Large Language Models

Qwen 2.5

Alibaba's open-weight multilingual LLM family (0.5B to 72B) trained on 18T tokens with 128K context and strong structured output.

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Qwen 2.5
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GPU VRAM- & Kwantisatiecalculator

Qwen 2.5 GPU VRAM & Hardware-compatibiliteit

Calculate required VRAM (model weights + activation/KV cache) across BF16, FP8, INT4, and GGUF Q4_K_M.

Quantization Precision
Context / Image Resolution
Estimated Required VRAM
22.6 GB Apache 2.0 Commercial
Model Weights: 20.2 GB KV / Activation Overhead: 2.4 GB

Instant Hardware Compatibility

RTX 3060 12GB Insufficient VRAM
RTX 4070/4080 16GB Insufficient VRAM
RTX 4090 24GB Smooth Fit
Mac Apple Silicon 16GB/32GB/64GB Needs 32GB+ Unified
Cloud GPU / API Cloud / API Ready

Open-Weight & Cloud Variant Matrix

Model Variant License Speed / Steps Local vs API Commercial Use
Qwen 2.5 / Coder 7B Apache 2.0 90+ tok/s (RTX 4070 · 128K ctx) Local ~6.0 GB (Q4_K_M) / 16.5 GB (BF16) Yes — Free Apache 2.0 Commercial
Qwen 2.5 / Coder 32B Apache 2.0 38+ tok/s (RTX 4090 · Top Open Coding) Local ~22.6 GB (Q4_K_M) / Mac 32GB+ Yes — Free Apache 2.0 Commercial
Qwen 2.5 72B Qwen License 128K Context Flagship Multilingual Mac 64GB / 2×24GB GPU (INT4) or Cloud API Free Commercial (<100M MAU)

Over deze tool

Qwen 2.5 by Alibaba Cloud Qwen Team spans 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B parameter checkpoints pretrained on 18 trillion tokens, supporting 29+ languages, 128K context, and reliable JSON/tool-calling output.

Open-Weight Licensing & Hardware Fit

  • Apache 2.0 Commercial Freedom: All sizes from 0.5B through 32B (except 3B/72B under Qwen License, free commercial under 100M MAU) ship under Apache 2.0.
  • Local Deployment Sweet Spots: Run Qwen 2.5 7B in ~6 GB VRAM (Q4_K_M), 14B on 12–16 GB GPUs, 32B on a single 24 GB RTX 4090 or 32 GB Mac, and 72B on 64 GB Unified Memory or dual-GPU servers.
Naam van tool Qwen 2.5
Categorie Large Language Models
Prijsmodel Open Weights (Apache 2.0 on 0.5B–32B) / API
Onderwerptag Qwen 2.5, Open Source LLM, Ollama, vLLM, Multilingual AI
#Qwen 2.5, Open Source LLM, Ollama, vLLM, Multilingual AI

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Qwen 2.5

Veelgestelde vragen

How much GPU VRAM do Qwen 2.5 7B, 14B, 32B, and 72B require in Ollama (Q4_K_M)?

At 8K context in GGUF Q4_K_M, Qwen 2.5 7B needs ~6.0 GB (fits 8GB GPUs), 14B needs ~10.9 GB (fits RTX 3060 12GB), 32B needs ~22.6 GB (fits RTX 4090 24GB or 32GB Mac), and 72B needs ~49 GB (fits 64GB Apple Silicon or dual 24GB GPUs).

Which Qwen 2.5 sizes use the Apache 2.0 open-source license?

Qwen 2.5 0.5B, 1.5B, 7B, 14B, and 32B are released under the permissive Apache 2.0 license for unrestricted commercial use. Only the 3B and flagship 72B checkpoints use the Qwen License (free commercial use below 100M monthly active users).

What context window and multilingual capabilities does Qwen 2.5 support?

Qwen 2.5 supports up to 128K input context tokens (via YaRN scaling) and up to 8K output tokens, with top-tier fluency across 29+ languages including English, Chinese, Japanese, Korean, Spanish, French, German, and Arabic.

Why is Qwen 2.5 32B considered a sweet spot for self-hosted AI?

Qwen 2.5 32B outperforms many older 70B models on MMLU, math, structured JSON output, and tool calling while fitting onto a single 24GB consumer GPU in 4-bit quantization under Apache 2.0.

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