Qwen 3 30B-A3B vs Qwen 3 32B — MoE speed vs dense quality at the same size
Chat + agents that prize throughput → 30B-A3B (MoE). Multi-step coding / reasoning where quality dominates → 32B (dense). Same VRAM, different speeds.
Qwen 3 30B-A3B
30B params · Apache 2.0 · qwen
Qwen 3 32B
32B params · Apache 2.0 · qwen
Who wins each use case
Each row is the dimension-weighted verdict for that use case. Use case weights live in src/lib/model-battle/comparator.ts and are public.
What hardware actually runs each model
VRAM math against the canonical hardware ladder. The largest context window that fits with headroom at Q4_K_M appears in each cell.
| Hardware tier | Qwen 3 30B-A3B | Qwen 3 32B |
|---|---|---|
RTX 3090 (24 GB) Used $700-1,000 — the local-AI workhorse | Q3 only, 2K ctx | Q3 only, 2K ctx |
RTX 4090 (24 GB) Used $1,400-1,900 — current consumer flagship | Q3 only, 2K ctx | Q3 only, 2K ctx |
RTX 5090 (32 GB) Retail $2,000-2,500 — Blackwell consumer | Q4 @ 4K, tight | Q4 @ 4K, tight |
Mac M4 Max (64 GB unified) $4,000-5,000 — Apple Silicon flagship | Q4 @ 16K, tight | Q4 @ 16K, tight |
Dual RTX 3090 (48 GB pooled) ~$1,500-2,000 — workstation budget build | Q4 @ 8K ctx | Q4 @ 8K, tight |
H100 (80 GB) $25K+ — datacenter / cloud rental tier | Q4 @ 16K ctx | Q4 @ 16K ctx |
On RTX 4090 @ Q4_K_M — bandwidth-derived estimate
Computed from each option's sustained TDP × predicted tok/s at $0.16/kWh. Cloud baseline: Claude Sonnet 4.6 (input + output).
Electricity-only cost — excludes the upfront hardware purchase, cooling, and amortized component depreciation. Hardware ROI math lives at /cost-vs-cloud; this line is for "is the marginal token cheaper than Claude?" not "should I buy this rig instead of paying Anthropic." MODELED ESTIMATE.
Same family, same release, two architectures. Qwen 3 30B-A3B is a Mixture-of-Experts model with ~3B active parameters per token — generates materially faster than the dense 32B because only a slice of the network fires per inference step. Qwen 3 32B is the dense version: every token uses every parameter.
Both need similar VRAM (the full model loads even when only some experts fire). The decision is throughput-vs-quality: MoE wins decisively on tokens-per-second; dense wins consistently on multi-step reasoning quality. For chat + simple agents, MoE. For complex coding + reasoning, dense.
The verdict for chat workloadsPick → Qwen 3 30B-A3B
clear edge for Qwen 3 30B-A3B — wins 2 of 10 dimensions (0 losses, 8 ties). Verdict reasoning below — no percentage shown on purpose (why).
Qwen 3 30B-A3B is the better fit for chat on the dimensions we score, taking 2 of 10 rows. The weighted score (30% vs 0%) reflects use-case priorities: quality (30%) + cost (20%) + speed (20%) anchor most of the call. Both models are worth running — this just tells you which one to reach for first.
| Dimension | Qwen 3 30B-A3B | Qwen 3 32B | Edge |
|---|---|---|---|
Editorial rating (1-10) Editor rating — single human assessment across reasoning, fluency, tool-use, instruction-following. | unrated | 8.9 | tie |
Parameters (B) | 30.0B | 32.0B | tie |
Context length (tokens) | 131K | 131K | tie |
License (commercial OK?) | ✓ Apache 2.0 | ✓ Apache 2.0 | tie |
Decode tok/s on NVIDIA GeForce RTX 4090 (Q4_K_M) Bandwidth-derived estimate. Smaller models stream faster on the same hardware. | 30.6 tok/s | 28.7 tok/s | Qwen |
Fits comfortably on NVIDIA GeForce RTX 4090? | ✕ 1.4 GB short | ✕ 3.0 GB short | Qwen |
Cost to run (local, Q4) Smaller model → less VRAM + less electricity per token. Cross-reference with /cost-vs-cloud for $-anchored math. | 18.1 GB at Q4_K_M | 19.3 GB at Q4_K_M | tie |
Community popularity Editorial popularity score — proxy for runtime support breadth + community recipe availability. | 94 | 92 | tie |
Multimodal support | text only | text only | tie |
Released | 2025-04-29 | 2025-04-29 | tie |
Which model wins on which VRAM tier. Picks update based on which one fits comfortably + which one’s strengths are unlocked by the available headroom.
| VRAM tier | Pick | Why |
|---|---|---|
| 16 GB | → Qwen 3 30B-A3B | Both tight at Q4. MoE's speed advantage matters more when you're already running at the edge of VRAM. |
| 24 GB | → Qwen 3 30B-A3B | Daily-driver: MoE wins on speed without a meaningful quality gap on chat workloads. |
| 32 GB+ | → Qwen 3 32B | With headroom, dense's quality advantage on reasoning + coding is the right pick. Load 30B-A3B as a sidecar for chat. |
Should I pick Qwen 3 30B-A3B (MoE) or Qwen 3 32B (dense)?
MoE for daily-driver chat where speed matters; dense for tasks where the model's full reasoning capacity is the bottleneck. The MoE version typically delivers materially higher tokens-per-second on the same hardware (specific multiplier depends on batch + runtime; measure on your stack). The dense version produces tighter outputs on multi-step tasks.
Do they use the same amount of VRAM?
Approximately yes — the full MoE network has to be loaded into memory even though only ~3B params fire per token. So both need ~18 GB at Q4_K_M weights. The MoE doesn't save VRAM; it saves compute (and therefore time).
Which runtimes support MoE properly?
vLLM and llama.cpp both handle MoE cleanly with recent builds. Ollama wraps llama.cpp but historically lags on MoE optimizations — check the Ollama release notes for explicit MoE mentions before assuming you'll see the throughput uplift.
Is there a quality gap?
Per Qwen's published benchmarks, the dense 32B leads on hard reasoning + math; the MoE 30B-A3B is close-but-slightly-behind on those, and roughly equal on chat + general knowledge tasks. The size of the gap is workload-dependent — A/B on your prompts.
Comparison data computed from live catalog rows + the model-battle comparator (src/lib/model-battle/comparator.ts). For arbitrary pairings outside this curated list, use /model-battle to pick any two models + your hardware.