❌ 超出单卡显存上限 (NEEDS 9x NVIDIA RTX 5080)
Qwen3-235B-A22B (MoE) on NVIDIA RTX 5080 (16GB VRAM)
You need 9x NVIDIA RTX 5080 (144 GB total VRAM) for full GPU execution, or rent an A100/H100 80GB cloud instance.
🎛️ 交互式显存、上下文长度与 CPU 卸载调优台
📊 实时显存占用与推理吞吐遥测
134.4 GB / 16 GB
🟦 模型权重 (含词表层): 132.44 GB
🟪 KV Cache 缓存: 1.47 GB
🟨 引擎与算子工作区: 0.49 GB
所需显卡显存 (GPU VRAM)134.4 GB
所需系统内存 (CPU 卸载)0.00 GB
当前硬件可用显存16 GB (1x NVIDIA RTX 5080)
显存余量 / 溢出缺口-118.40 GB
预计生成速度 (Decode Speed)~4.9 tokens/sec
预计预填充速度 (Prefill Speed)~1320 tokens/sec
4K 提示词首字延迟 (TTFT)~3103 ms
Transformer 层数与注意力头MoE-GQA (94L, 4/64 KV/Q heads)
🚀 一键复制启动命令 (vLLM / llama.cpp / Ollama)
ollama run qwen3:235b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf Qwen/Qwen3-235B-A22B-GGUF -c 8192 -ngl 99 -fa
云显卡实时比价 & 租卡 vs 买卡回本计算器
本地显存不够？60 秒一键租用预装 vLLM/Ollama 的云端显卡 (Qwen3-235B-A22B (MoE): 134.4 GB in 4-bit)
Recommended Cloud Tier: Multi-GPU H100 / H200 Node · Full FP16 Needs: 440.31 GB
租卡对比买卡回本周期： ~32 months (vs $999 hardware)
| Cloud Provider | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Est. Monthly Cost (4h/day) | Free Credit / Trial | Deploy |
|---|---|---|---|---|---|---|
| RunPod | $0.44 / hr | $1.39 / hr | $2.39 / hr | $52.80 / mo | Instant PyTorch / vLLM Templates + Serverless GPU | 在 RunPod 一键部署 → |
| Vast.ai | $0.29 / hr | $0.95 / hr | $1.79 / hr | $34.80 / mo | Save 50–70% on RTX 4090 / 5090 / A100 rentals | 租用低价显卡 → |
| Vultr Cloud GPU | — | $1.79 / hr | $2.69 / hr | $214.80 / mo (A100) | $300 Free Credit | 领取 $300 免费算力 → |
| DigitalOcean GPU | — | — | $2.99 / hr | $358.80 / mo (H100) | $200 Free Credit (60d) | 领取 $200 试用金 → |
全量化精度档位显存与速度明细表: Qwen3-235B-A22B (MoE) × NVIDIA RTX 5080
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA RTX 5080 (16GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 437.91 GB | 1.47 GB | 439.87 GB | 显存不足 (+423.9GB) | ~1.4 / 1320 tok/s | 28x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 225.83 GB | 1.47 GB | 227.79 GB | 显存不足 (+211.8GB) | ~2.8 / 1320 tok/s | 15x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 232.64 GB | 1.47 GB | 234.6 GB | 显存不足 (+218.6GB) | ~2.7 / 1320 tok/s | 15x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 179.54 GB | 1.47 GB | 181.5 GB | 显存不足 (+165.5GB) | ~3.5 / 1320 tok/s | 12x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 155.86 GB | 1.47 GB | 157.81 GB | 显存不足 (+141.8GB) | ~4.1 / 1320 tok/s | 10x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 132.44 GB | 1.47 GB | 134.4 GB | 显存不足 (+118.4GB) | ~4.9 / 1320 tok/s | 9x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 116.5 GB | 1.47 GB | 118.46 GB | 显存不足 (+102.5GB) | ~5.7 / 1320 tok/s | 8x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 107.17 GB | 1.47 GB | 109.13 GB | 显存不足 (+93.1GB) | ~6.3 / 1320 tok/s | 7x |
在其他显卡上测试该模型
Qwen3-235B-A22B (MoE) on NVIDIA RTX 5090 (32GB)Qwen3-235B-A22B (MoE) on NVIDIA RTX 4090 (24GB)Qwen3-235B-A22B (MoE) on NVIDIA RTX 4080 Super (16GB)Qwen3-235B-A22B (MoE) on NVIDIA RTX 4070 Ti Super (16GB)Qwen3-235B-A22B (MoE) on NVIDIA RTX 3090 (24GB)Qwen3-235B-A22B (MoE) on NVIDIA RTX 4060 Ti 16GB (16GB)Qwen3-235B-A22B (MoE) on NVIDIA RTX 3060 12GB (12GB)Qwen3-235B-A22B (MoE) on NVIDIA RTX 6000 Ada (48GB)Qwen3-235B-A22B (MoE) on NVIDIA RTX A6000 (48GB)Qwen3-235B-A22B (MoE) on NVIDIA L40S 48GB (48GB)
该显卡可运行的其他热门大模型
DeepSeek-R1 671B (MoE) on NVIDIA RTX 5080DeepSeek-V3-0324 671B (MoE) on NVIDIA RTX 5080DeepSeek-R1-Distill-Llama-70B on NVIDIA RTX 5080DeepSeek-R1-Distill-Qwen-32B on NVIDIA RTX 5080DeepSeek-R1-Distill-Qwen-14B on NVIDIA RTX 5080DeepSeek-R1-Distill-Llama-8B on NVIDIA RTX 5080DeepSeek-R1-Distill-Qwen-7B on NVIDIA RTX 5080Qwen3-30B-A3B (MoE) on NVIDIA RTX 5080Qwen3-32B on NVIDIA RTX 5080Qwen3-14B on NVIDIA RTX 5080