BBrainOutput
Alibaba·2 sizes·General LLM / Coding LLM

Qwen2.5 models: sizes & hardware to run them

The Qwen2.5 family spans 2 sizes from 7.6B to 7.6B. Each size maps to a different hardware tier — below is the approximate memory each needs at 4-bit and the device we’d start with for a private local deployment.

CodeLong context

Sizes & hardware

ModelParamsContext~VRAM @ 4-bitMinimum deviceRecommended
Qwen2.5 7B Instruct7.6B33K~4.9GBNVIDIA GeForce RTX 3060 12GBNVIDIA B200 (placeholder)
Qwen2.5 Coder 7B Instruct7.6B131K~4.9GBNVIDIA GeForce RTX 3060 12GBNVIDIA B200 (placeholder)

Memory figures are approximate working-set estimates (weights + KV cache at modest context); treat as ±. Device picks come from our compatibility engine, best on-prem fit first.

Open each size

General LLM
Qwen2.5 7B Instruct
Roughly 5 GB of memory to run at Q4_K_M (estimated). Larger quantizations need proportionally more.
Coding LLM
Qwen2.5 Coder 7B Instruct
Roughly 5 GB of memory to run at Q4_K_M (estimated). Larger quantizations need proportionally more.

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