Qwen2.5 14B

Qwen2.5 14B is Alibaba's mid-size open-weight model with 128K context and an Apache 2.0 license, and the absolute single-GPU sweet spot for local AI deployment in 2026. It packs near-frontier multilingual reasoning and mathematical processing into a footprint that fits comfortably on standard 16 GB VRAM graphics configurations — placing it well within reach of RTX 4080 and RTX 4090 users without requiring multi-GPU infrastructure. Trained on 18 trillion tokens with dedicated mathematical and coding focus, Qwen2.5 14B consistently benchmarks above its parameter count on multilingual reasoning, structured output, and agentic tool-use tasks. For Mac users, Q8_0 runs natively on any MacBook Pro M3 Max with 48 GB or more of unified memory.

Alibaba Qwen · Qwen

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedJune 2026SourcesExact model card, Qwen GitHub repository, Ollama Qwen 2.5 model library

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

Builders hosting local AI on a single RTX 4080 (16 GB) or RTX 4090 (24 GB) who want near-frontier multilingual reasoning, mathematical accuracy, and agentic coding performance without multi-GPU infrastructure. The sweet spot between consumer accessibility and frontier-class open-weight capability for 2026 local deployment.

Who should use it

Builders hosting local AI on a single RTX 4080 (16 GB) or RTX 4090 (24 GB) who want near-frontier multilingual reasoning, mathematical accuracy, and agentic coding performance without multi-GPU infrastructure. The sweet spot between consumer accessibility and frontier-class open-weight capability for 2026 local deployment.
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

Single-GPU mid-tier reasoning, multilingual chat, mathematical processing, coding, structured output generation, and agentic workflows on 12–24 GB VRAM consumer hardware
chat workflows
reasoning workflows
coding workflows
multilingual workflows

Deployment and hardware notes

14B parameters. Q4_K_M requires approximately 10 GB VRAM — minimum 12 GB GPU recommended, 16 GB preferred. Q8_0 requires approximately 16 GB VRAM. FP16 requires approximately 28 GB VRAM. MacBook Pro M3 Max (48 GB unified memory) runs Q8_0 comfortably in Ollama. CPU inference via llama.cpp possible with 32 GB system RAM but noticeably slower. Ollama tag: qwen2.5:14b.

License and usage notes

Apache 2.0. 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.
Builders hosting local AI on a single RTX 4080 (16 GB) or RTX 4090 (24 GB) who want near-frontier multilingual reasoning, mathematical accuracy, and agentic coding performance without multi-GPU infrastructure. The sweet spot between consumer accessibility and frontier-class open-weight capability for 2026 local deployment.
Start with `ollama run qwen2.5:14b` — Ollama handles the download and quantization selection automatically. On a 16 GB GPU, Q4_K_M runs with comfortable KV cache headroom for 128K-context sessions. On an RTX 4090 (24 GB) or MacBook Pro M3 Max (48 GB), use Q8_0 for near-reference output quality at only a modest latency cost. For a private browser-based interface, connect Open WebUI to the Ollama API at localhost:11434. For agentic coding workflows, wire the endpoint into Continue or Cline — at 14B, the model handles multi-file context, tool-call planning, and structured JSON output with notably more reliability than 7B alternatives.

Limitations

Q4_K_M is tight in 12 GB VRAM — 16 GB is the comfortable floor and recommended minimum for long-context sessions. Q8_0 requires 16–24 GB. For pure code-generation benchmarks, Qwen 2.5 Coder 14B edges ahead. Teams without a 16+ GB GPU should consider Qwen 2.5 7B instead.
14B parameters. Q4_K_M requires approximately 10 GB VRAM — minimum 12 GB GPU recommended, 16 GB preferred. Q8_0 requires approximately 16 GB VRAM. FP16 requires approximately 28 GB VRAM. MacBook Pro M3 Max (48 GB unified memory) runs Q8_0 comfortably in Ollama. CPU inference via llama.cpp possible with 32 GB system RAM but noticeably slower. Ollama tag: qwen2.5:14b.
Context window and limits: 128K tokens.
Verify the exact model card, provider docs, license, and serving support before production use.

Local workflow notes

Start with `ollama run qwen2.5:14b` — Ollama handles the download and quantization selection automatically. On a 16 GB GPU, Q4_K_M runs with comfortable KV cache headroom for 128K-context sessions. On an RTX 4090 (24 GB) or MacBook Pro M3 Max (48 GB), use Q8_0 for near-reference output quality at only a modest latency cost. For a private browser-based interface, connect Open WebUI to the Ollama API at localhost:11434. For agentic coding workflows, wire the endpoint into Continue or Cline — at 14B, the model handles multi-file context, tool-call planning, and structured JSON output with notably more reliability than 7B alternatives.

Local runtimes: Ollama (qwen2.5:14b), LM Studio, llama.cpp, Transformers

Platforms: Windows, macOS, Linux

Will Qwen2.5 14B run on your machine?

Qwen2.5 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

Quantization	Weights	Needs (with overhead)	Quality
Q4_K_M	9 GB	10.5 GB	good
Q8_0	16 GB	17.5 GB	high
FP16	28 GB	29.5 GB	reference

Fit on common hardware at Q4_K_M

Hardware	Memory the model can use	System RAM	Verdict
CPU Only	None (CPU only)	16 GB	CPU offload
RTX 4060 Laptop	8 GB	16 GB	CPU offload
RTX 3060 (12GB)	12 GB	32 GB	Tight
RTX 4060 Ti (16GB)	16 GB	32 GB	Comfortable
RTX 3090	24 GB	64 GB	Comfortable
Apple Silicon (Unified Memory) 36 GB	27 GB of 36 GB	36 GB	Comfortable

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 14B against your exact hardware How these verdicts are calculated

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