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Check exact model cardOpen weights where releasedUpdated August 2026

Qwen2.5 Coder 14B

Qwen2.5 Coder 14B is a coding-focused Qwen model for developer assistants, code review, and agentic coding workflows.

Alibaba Qwen · Qwen

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedAugust 2026SourcesOpenSourcesAI checker catalog + Hugging Face model card (Qwen/Qwen2.5-Coder-14B-Instruct), Hugging Face

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

Coding assistants, repository help, and developer workflow evaluation.

Who should use it

  • Coding assistants, repository help, and developer workflow evaluation.
  • Builders who want local or self-hosted testing options.
  • Developers evaluating coding assistant, repo-editing, and code review workflows.
  • Teams testing tool-use, agentic planning, and multi-step workflow behavior.

Common workflows

  • Coding assistants and software workflows
  • coding workflows
  • developer workflows
  • agents workflows
  • open weights workflows

Deployment and hardware notes

Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.

License and usage notes

Check exact model card. Open weights where released. Verify the exact model card and license terms for the checkpoint or hosted provider you use.

Strengths

  • Open weights where released model option for Qwen workflows.
  • Coding assistants, repository help, and developer workflow evaluation.
  • Use the exact checkpoint and quantization that matches your hardware and latency target.

Limitations

  • Verify license, deployment requirements, runtime support, and fit on your own workload before production use.
  • Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.
  • Context window and limits: 131,072 tokens.
  • Verify the exact model card, provider docs, license, and serving support before production use.

Local workflow notes

Use the exact checkpoint and quantization that matches your hardware and latency target.

Local runtimes: Ollama where supported, LM Studio where supported, llama.cpp where supported, Transformers

Platforms: Windows, macOS, Linux

Will Qwen2.5-Coder 14B run on your machine?

Qwen2.5-Coder 14B is 14B parameters and needs 10.5 GB of VRAM at Q4_K_M — 9 GB of weights plus 1.5 GB of runtime overhead for the inference server itself.

VRAM by quantization

QuantizationWeightsNeeds (with overhead)Quality
Q4_K_M9 GB10.5 GBgood
Q8_015.7 GB17.2 GBhigh
FP1629.6 GB31.1 GBreference

Fit on common hardware at Q4_K_M

HardwareMemory the model can useSystem RAMVerdict
CPU OnlyNone (CPU only)16 GBCPU offload
RTX 4060 Laptop8 GB16 GBCPU offload
RTX 3060 (12GB)12 GB32 GBTight
RTX 4060 Ti (16GB)16 GB32 GBComfortable
RTX 309024 GB64 GBComfortable
Apple Silicon (Unified Memory) 36 GB27 GB of 36 GB36 GBComfortable

Comfortable means VRAM clears the requirement by 2 GB or more. Tight means it covers the requirement with no margin. CPU offload means the model does not fit in VRAM but system RAM is at least 1.6× the weights, so it will run at reduced speed — expect roughly 1–5 tokens per second. Figures are weights plus a fixed runtime overhead and exclude KV-cache growth, which scales with context length.

Apple Silicon shares one pool of memory between the system and the GPU, so a model cannot use all of it. These rows apply the same 75% usable fraction the Compatibility Checker uses, which is why a 36 GB Mac is graded on less than 36 GB.

Check Qwen2.5-Coder 14B against your exact hardwareHow these verdicts are calculated

VRAM fit by quantization level

Enter your GPU VRAM below to see which quantization of Qwen2.5 Coder 14B fits and get the Ollama run command.

Sources to verify

OpenSourcesAI checker catalog + Hugging Face model card (Qwen/Qwen2.5-Coder-14B-Instruct)Hugging Face

Related resources

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

Qwen family hubOpen-source AI coding assistantsLocal coding assistant stackCursor vs Windsurf vs ClineAI tools directory
Hardware~9 GB at Q4_K_M (14B parameters)RuntimeOllama or LM Studio where supported, llama.cpp, Transformers, vLLMContext131,072 tokensLast updated2026
OpenSourcesAI checker catalog + Hugging Face model card (Qwen/Qwen2.5-Coder-14B-Instruct) →

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

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 Coder 32B →Qwen2.5 Coder 7B →Qwen2.5 Math 72B →Qwen2.5 32B →Qwen2.5 72B →Qwen3 4B →

Guides, stacks, and comparisons

Open-source AI coding assistants →Local coding assistant stack →Cursor vs Windsurf vs Cline →What is MCP? →MCP security basics →Best MCP servers for AI agents →

Related models

Qwen2.5 Coder 32BCoding assistants, repository help, and developer workflow evaluation.Qwen2.5 Coder 7BCoding assistants, repository help, and developer workflow evaluation.Qwen2.5 Math 72BMath-heavy prompts, reasoning tests, and educational workflow evaluation.Qwen2.5 32B32B open-weight model in the Qwen family.Qwen2.5 72B72B open-weight model in the Qwen family.Qwen3 4B4B open-weight model in the Qwen family.

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