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v2.2 · 2026-06-12
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Qwen3 8B

qwen3-8b
REASONING

Qwen3 8B is a 8.2B reasoning 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
Qwen3
MODEL TYPE
reasoning
PARAMETERS
8.2B
MODALITIES
text
ARCHITECTURE
Decoder-only Qwen3 causal Transformer with grouped-query attention and a hybrid thinking/non-thinking chat template.
CONTEXT WINDOW
32,768 tokens
TRAINING TOKENS
36.0T
RELEASE DATE
2025-04-29
LICENSE & LINKS
GGUF REPOSITORIES
Official Qwen GGUF quantization repository with Q4_K_M, Q5_K_M, and Q8_0 sizes listed.
SETUP HINTS
OLLAMA
Use `ollama run qwen3:8b` for Ollama's packaged build, or `ollama run hf.co/Qwen/Qwen3-8B-GGUF:Q4_K_M` to pin the official Hugging Face GGUF.
LM STUDIO
Search for `Qwen/Qwen3-8B-GGUF` in LM Studio and select Q4_K_M or Q5_K_M.
LLAMA.CPP
Use `llama-server -hf Qwen/Qwen3-8B-GGUF:Q4_K_M`; use Qwen3 chat template settings and add `/think` or `/no_think` when you need to control thinking mode.
QUANTIZATION FILE SIZES (GGUF)
Q4_K_M
5.03 GB
Q5_K_M
5.85 GB
Q8_0
8.71 GB
Quantization note: Q4_K_M for practical local reasoning; Q5_K_M if memory allows.
RAM / VRAM ESTIMATES
MIN RAM
7 GB
COMFORTABLE RAM
9.8 GB
MIN VRAM
6.5 GB
COMFORTABLE VRAM
8.8 GB
These are conservative local-inference estimates, not official hardware requirements.
HARDWARE FIT
CPU only (no GPU)
yes
8 GB RAM
usable
16 GB RAM
good
32 GB RAM
good
8 GB VRAM
usable
12 GB VRAM
good
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
·Balanced local Qwen3 reasoning on 16GB RAM systems.
·Agent and tool-use experiments using the Qwen3 family.
·Math, coding, and logic prompts where 4B is too small.
AVOID IF
·You have only 8GB RAM and need comfortable performance.
·You need 32B-class quality.
·You need deterministic non-reasoning chat without thinking-mode management.
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.
·Qwen3 model cards state 32,768 native context and 131,072 tokens with YaRN; this record uses the native context as the primary contextWindowTokens value.
·Reasoning mode may emit thinking content unless disabled by the chat template or `/no_think` prompt convention.
SOURCE URLS
FIELD EVIDENCE
LANGUAGES
ChineseEnglishFrenchSpanishPortugueseGermanItalianRussianJapaneseKoreanVietnameseThaiArabic100+ languages and dialects
CAPABILITIES
tool callingreasoning tuned
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