Alibaba Qwen 3 30B-A3B (MoE · 3B Active) VRAM Requirements Guide
Ultra-fast Mixture-of-Experts with only 3.3B active parameters per token. Delivers 100+ tok/s on 24GB GPUs and 40+ tok/s on Apple Silicon.
>98.2% retention. 18.0 GB weights, leaving +3.3 GB headroom on Dual NVIDIA RTX 3060 Rig (24GB VRAM).
Near-lossless 8-bit precision requiring 36.0 GB VRAM for benchmark evaluations.
Uncompressed FP16 weights taking 64.9 GB. Demands multi-GPU or datacenter accelerators.
Architectural Memory Breakdown & Attention Scaling
Qwen 3 30B-A3B is a sparse Mixture-of-Experts architecture containing 30.5B total parameters while activating just 3.3B parameters per token across 48 layers. With 4-KV-head GQA keeping 8k KV-cache under 0.75 GiB, total 4-bit Q4_K_M memory is 20.66 GB, fitting inside 24GB GPUs while delivering blistering 95+ tok/s generation throughput.
In llama.cpp, pass `-fa on -ctk q8_0 -ctv q8_0` to unlock 48k context inside 23.1 GB VRAM. When running on 16GB GPUs, pass `--n-cpu-moe 10` to offload cold expert weights to DDR5 RAM with minimal speed loss.
Dual NVIDIA RTX 3060 Rig (24GB VRAM) provides 24.0 GB physical VRAM (+3.3 GB operational margin at 8k context).
Run Alibaba Qwen 3 30B-A3B (MoE · 3B Active) Locally
ollama run qwen-3-30b-a3b:q4_K_Mvllm serve Alibaba Cloud/qwen-3-30b-a3b --max-model-len 8192 --gpu-memory-utilization 0.95Frequently Asked Questions
Why does Qwen 3 30B-A3B generate tokens so much faster than dense 32B models?
Because only 3.3B parameters compute per token, memory bandwidth demands drop by nearly 90%, streaming tokens at over 100 tok/s on an RTX 4090.
Does Qwen 3 30B-A3B fit on Apple Silicon Macs?
Yes! A 36GB M3/M4 Max MacBook Pro holds all 20.66 GB in unified Metal RAM and generates 65–85 tok/s silently on battery power.
Test Custom Hardware & Context Windows
Simulate Alibaba Qwen 3 30B-A3B (MoE · 3B Active) across all 31 GPUs, adjust context up to 131,072 tokens, or calculate RunPod break-even costs.
Open Alibaba Qwen 3 30B-A3B (MoE · 3B Active) in GPU Garden Matchmaker →