Model datasheet · Qwen3

Qwen3 30B-A3B

Superseded

Superseded by Qwen3.6 35B-A3B, the same 3B-active MoE design at a similar size, two generations on. Kept as the record of the model that made small-active MoE the default answer for fast local inference.

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)

Architecture, from its own config.json

48 layers · 48 full attention

32 attention heads over 4 KV heads (GQA 8:1), head dim 128 · mixture of experts, 3.3B of 30.5B active per token across 8 experts · vocabulary 151,936

Where the memory goes: weights by band above, plus a KV cache of 768MB at 8K · 3GB at 32K · 12GB at 128K, plus the runtime's own working memory — dominated by the output projection over that vocabulary.

Inside one layer, from its own tensors

48 × full-attention layer

The green line down the right of each half is the residual: it leaves the spine above the norm, skips the operation entirely and rejoins at the +. Where attention is drawn open, q, k and v are scaled to their real output widths — a model that shares key and value heads across many query heads shows one wide box beside two narrow ones.

Around the stack: an embedding of 151,936 × 2,048, a final norm, and a separate output projection.

On the norm names: despite what it is called, post_attention_layernorm is drawn after the residual add here, which is where this family applies it — it normalises the input to the feed-forward, not the output of attention. Models that carry a separate pre_feedforward_layernorm use it the other way, and are drawn that way.

§1 Characteristics by quantization band

Q2–Q3 — 2–3 bit

11GB weights · e.g. IQ3_XXS , Q3_K_M + KV: 768MB at 8K · 3GB at 32K · 12GB at 128K

Math & reasoning

4/10

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.

Coding

5/10

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.

Tool calling / agentic

4/10

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 128K

Math & reasoning

7/10

data 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.

Coding

7/10

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.

Tool calling / agentic

6/10

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.

Instruction following

7/10

data checked Aug 2026

quant-degradation-community — community consensus @ Q4_K_M · aggregated · 2026-08-30
Editorial derivation: IF not quant-sensitive.

Q6–Q8 — 6–8 bit

24GB weights · e.g. Q6_K + KV: 768MB at 8K · 3GB at 32K · 12GB at 128K

Math & reasoning

8/10

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.

Coding

7/10

data checked Aug 2026

quant-degradation-community — KLD delta vs bf16 @ Q6_K · aggregated · 2026-08-30
Editorial derivation: near-lossless at 6-bit.

Tool calling / agentic

7/10

data checked Aug 2026

quant-degradation-community — community consensus @ Q6_K · aggregated · 2026-08-30
Editorial derivation: near-lossless at 6-bit.

Instruction following

7/10

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 128K

Math & reasoning

8/10

data 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

Coding

7/10

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).

Tool calling / agentic

7/10

data checked Aug 2026

vendor-model-card — BFCL v3 (thinking mode) = 69.1 @ bf16 · vendor-reported · 2026-08-30
Non-thinking mode: 58.6.

Instruction following

7/10

data checked Oct 2026

vendor-model-card — IFEval strict prompt (thinking mode) = 86.5 @ bf16 · vendor-reported · 2026-08-30
livebench — LiveBench Instruction Following (category average, as displayed) = 83.2 · aggregated · 2026-10-04
LiveBench release 2025-04-25, row 'Qwen 3 30B A3B' (qwen3-30b-a3b-thinking in https://livebench.ai/table_2025_04_25.csv; per task paraphrase 82.8, simplify 84.65, story_generation 81.917, summarize 83.567), category figure as displayed on livebench.ai on 2026-10-04. Same release: Overall 66.9, Reasoning 71.3, Coding 47.5, Mathematics 76.6, Data Analysis 66.6, Language 56.2. Corrected 2026-10-04: this entry previously stored a 74.3 'global average (2024-11-25 question set)' taken from the llm-stats.com mirror; the model does not appear on livebench.ai's 2024-11-25 release at all, and the overall it shows on the releases that include it is 66.9 (2025-04-25) and 57.8 (2025-05-30, where agentic coding scores 1.7). Independent; thinking mode; quant not stated (presumed full precision). Not comparable with 2026-06-25 figures for newer models — the question set differs.

Long context

6/10

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

ISSUE-01 severe Q2–Q3 · math, tool-calling

≤3-bit quantization hits this MoE's expert weights hard: arithmetic becomes unreliable and tool calls produce malformed arguments.

Fix

Move up a quant band [strong]

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.

Fix

Code interpreter / calculator tool [partial]

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)

ISSUE-02 moderate Q2–Q3 / Q4–Q5 / Q6–Q8 / FP16 · creative-writing

Fast and smart, but its prose reads utilitarian — repetitive sentence rhythms and little voice.

No fix

No known fix — choose a different model

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)

ISSUE-03 mild Q2–Q3 / Q4–Q5 / Q6–Q8 / FP16 · instruction-following

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.

Fix

Prompt technique [strong]

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)