✅ 显存充足可流畅运行 (GGUF Q4_K_M / AWQ (4.83 bpw))
Llama-3.3-70B-Instruct on 2x NVIDIA RTX 3090 (48GB) (48GB VRAM)
Unquantized FP16 (134.86 GB) exceeds 48 GB, but 4-bit Q4_K_M requires 43.09 GB (90% VRAM utilization) at ~15.1 tokens/sec.
🎛️ 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
43.09 GB / 48 GB
🟦 模型权重 (含词表层): 40.09 GB
🟪 KV Cache 缓存: 2.5 GB
🟨 引擎与算子工作区: 0.5 GB
所需显卡显存 (GPU VRAM)43.09 GB
所需系统内存 (CPU 卸载)0.00 GB
当前硬件可用显存48 GB (1x 2x NVIDIA RTX 3090 (48GB))
显存余量 / 溢出缺口4.91 GB
预计生成速度 (Decode Speed)~15.1 tokens/sec
预计预填充速度 (Prefill Speed)~523 tokens/sec
4K 提示词首字延迟 (TTFT)~7827 ms
Transformer 层数与注意力头Dense-GQA (80L, 8/64 KV/Q heads)
🚀 一键复制启动命令 (vLLM / llama.cpp / Ollama)
ollama run llama3.3:70b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-3.3-70B-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-3.3-70B-Instruct: 43.09 GB in 4-bit)
Recommended Cloud Tier: L40S / RTX 6000 Ada (48GB) · Full FP16 Needs: 134.86 GB
租卡对比买卡回本周期： ~32 months (vs $1,500 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-3.3-70B-Instruct × 2x NVIDIA RTX 3090 (48GB)
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x 2x NVIDIA RTX 3090 (48GB) (48GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 131.41 GB | 2.5 GB | 134.41 GB | 显存不足 (+86.4GB) | ~1.2 / 523 tok/s | 3x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 67.82 GB | 2.5 GB | 70.82 GB | 显存不足 (+22.8GB) | ~2.1 / 523 tok/s | 2x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 69.81 GB | 2.5 GB | 72.81 GB | 显存不足 (+24.8GB) | ~2 / 523 tok/s | 2x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 53.88 GB | 2.5 GB | 56.88 GB | 显存不足 (+8.9GB) | ~4.4 / 523 tok/s | 2x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 46.95 GB | 2.5 GB | 49.94 GB | 显存不足 (+1.9GB) | ~9.3 / 523 tok/s | 2x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 40.09 GB | 2.5 GB | 43.09 GB | 显存紧凑 (余量 <12%) | ~15.1 / 523 tok/s | 1x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 35.21 GB | 2.5 GB | 38.21 GB | 支持 (80%) | ~17.2 / 523 tok/s | 1x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 32.38 GB | 2.5 GB | 35.38 GB | 支持 (74%) | ~18.7 / 523 tok/s | 1x |
在其他显卡上测试该模型
Llama-3.3-70B-Instruct on NVIDIA RTX 5090 (32GB)Llama-3.3-70B-Instruct on NVIDIA RTX 4090 (24GB)Llama-3.3-70B-Instruct on NVIDIA RTX 5080 (16GB)Llama-3.3-70B-Instruct on NVIDIA RTX 4080 Super (16GB)Llama-3.3-70B-Instruct on NVIDIA RTX 4070 Ti Super (16GB)Llama-3.3-70B-Instruct on NVIDIA RTX 3090 (24GB)Llama-3.3-70B-Instruct on NVIDIA RTX 4060 Ti 16GB (16GB)Llama-3.3-70B-Instruct on NVIDIA RTX 3060 12GB (12GB)Llama-3.3-70B-Instruct on NVIDIA RTX 6000 Ada (48GB)Llama-3.3-70B-Instruct on NVIDIA RTX A6000 (48GB)
该显卡可运行的其他热门大模型
DeepSeek-R1 671B (MoE) on 2x NVIDIA RTX 3090 (48GB)DeepSeek-V3-0324 671B (MoE) on 2x NVIDIA RTX 3090 (48GB)DeepSeek-R1-Distill-Llama-70B on 2x NVIDIA RTX 3090 (48GB)DeepSeek-R1-Distill-Qwen-32B on 2x NVIDIA RTX 3090 (48GB)DeepSeek-R1-Distill-Qwen-14B on 2x NVIDIA RTX 3090 (48GB)DeepSeek-R1-Distill-Llama-8B on 2x NVIDIA RTX 3090 (48GB)DeepSeek-R1-Distill-Qwen-7B on 2x NVIDIA RTX 3090 (48GB)Qwen3-235B-A22B (MoE) on 2x NVIDIA RTX 3090 (48GB)Qwen3-30B-A3B (MoE) on 2x NVIDIA RTX 3090 (48GB)Qwen3-32B on 2x NVIDIA RTX 3090 (48GB)