❌ 超出单卡显存上限 (NEEDS 4x NVIDIA RTX 4070 Ti Super)
Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 4070 Ti Super (16GB VRAM)
You need 4x NVIDIA RTX 4070 Ti Super (64 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
63.7 GB / 16 GB
🟦 模型权重 (含词表层): 61.71 GB
🟪 KV Cache 缓存: 1.5 GB
🟨 引擎与算子工作区: 0.49 GB
所需显卡显存 (GPU VRAM)63.7 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 4070 Ti Super)
显存余量 / 溢出缺口-47.70 GB
预计生成速度 (Decode Speed)~7.3 tokens/sec
预计预填充速度 (Prefill Speed)~1333 tokens/sec
4K 提示词首字延迟 (TTFT)~3072 ms
Transformer 层数与注意力头MoE-GQA (48L, 8/40 KV/Q heads)
🚀 一键复制启动命令 (vLLM / llama.cpp / Ollama)
ollama run llama4:scout # Or llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-4-Scout-17B-16E-Instruct-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 (Llama-4-Scout-17B-16E (109B MoE): 63.7 GB in 4-bit)
Recommended Cloud Tier: NVIDIA A100 / H100 (80GB) · Full FP16 Needs: 205.47 GB
租卡对比买卡回本周期： ~32 months (vs $799 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: Llama-4-Scout-17B-16E (109B MoE) × NVIDIA RTX 4070 Ti Super
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA RTX 4070 Ti Super (16GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 203.03 GB | 1.5 GB | 205.02 GB | 显存不足 (+189.0GB) | ~1.8 / 1333 tok/s | 13x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 104.75 GB | 1.5 GB | 106.74 GB | 显存不足 (+90.7GB) | ~3.8 / 1333 tok/s | 7x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 107.86 GB | 1.5 GB | 109.85 GB | 显存不足 (+93.8GB) | ~3.7 / 1333 tok/s | 7x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 83.24 GB | 1.5 GB | 85.23 GB | 显存不足 (+69.2GB) | ~5 / 1333 tok/s | 6x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 72.41 GB | 1.5 GB | 74.4 GB | 显存不足 (+58.4GB) | ~5.9 / 1333 tok/s | 5x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 61.71 GB | 1.5 GB | 63.7 GB | 显存不足 (+47.7GB) | ~7.3 / 1333 tok/s | 4x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 54.23 GB | 1.5 GB | 56.22 GB | 显存不足 (+40.2GB) | ~8.6 / 1333 tok/s | 4x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 49.88 GB | 1.5 GB | 51.87 GB | 显存不足 (+35.9GB) | ~9.6 / 1333 tok/s | 4x |
在其他显卡上测试该模型
Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 5090 (32GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 4090 (24GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 5080 (16GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 4080 Super (16GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 3090 (24GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 4060 Ti 16GB (16GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 3060 12GB (12GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX 6000 Ada (48GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA RTX A6000 (48GB)Llama-4-Scout-17B-16E (109B MoE) on NVIDIA L40S 48GB (48GB)
该显卡可运行的其他热门大模型
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