ForCausalLM (vLLM)
License apache-2.0
Modality text->text
Released November 2024
Publisher Qwen ↗

Pricing

Shared router · per token
On request
Not available on the shared router. Pricing on request as a dedicated GPU deployment.
Dedicated GPU · per hour
from €1,08 per hour
Your own vLLM instance on European cloud (24 GB VRAM), billed hourly.

Shared EU router, pay-per-token, scale-to-zero. Dedicated GPU deployments are billed hourly, see pricing.

✓ Verified working on 29-07-2026, responded in 79293 ms on our EU infrastructure.

Bench index

Bench is our quality index for every model, 0 to 100. It rests on public measurements (Epoch AI with about 40 benchmarks, LiveBench, LMArena and more) and is refreshed every morning. Where nobody measures, it shows an estimate with a range.

52.4 / 100 · Via LiveBench

Translated from its LiveBench result onto the same scale.

Range 46.0 to 58.7

Sources: Epoch AI ↗ · LiveBench ↗ · LMArena ↗ · Refreshed daily, last on 23-10-2026

LiveBench · 46.2
Reasoning 42.1
Coding 56.9
Mathematics 46.6
Data analysis 49.9
Language 23.3
Instruction following 58.7
LiveBench · All tasks
Reasoning
web of lies v2 70.0
zebra puzzle 24.3
spatial 32.0
Coding
LCB generation 57.7
coding completion 56.0
Mathematics
AMPS Hard 47.0
math comp 51.0
olympiad 41.8
Data analysis
cta 52.0
tablejoin 31.6
tablereformat 66.0
Language
connections 17.5
plot unscrambling 26.2
typos 26.0
Instruction following
paraphrase 56.3
simplify 60.9
story generation 63.2
summarize 54.4

Call it now

Drop-in replacement for OpenAI: change only the base URL and API key. The Anthropic format (/v1/messages) is supported too.

curl https://hostyourai.com/api/v1/chat/completions \
  -H "Authorization: Bearer hyai-..." \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen2.5-Coder-32B-Instruct-AWQ",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Frequently asked questions

Can I run Qwen2.5 Coder 32B Instruct AWQ in the EU?

Yes. HostYourAI runs Qwen2.5 Coder 32B Instruct AWQ on GPUs in European datacenters via vLLM. Prompts and outputs never leave the EU and there is no US cloud provider in the chain.

Is hosting Qwen2.5 Coder 32B Instruct AWQ GDPR-compliant?

Yes. All processing happens inside the EU, a Data Processing Agreement (DPA) is available and the subprocessor list is public. Open-source weights also mean: no training on your data.

How much does Qwen2.5 Coder 32B Instruct AWQ cost?

Qwen2.5 Coder 32B Instruct AWQ is available on request: through the shared EU router after a quick chat, or as your own dedicated GPU deployment billed per hour. Tell us your use case and we will set it up for you.

Is the API OpenAI-compatible?

Yes. You use the standard OpenAI SDKs with a custom base URL (https://hostyourai.com/api/v1). The Anthropic Messages API is supported as a drop-in as well.

More models from Qwen

Qwen Image 2.1 Turbo

Qwen-Image-2.1-Turbo is an accelerated checkpoint of Qwen-Image-2.1 for text-to-image generation and image editing with 8 denoising steps. It uses the same 7B visual generation architecture and loads directly with QwenImage21Pipeline in Diffusers.

7.1B View model →
Qwen Image 2.1 PE T2I

We are excited to open-source Qwen-Image-2.1, a unified text-to-image generation and image editing model in the Qwen family. With just 7B parameters in its visual generation component (32 Single-Stream DiT layers), Qwen-Image-2.1 balances generation quality, inference efficiency, and versatility.

9.4B 262K context View model →
Qwen Image 2.1

We are excited to open-source Qwen-Image-2.1, a unified text-to-image generation and image editing model in the Qwen family. With just 7B parameters in its visual generation component (32 Single-Stream DiT layers), Qwen-Image-2.1 balances generation quality, inference efficiency, and versatility.

7.1B View model →
Qwen3.8 Flash Next

[!Note] This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, TokenSpeed, etc.

180B 262K context View model →
Qwen3.8 27B

[!Note] This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, TokenSpeed, etc.

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Qwen3 ASR 0.6B hf

The Qwen3-ASR family includes Qwen3-ASR-1.7B and Qwen3-ASR-0.6B, which support language identification and ASR for 52 languages and dialects. Both leverage large-scale speech training data and the strong audio understanding capability of their foundation model, Qwen3-Omni. The 1.7B version achieves state-of-the-art performance among open-source ASR models and is competitive with the strongest proprietary commercial APIs.

0.8B 66K context View model →

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