Model datasheet · Qwen3
Qwen3 30B-A3B
A mixture-of-experts model: 30B of knowledge, but only ~3B active per token, so it runs fast on machines that can hold it. The best quality-per-second story on 32GB Macs.
- Vendor
- Alibaba
- Architecture
- MoE · 30.5B total / ~3.3B active
- Context
- 131,072 tokens
- License
- Apache-2.0
- Released
- 2025-04-29
- Vision
- No
→ GGUF (llama.cpp / LM Studio / Ollama) → MLX (Apple Silicon)
§1 Characteristics by quantization band
data checked Aug 2026
- quant-degradation-community — degradation reports @ Q3_K_M · aggregated · 2026-08-30
Editorial derivation: ≤3-bit hits MoE expert weights hard; math cliff applies.
data checked Aug 2026
- quant-degradation-community — degradation reports @ Q3_K_M · aggregated · 2026-08-30
Editorial derivation: usable but error-prone at 3-bit.
data checked Aug 2026
- quant-degradation-community — community consensus @ Q3_K_M · aggregated · 2026-08-30
Editorial derivation: malformed tool arguments become common at ≤3-bit.
Q4–Q5 — 4–5 bit
17GB weights · e.g. Q4_K_M , Q5_K_M , mlx-4bit + KV: 768MB at 8K · 3GB at 32K · 12GB at 128Kdata checked Aug 2026
- quant-degradation-community — KLD delta vs bf16 @ Q4_K_M · aggregated · 2026-08-30
Editorial derivation: MoE models tolerate 4-bit well; small loss vs full band.
data checked Aug 2026
- quant-degradation-community — KLD delta vs bf16 @ Q4_K_M · aggregated · 2026-08-30
Editorial derivation: small loss vs full band at Q4_K_M.
data checked Aug 2026
- quant-degradation-community — community consensus @ Q4_K_M · aggregated · 2026-08-30
Editorial derivation: occasional argument-format slips at 4-bit.
data checked Aug 2026
- quant-degradation-community — community consensus @ Q4_K_M · aggregated · 2026-08-30
Editorial derivation: IF not quant-sensitive.
data checked Aug 2026
- quant-degradation-community — KLD delta vs bf16 @ Q6_K · aggregated · 2026-08-30
Editorial derivation: Q6_K is near-lossless; carried from full band.
data checked Aug 2026
- quant-degradation-community — KLD delta vs bf16 @ Q6_K · aggregated · 2026-08-30
Editorial derivation: near-lossless at 6-bit.
data checked Aug 2026
- quant-degradation-community — community consensus @ Q6_K · aggregated · 2026-08-30
Editorial derivation: near-lossless at 6-bit.
data checked Aug 2026
- quant-degradation-community — community consensus @ Q6_K · aggregated · 2026-08-30
Editorial derivation: near-lossless at 6-bit.
FP16 — full precision (fp16/bf16)
61GB weights · e.g. bf16 + KV: 768MB at 8K · 3GB at 32K · 12GB at 128Kdata checked Aug 2026
- vendor-model-card — AIME'24 (thinking mode) = 80.4 @ bf16 · vendor-reported · 2026-08-30
Qwen3 Technical Report Table 15. Non-thinking mode drops to 32.8.
- vendor-model-card — MATH-500 (thinking mode) = 98 @ bf16 · vendor-reported · 2026-08-30
data checked Aug 2026
- vendor-model-card — LiveCodeBench v5 (thinking mode) = 62.6 @ bf16 · vendor-reported · 2026-08-30
Non-thinking mode: 29.8. Model absent from the Aider polyglot leaderboard as of 2026-08-30 (Qwen3 32B scores 40.0% there for reference).
data checked Aug 2026
- vendor-model-card — BFCL v3 (thinking mode) = 69.1 @ bf16 · vendor-reported · 2026-08-30
Non-thinking mode: 58.6.
data checked Aug 2026
- vendor-model-card — IFEval strict prompt (thinking mode) = 86.5 @ bf16 · vendor-reported · 2026-08-30
- livebench — global average (2024-11-25 question set) = 74.3 @ bf16 · aggregated · 2026-08-30
Cross-domain global average (rank #17 on the public leaderboard, retrieved via the llm-stats.com mirror); independently corroborates the vendor-reported numbers.
data checked Aug 2026
- vendor-model-card — RULER @128K = 79.2 @ bf16 · vendor-reported · 2026-08-30
RULER @64K: 89.1, @32K: 92.4.
§2 Known issues & what fixes them
≤3-bit quantization hits this MoE's expert weights hard: arithmetic becomes unreliable and tool calls produce malformed arguments.
Q4_K_M (~19GB) is the sweet spot; if you only have ~13GB free, a smaller dense model at 4-bit usually beats this model at 3-bit.
Offload math to a tool — works because tool calling recovers at 4-bit+, but at 3-bit the tool calls themselves get flaky.
Evidence · 2 sources · community-consensus
- llama.cpp quantization quality discussions (MoE quant sensitivity) (github, 2026-08-30)
- r/LocalLLaMA threads on Qwen3-30B-A3B quant choice (reddit, 2026-08-30)
Fast and smart, but its prose reads utilitarian — repetitive sentence rhythms and little voice.
Model character, not a settings problem. Gemma 3 12B writes better in half the memory.
Evidence · 1 source · anecdotal
- r/LocalLLaMA model-recommendation threads for writing (reddit, 2026-08-30)
Qwen3 models fall into endless repetition loops under greedy decoding — the model card itself warns 'DO NOT use greedy decoding'. Quantized builds hit it more often.
Use the card's sampler settings (temp 0.6 / top_p 0.95 / top_k 20 in thinking mode; 0.7 / 0.8 / 20 non-thinking) and presence_penalty up to 1.5 if loops persist.
Evidence · 2 sources · community-consensus
- Qwen3 model card (greedy-decoding warning + recommended samplers) (vendor, 2026-08-30)
- llama.cpp issue: Qwen3 GGUF repetition reports (github, 2026-08-30)