Updated minutes ago
Qwen 3 30B-A3B
Alibaba · moe · 30.5B parameters · 131,072 context
Quality70.0
Architecture Details
TypeMOE
Total Parameters30.5B
Active Parameters3.3B
Layers48
Hidden Dimension2,048
Attention Heads32
KV Heads4
Head Dimension128
Vocab Size151,936
Total Experts128
Active Experts8
Memory Requirements
BF16 Weights
61.0 GB
FP8 Weights
30.5 GB
INT4 Weights
15.3 GB
KV-Cache per Token24576 bytes
Activation Estimate0.50 GB
Fits on (single-node)
B200 SXM BF16B100 SXM BF16GB200 NVL72 (per GPU) BF16GB300 NVL72 (per GPU) BF16H200 SXM BF16H100 SXM BF16H100 PCIe BF16H100 NVL BF16
GPU Recommendations
H200 SXMoptimal
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
1.1K tok/s
Cost/Month
$2553
Cost/M Tokens
$0.93
H100 SXMoptimal
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
1.1K tok/s
Cost/Month
$1794
Cost/M Tokens
$0.65
H100 PCIeoptimal
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
1.1K tok/s
Cost/Month
$1794
Cost/M Tokens
$0.65
API Pricing Comparison
No API pricing data available for this model.
Quality Benchmarks
MMLU75.0
HumanEval48.0
GSM8K80.0
MT-Bench78.0
Capabilities
Features
✓ Tool Use✗ Vision✓ Code✓ Math✓ Reasoning✓ Multilingual✓ Structured Output
Supported Frameworks
vllmsglangtgiollama
Supported Precisions
BF16 (default)FP8INT4