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Alibaba CloudvsNVIDIA
Qwen3 30B A3B vs Nemotron 3.5 Lightning 30B A3B
Side-by-side technical showdown between Qwen3 30B A3B and Nemotron 3.5 Lightning 30B A3B on TheModelverse. Compare verified LLM benchmark scores, quantization compression, local GPU hardware sizing, and API inference pricing.
Executive Showdown & Winner Breakdown
Who Wins Where: Qwen3 30B A3B vs Nemotron 3.5 Lightning 30B A3B
Verified across benchmarks, pricing & local VRAM footprintVRAM Efficiency (INT4)
0 GB lighterLeader:Nemotron 3.5 Lightning 30B A3B(19.2 GB vs 19 GB)
Nemotron 3.5 Lightning 30B A3B requires substantially less memory to run at scale, fitting on more accessible GPU tiers.
Hardware Sizing & Model Compression
LLM Hardware Sizing & Quantization Sizing
Simulate weight compression levels and dynamic KV-cache expansion to verify whether these models fit on your local hardware or cloud GPU cluster.
Quantization Level
Active Precision
4 bits / parameter
VRAM Reduction
-72.5% vs FP16
Benchmark Retention
96–98% (Sweet Spot)
Context Simulator
Total VRAM Footprint @ 8k Context (INT4 (GGUF / AWQ))
Model 1Open Weights
Qwen3 30B A3B
19.2GB
Weights: ~16.5 GBKV Cache: ~0.2 GB+15% CUDA Buffer
Total Params:
30B
Active Compute:
Dense
Attention Scheme:
GQA (Grouped-Query)
Max Context:
131k tokens
Target Hardware: 1x RTX 3090 / 4090 (24GB) or Mac (32GB Unified)
Model 2Open Weights
Nemotron 3.5 Lightning 30B A3B
19GB
Weights: ~16.5 GBKV Cache: ~0 GB+15% CUDA Buffer
Total Params:
30B
Active Compute:
3B (Sparse MoE)
Attention Scheme:
GQA (Grouped-Query)
Max Context:
1000k tokens
Target Hardware: 1x RTX 3090 / 4090 (24GB) or Mac (32GB Unified)
GPU & Hardware Tier Compatibility Matrix
| Hardware Configuration | Available VRAM | Qwen3 30B A3B | Nemotron 3.5 Lightning 30B A3B |
|---|---|---|---|
16 GB VRAM RTX 4070 / 4080 (16GB), Mac M-Series (16GB) | 16 GB | ✕ OOM | ✕ OOM |
24 GB VRAM 1x RTX 3090 / 4090 (24GB), Mac M-Series (32GB) | 24 GB | Optimal | Optimal |
48 GB VRAM 2x RTX 4090 (TP=2), 1x L40S, Mac M-Series (64GB) | 48 GB | Optimal | Optimal |
80 GB VRAM 1x NVIDIA A100 / H100 (80GB), Mac Studio (128GB) | 80 GB | Optimal | Optimal |
160 GB Node 2x H100 (TP=2), 4x L40S, Mac Studio (192GB) | 160 GB | Optimal | Optimal |
Multi-Node Cluster 4x–8x H100 Datacenter Cluster | 320 GB | Optimal | Optimal |
Inference Economics & Cost Simulator
Token Pricing & Scale Economics
Calculate estimated monthly cloud API spend and evaluate the breakeven point vs self-hosted GPU hardware.
10k500k1M
| Pricing Metric | Qwen3 30B A3B | Nemotron 3.5 Lightning 30B A3B |
|---|---|---|
| Input Cost (/1M tokens) | Free / Open | Free / Open |
| Cached Input (/1M tokens) | — | — |
| Output Cost (/1M tokens) | Free / Open | Free / Open |
| Simulated Monthly Bill (100,000 calls) | $0 API Cost Open Weights | $0 API Cost Open Weights |
| Self-Hosted Breakeven vs $864/mo GPU | Self-Hostable Day 1 | Self-Hostable Day 1 |
Architecture Matrix
| Feature | Qwen3 30B A3B | Nemotron 3.5 Lightning 30B A3B |
|---|---|---|
| Release Date | 4/28/2025 | 8/11/2026 |
| Routing / MoE | Dense | Sparse MoE |
| Attention Mechanism | Standard / GQA | Standard / GQA |
| Source Type | Open Weights (Open) | Open Weights (Open) |
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