DevPulse → Can I Run It? → Qwen 3 30B-A3B (MoE)

Can I Run

Qwen 3 30B-A3B (MoE)

Mixture-of-experts model: 30B total parameters, ~3B active per token. Trades VRAM for compute efficiency.

ChatCodeReasoningMultilingualTool UseMoE
30B
Parameters
3B
Active (MoE)
128K
Context
19.0 GB
RAM (Q4_K_M)

RAM by quantization

Lower quantization = less RAM but lower quality. Q4_K_M is the recommended sweet spot for most users.

FormatBitsRAMQualityVerdict
Q4_K_MREC419.0 GBGoodRuns OK
Q5_K_M522.0 GBGoodAfter cleanup
Q8_0833.0 GBExcellentTight fit

Which Mac can run Qwen 3 30B-A3B (MoE)?

Based on the recommended Q4_K_M quantization. You need RAM for both the model and your running apps — DevPulse calculates this for you. No CUDA installation. No driver hell. Just Apple Silicon doing what Jensen charges $30K for.

8 GB
Can’t run
16 GB
Can’t run
24 GB
Close apps first
~5 GB for apps
32 GB
Runs well
~13 GB for apps
36 GB
Runs well
~17 GB for apps
48 GB
Runs great
~29 GB for apps
64 GB
Runs great
~45 GB for apps
96 GB
Runs great
~77 GB for apps
128 GB
Runs great
~109 GB for apps
192 GB
Runs great
~173 GB for apps

Tips for running Qwen 3 30B-A3B (MoE)

1 MoE: VRAM holds all 30B but only 3B active per token — much faster than dense 30B

2 Q4_K_M fits on 32 GB Macs with most apps closed

3 Great quality-per-token-speed ratio for laptop inference

Skip the cloud GPU bill

Why pay $3/hr for an A100 when your Mac has unified memory sitting idle? DevPulse tells you if Qwen 3 30B-A3B (MoE) fits right now — factoring in Chrome, Docker, and everything else hogging your RAM.

Download Free

Model details

Provider
Alibaba
Architecture
moe
License
Apache 2.0
Commercial use
Yes
Min RAM
32 GB
Recommended
48 GB
Released
2025-04
Ollama
qwen3:30b-a3b
VRAM estimates from CanIRun.ai. Data sourced from llama.cpp, Ollama, and LM Studio.

Tokens per second on Apple Silicon

How fast will Qwen 3 30B-A3B (MoE) run on each chip?

Apple Silicon inference is bandwidth-bound — every generated token streams the model's active weights through unified memory once. Estimates are for single-batch generation at Q4_K_M (19.0 GB) at ~70% of peak bandwidth (typical llama.cpp / Ollama efficiency). Speculative decoding can lift these another 30-60%.

ChipBandwidthSmallest RAM that fitstok/s (est.)
M168 GB/s—won't fit
M2100 GB/s—won't fit
M3100 GB/s—won't fit
M4120 GB/s32 GB~4 tok/s
M2 Pro200 GB/s32 GB~7 tok/s
M3 Pro150 GB/s36 GB~6 tok/s
M4 Pro273 GB/s48 GB~10 tok/s
M2 Max400 GB/s32 GB~15 tok/s
M3 Max400 GB/s36 GB~15 tok/s
M4 Max546 GB/s36 GB~20 tok/s
M2 Ultra800 GB/s64 GB~29 tok/s
M3 Ultra819 GB/s96 GB~30 tok/s

“Smallest RAM that fits” assumes ~40% headroom for context, OS, and your dev stack. Reclaim VRAM before loading →

Running it offline

Local-AI guides for Qwen 3 30B-A3B (MoE).

Knowing the model fits is half the problem. The other half is keeping your Mac's unified memory free enough to actually load it, and keeping the load alive across a long session.

CLI for local AI agents →

Pre-flight checks, JSON output, exit codes for scripts. Built for Ollama, llama.cpp, LM Studio, and MLX.

Free VRAM before loading →

The 6-step protocol for clearing unified-memory headroom on Apple Silicon, from cheapest to deepest.

Fix Ollama OOM on Mac →

When Ollama says “out of memory” on a Mac with plenty of RAM. Diagnose and fix in one command.

Similar Models

Related Pages

Qwen 2.5 Coder 32B
Alibaba — 32B params
Qwen 3 32B
Alibaba — 32B params
DeepSeek R1 Distill 32B
DeepSeek — 32B params
Gemma 3 27B
Google — 27B params
Mistral Small 3.1 24B
Mistral AI — 24B params
Kimi VL A3B Thinking
Moonshot AI — 16B params

Run Qwen 3 30B-A3B (MoE) locally. No GPU required.

While cloud GPU prices keep climbing, your Mac can run Qwen 3 30B-A3B (MoE) for free. DevPulse tells you if it fits alongside your dev tools — before you download 19.0 GB of model weights.

Download for macOS

macOS 14+ · Apple Silicon & Intel · Free during launch