v2.2 · 2026-06-12
Qwen2.5 14B Instruct
qwen2-5-14b-instruct
Qwen2.5 14B Instruct is a 14.7B text chat model profile for local AI planning. This page records provider, license, context, quantization, hardware-fit estimates, setup hints, source links, and caveats so readers do not choose a model by name alone.
PROVIDER
Qwen
FAMILY
Qwen2.5
MODEL TYPE
text-chat
PARAMETERS
14.7B
MODALITIES
text
ARCHITECTURE
Decoder-only causal Transformer using Qwen2 architecture with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and grouped-query attention.
CONTEXT WINDOW
131,072 tokens
TRAINING TOKENS
18.0T
RELEASE DATE
2024-09-19
GGUF REPOSITORIES
Qwen/Qwen2.5-14B-Instruct-GGUF official
Official Qwen GGUF quantization repository with Q4_K_M, Q5_K_M, and Q8_0 file sizes listed on Hugging Face.
SETUP HINTS
OLLAMA
Use `ollama run qwen2.5:14b` for Ollama's packaged build, or `ollama run hf.co/Qwen/Qwen2.5-14B-Instruct-GGUF:Q4_K_M` to pin the official Hugging Face GGUF.LM STUDIO
Search for `Qwen/Qwen2.5-14B-Instruct-GGUF` in LM Studio and select Q4_K_M or Q5_K_M.
LLAMA.CPP
Use `llama-server -hf Qwen/Qwen2.5-14B-Instruct-GGUF:Q4_K_M`; enable YaRN only when you actually need context beyond 32K.QUANTIZATION FILE SIZES (GGUF)
Q4_K_M
8.99 GB
Q5_K_M
10.5 GB
Q8_0
15.7 GB
Quantization note: Q4_K_M for the default local catalog record; Q5_K_M if memory allows.
RAM / VRAM ESTIMATES
MIN RAM
11.2 GB
COMFORTABLE RAM
14.5 GB
MIN VRAM
10.8 GB
COMFORTABLE VRAM
13.7 GB
These are conservative local-inference estimates, not official hardware requirements.
HARDWARE FIT
CPU only (no GPU)
yes
8 GB RAM
not_recommended
16 GB RAM
good
32 GB RAM
good
8 GB VRAM
not_recommended
12 GB VRAM
usable
24 GB VRAM
good
Apple Silicon (unified memory)
good_16gb_plus
Hardware fit values are conservative local-inference estimates based on GGUF size plus runtime and KV-cache overhead. Actual requirements depend on context length, quantization, runtime, GPU offload, and other running apps.
BEST FOR
·Higher-quality general chat than 7B while remaining feasible on 16GB to 32GB systems.
·Reasonably strong writing, analysis, multilingual chat, and structured outputs.
·Local workflows where 7B is too weak but 32B is too heavy.
AVOID IF
·You need fast responses on low-end laptops.
·You need a specialist coding checkpoint.
·You plan to use very long contexts on 16GB RAM without careful KV-cache settings.
CAVEATS
·RAM and VRAM figures are estimates from GGUF file size plus conservative runtime and KV-cache overhead; exact memory depends on context length, batch size, backend, and GPU offload.
·Official Qwen2.5 model cards state 131,072-token context via YaRN, while many local GGUF/Ollama defaults expose 32K unless configured for longer context.
·Tool-use support was not treated as verified for this record because the official model card does not make a discrete tool-calling capability claim for this specific checkpoint.
SOURCE URLS
FIELD EVIDENCE
parameterSizeBhuggingface.co/Qwen/Qwen2.5-14B-Instruct
architecturehuggingface.co/Qwen/Qwen2.5-14B-Instruct
contextWindowTokenshuggingface.co/Qwen/Qwen2.5-14B-Instruct
trainingTokensqwenlm.github.io/blog/qwen2.5/
releaseDateqwenlm.github.io/blog/qwen2.5/
q4FileSizeGbhuggingface.co/Qwen/Qwen2.5-14B-Instruct-GGUF
q5FileSizeGbhuggingface.co/Qwen/Qwen2.5-14B-Instruct-GGUF
q8FileSizeGbhuggingface.co/Qwen/Qwen2.5-14B-Instruct-GGUF
LANGUAGES
ChineseEnglishFrenchSpanishPortugueseGermanItalianRussianJapaneseKoreanVietnameseThaiArabic
CAPABILITIES
No special capabilities flagged.