RTX PRO 6000 MoE Benchmark: 50.5 tok/s Max

🔑 Enhanced Key Takeaways

• •RTX PRO 6000 Blackwell features 96GB GDDR7 VRAM with 1.792 TB/s bandwidth and 512-bit memory interface, enabling single-GPU handling of 70B FP8 models or high-concurrency 30B AWQ MoE workloads[1][2][4].
• •A single RTX PRO 6000 achieves ~8,400 tok/s on Qwen3-Coder-30B-AWQ at 400 concurrent requests, rivaling 4x RTX 4090 setups while using less power[2].
• •Blackwell architecture provides 125 TFLOPS FP32, 752 5th-gen Tensor Cores, and native FP4/NVFP4 support optimized for quantized LLM inference[1][4].
• •RTX PRO 6000 supports LoRA/QLoRA fine-tuning of 30B-40B models on a single GPU due to high VRAM and Tensor Core efficiency[2].

🛠️ Technical Deep Dive

• •Blackwell GPU (SM120) includes 24,064 CUDA cores, 752 5th-gen Tensor Cores (up to 4000 AI TOPS), 188 4th-gen RT Cores, PCIe 5.0 x16, and MIG partitioning for multi-instance workloads[4][5].
• •96GB GDDR7 ECC memory on 512-bit bus delivers 1.792 TB/s bandwidth, critical for KV cache in long-context (32k/64k) LLM serving with high batch sizes[1][2][4].
• •Native NVFP4/FP4 support in Tensor Cores enhances quantized inference efficiency for models like Qwen3-30B AWQ MoE, where active params (~3.3B) fit with 72GB KV headroom at 400 concurrency[1][2].

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline