Large Language Models
Alibaba's open-weight multilingual LLM family (0.5B to 72B) trained on 18T tokens with 128K context and strong structured output.
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.
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.| 도구 이름 | Qwen 2.5 |
|---|---|
| 카테고리 | Large Language Models |
| 요금제 | Open Weights (Apache 2.0 on 0.5B–32B) / API |
| 주제 태그 | Qwen 2.5, Open Source LLM, Ollama, vLLM, Multilingual AI |
이 도구의 트래픽, 순위 및 참여도 지표입니다.
아직 분석 데이터가 없습니다
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).
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).
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.
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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