Qwen3-30B-A3B (MoE) on NVIDIA RTX 6000 Ada (48GB VRAM)
Full FP16 (64.23 GB) exceeds 48 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 20.45 GB (19.05 GB weights + 0.75 GB KV cache at 8K context), delivering ~280 tokens/sec.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
20.45 GB / 48 GB🚀 Ready-to-Run Launch Command
ollama run qwen3:30b-a3b # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf Qwen/Qwen3-30B-A3B-GGUF -c 8192 -ngl 99 -fa
Need 20.45 GB+ VRAM to run Qwen3-30B-A3B (MoE)?
Recommended cloud setup for 4-bit: RTX 4090 (24GB) (or 64.23 GB for full FP16). Spin up a pre-configured vLLM / Ollama instance in 60 seconds:
| Provider | Best For | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Bonus / Trial | Action |
|---|---|---|---|---|---|---|
| RunPod | 1-Click vLLM / Ollama Pods | $0.44 / hr | $1.39 / hr | $2.39 / hr | Instant PyTorch / vLLM Pods + Serverless GPU | Deploy on RunPod → |
| Vast.ai | Cheapest Spot GPU Market | $0.29 / hr | $0.95 / hr | $1.79 / hr | Save 50–70% on RTX 4090 / 5090 / A100 rentals | Check Spot GPUs → |
| Vultr Cloud GPU | Enterprise SLA & Bare Metal | — | $1.79 / hr | $2.69 / hr | $300 Free Credit for New Users | Claim $300 Credit → |
| DigitalOcean GPU Droplets | Managed H100 Droplets | — | — | $2.99 / hr | $200 Free Trial (60 Days) | Get $200 Free → |
All Quantization Formats: Qwen3-30B-A3B (MoE) on NVIDIA RTX 6000 Ada
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x NVIDIA RTX 6000 Ada (48GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 62.83 GB | 0.75 GB | 64.23 GB | No (Over by 16.2GB) | ~11.6 tok/s | 2x NVIDIA RTX 6000 Ada |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 33.38 GB | 0.75 GB | 34.78 GB | Yes (72%) | ~169.8 tok/s | 1x NVIDIA RTX 6000 Ada |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 33.38 GB | 0.75 GB | 34.78 GB | Yes (72%) | ~169.8 tok/s | 1x NVIDIA RTX 6000 Ada |
| GGUF Q6_K (6-bit) | 6.56 bpw | 25.76 GB | 0.75 GB | 27.16 GB | Yes (57%) | ~220 tok/s | 1x NVIDIA RTX 6000 Ada |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 22.34 GB | 0.75 GB | 23.75 GB | Yes (49%) | ~253.6 tok/s | 1x NVIDIA RTX 6000 Ada |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 19.05 GB | 0.75 GB | 20.45 GB | Yes (43%) | ~280 tok/s | 1x NVIDIA RTX 6000 Ada |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 16.69 GB | 0.75 GB | 18.09 GB | Yes (38%) | ~280 tok/s | 1x NVIDIA RTX 6000 Ada |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 15.35 GB | 0.75 GB | 16.76 GB | Yes (35%) | ~280 tok/s | 1x NVIDIA RTX 6000 Ada |
Frequently Asked Questions (Qwen3-30B-A3B (MoE) + NVIDIA RTX 6000 Ada)
Can NVIDIA RTX 6000 Ada (48GB) run Qwen3-30B-A3B (MoE) locally?
Yes — NVIDIA RTX 6000 Ada (48GB) can run Qwen3-30B-A3B (MoE) using FP8 (W8A8 / E4M3). Full FP16 (64.23 GB) exceeds 48 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 20.45 GB (19.05 GB weights + 0.75 GB KV cache at 8K context), delivering ~280 tokens/sec.
How is KV Cache calculated for Qwen3-30B-A3B (MoE)?
Qwen3-30B-A3B (MoE) uses MoE-GQA with 48 transformer layers, 32 attention heads, and 4 KV heads (head dimension 128). At 8,192 tokens in FP16, KV cache consumes 0.75 GB. Enabling 8-bit KV cache (-ctk q8_0 -ctv q8_0 in llama.cpp or --kv-cache-dtype fp8 in vLLM) cuts this in half to 0.38 GB.