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Qwen License (permissive; check model card for commercial use terms)Open weightsUpdated June 2026

Qwen2.5 72B Instruct

Qwen2.5 72B Instruct is Alibaba's flagship open-weight 72B general assistant model with 128K context. Strong multilingual performance, coding, and reasoning — competitive with frontier closed models on benchmarks.

Alibaba Qwen · Qwen

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedJune 2026SourcesAlibaba Qwen 2.5 72B Instruct model page

Model checkpoints, context windows, provider support, local runtime compatibility, and license terms can change quickly. Verify the exact model card before production or commercial use.

Best for

Teams requiring frontier-class open-weight reasoning and multilingual performance with local control. Practical for multi-GPU setups (2×RTX 3090/4090) or Mac Studio M2/M3 Ultra with 192 GB unified memory.

Who should use it

  • Teams requiring frontier-class open-weight reasoning and multilingual performance with local control. Practical for multi-GPU setups (2×RTX 3090/4090) or Mac Studio M2/M3 Ultra with 192 GB unified memory.
  • Builders who want local or self-hosted testing options.
  • Developers evaluating coding assistant, repo-editing, and code review workflows.

Common workflows

  • Multilingual reasoning, long-context chat, coding, and frontier-class open-weight evaluation
  • chat workflows
  • multilingual workflows
  • reasoning workflows
  • coding workflows

Deployment and hardware notes

72B parameters. Q4_K_M requires approximately 45 GB VRAM — needs multi-GPU (2×RTX 3090 = 48 GB, 2×RTX 4090 = 48 GB) or Mac Studio/Pro with 64 GB+ unified memory. Q8_0 requires approximately 80 GB VRAM. FP16 requires approximately 144 GB VRAM. Ollama tag: qwen2.5:72b.

License and usage notes

Qwen License (permissive; check model card for commercial use terms). Open weights. Verify the exact model card and license terms for the checkpoint or hosted provider you use.

Strengths

  • Open weights model option for Qwen workflows.
  • Teams requiring frontier-class open-weight reasoning and multilingual performance with local control. Practical for multi-GPU setups (2×RTX 3090/4090) or Mac Studio M2/M3 Ultra with 192 GB unified memory.
  • Multi-GPU required for CUDA local inference at Q4. Mac Studio M2 Ultra (192 GB) handles Q4 well in Ollama. Use vLLM tensor parallelism for production multi-GPU serving.

Limitations

  • 72B scale requires multi-GPU workstations or high-memory Apple Silicon for local inference. Cloud hosted inference (Together AI, Fireworks, Groq) is the most practical path for most teams.
  • 72B parameters. Q4_K_M requires approximately 45 GB VRAM — needs multi-GPU (2×RTX 3090 = 48 GB, 2×RTX 4090 = 48 GB) or Mac Studio/Pro with 64 GB+ unified memory. Q8_0 requires approximately 80 GB VRAM. FP16 requires approximately 144 GB VRAM. Ollama tag: qwen2.5:72b.
  • Context window and limits: 128K tokens.
  • Verify the exact model card, provider docs, license, and serving support before production use.

Local workflow notes

Multi-GPU required for CUDA local inference at Q4. Mac Studio M2 Ultra (192 GB) handles Q4 well in Ollama. Use vLLM tensor parallelism for production multi-GPU serving.

Local runtimes: vLLM (multi-GPU recommended), llama.cpp (Q4 CPU or Mac), Ollama (qwen2.5:72b), Transformers

Platforms: Windows, macOS, Linux

Sources to verify

Alibaba Qwen 2.5 72B Instruct model page

Related resources

Continue with model source notes, local tools, and implementation guides related to this model.

Qwen family hubBest local LLM toolsPrivate local chatbot stackOllama + Open WebUI starter stackAI tools directory
Hardware45 GB (Q4_K_M, multi-GPU required)RuntimevLLM (multi-GPU), llama.cpp (CPU/Mac), Ollama (qwen2.5:72b), hosted endpointsContext128K tokensLast updated2026
Alibaba Qwen 2.5 72B Instruct model page →

Model ecosystem connections

Use these next-step links to move from this profile into related tools, comparisons, guides, stacks, and curated shortlists.

Recommended runtimes and tools

Ollama →llama.cpp →Transformers →vLLM →LM Studio →

Setup and deployment

Check if this model fits your GPU →What can 12GB VRAM run? →What is quantization? GGUF, Q4, Q8 explained →What is VRAM and how much do you need? →

Related model pages

Qwen family hub →Qwen2.5 7B →Qwen2.5 Coder 14B →Qwen2.5 Coder 32B →Qwen3 14B →Qwen2.5 32B →Qwen2.5 72B →

Guides, stacks, and comparisons

Best local LLM tools →Private local chatbot stack →Ollama + Open WebUI starter stack →Open-source AI coding assistants →Local coding assistant stack →Cursor vs Windsurf vs Cline →

Related models

Qwen2.5 7BDevelopers running local AI on 6–8 GB VRAM consumer GPUs who need a fast, highly capable 7B model for code completion, agentic function calling, and structured JSON output generation. The recommended 7B starting point for any local inference pipeline in 2026 before stepping to 14B or 72B.Qwen2.5 Coder 14BCoding assistants, repository help, and developer workflow evaluation.Qwen2.5 Coder 32BCoding assistants, repository help, and developer workflow evaluation.Qwen3 14BMultilingual chat, assistant workflows, and Qwen-family comparisons.Qwen2.5 32B32B open-weight model in the Qwen family.Qwen2.5 72B72B open-weight model in the Qwen family.

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