✅ 完美运行原生 FP16 与全量化格式
Qwen2.5-7B-Instruct on NVIDIA H200 141GB SXM (141GB VRAM)
In 4-bit GGUF Q4_K_M, Qwen2.5-7B-Instruct consumes 5.43 GB (4.5 GB weights + 0.44 GB KV cache at 8K context), achieving ~350 tokens/sec decode and ~18000 tokens/sec prefill.
🎛️ 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
5.43 GB / 141 GB
🟦 模型权重 (含词表层): 4.5 GB
🟪 KV Cache 缓存: 0.44 GB
🟨 引擎与算子工作区: 0.49 GB
所需显卡显存 (GPU VRAM)5.43 GB
所需系统内存 (CPU 卸载)0.00 GB
当前硬件可用显存141 GB (1x NVIDIA H200 141GB SXM)
显存余量 / 溢出缺口135.57 GB
预计生成速度 (Decode Speed)~350 tokens/sec
预计预填充速度 (Prefill Speed)~18000 tokens/sec
4K 提示词首字延迟 (TTFT)~228 ms
Transformer 层数与注意力头Dense-GQA (28L, 4/28 KV/Q heads)
🚀 一键复制启动命令 (vLLM / llama.cpp / Ollama)
ollama run qwen2.5:7b # Or llama.cpp server (8K context, Flash Attention): llama-server -hf Qwen/Qwen2.5-7B-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 (Qwen2.5-7B-Instruct: 5.43 GB in 4-bit)
Recommended Cloud Tier: RTX 4090 (24GB) · Full FP16 Needs: 15.56 GB
租卡对比买卡回本周期： ~32 months (vs $36,000 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: Qwen2.5-7B-Instruct × NVIDIA H200 141GB SXM
| Quantization Format | Core / Emb BPW | Weights (GiB) | KV Cache (8K) | Total VRAM | 1x NVIDIA H200 141GB SXM (141GB) | Decode / Prefill | Min GPUs |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 / 16 | 14.19 GB | 0.44 GB | 15.12 GB | 支持 (11%) | ~216.4 / 18000 tok/s | 1x |
| FP8 (W8A8 / E4M3) | 8.25 / 8.5 | 7.35 GB | 0.44 GB | 8.28 GB | 支持 (6%) | ~350 / 18000 tok/s | 1x |
| GGUF Q8_0 (8.50 bpw) | 8.5 / 8.5 | 7.54 GB | 0.44 GB | 8.47 GB | 支持 (6%) | ~350 / 18000 tok/s | 1x |
| GGUF Q6_K (6.56 bpw) | 6.56 / 6.56 | 5.82 GB | 0.44 GB | 6.74 GB | 支持 (5%) | ~350 / 18000 tok/s | 1x |
| GGUF Q5_K_M (5.69 bpw) | 5.69 / 6.56 | 5.16 GB | 0.44 GB | 6.08 GB | 支持 (4%) | ~350 / 18000 tok/s | 1x |
| GGUF Q4_K_M / AWQ (4.83 bpw) | 4.83 / 6.56 | 4.5 GB | 0.44 GB | 5.43 GB | 支持 (4%) | ~350 / 18000 tok/s | 1x |
| GGUF IQ4_XS (4.25 bpw) | 4.25 / 5.5 | 3.93 GB | 0.44 GB | 4.85 GB | 支持 (3%) | ~350 / 18000 tok/s | 1x |
| GGUF Q3_K_M (3.91 bpw) | 3.91 / 5 | 3.61 GB | 0.44 GB | 4.53 GB | 支持 (3%) | ~350 / 18000 tok/s | 1x |
在其他显卡上测试该模型
Qwen2.5-7B-Instruct on NVIDIA RTX 5090 (32GB)Qwen2.5-7B-Instruct on NVIDIA RTX 4090 (24GB)Qwen2.5-7B-Instruct on NVIDIA RTX 5080 (16GB)Qwen2.5-7B-Instruct on NVIDIA RTX 4080 Super (16GB)Qwen2.5-7B-Instruct on NVIDIA RTX 4070 Ti Super (16GB)Qwen2.5-7B-Instruct on NVIDIA RTX 3090 (24GB)Qwen2.5-7B-Instruct on NVIDIA RTX 4060 Ti 16GB (16GB)Qwen2.5-7B-Instruct on NVIDIA RTX 3060 12GB (12GB)Qwen2.5-7B-Instruct on NVIDIA RTX 6000 Ada (48GB)Qwen2.5-7B-Instruct on NVIDIA RTX A6000 (48GB)
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