RTX 2080 Ti / 3090 / 4090 / 5090 AI 入门选购建议与参数对比(截至 2025 年11月)
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💡 选购建议(按预算 & 需求)
| 目标 | 推荐显卡 | 价格区间(人民币) | 理由说明 |
|---|---|---|---|
| 预算有限,只想体验 AI 玩玩 | RTX 2080 Ti(魔改 22GB) | ¥2,000 – ¥3,000 | 性价比极高;显存魔改后(22GB)可运行多数 LLM 推理(如 7B 模型量化版),适合入门学习。但架构较老(Turing),无 FP8/BF16/TF32 原生支持。 |
| 中等预算,追求稳定 & 性能平衡 | RTX 3090 | ¥5,500 – ¥6,000 | 24GB 大显存 + Ampere 架构,支持 BF16/TF32,适合运行 13B 以内量化模型,兼顾训练与推理,是老卡中的“AI 神卡”。 |
| 追求前沿 AI 能力 & 最佳性价比(高端) | RTX 4090 / 4090D | ¥15,000 – ¥20,000 | Ada Lovelace 架构,原生支持 FP8、DLSS 3、超高 Tensor 性能;24GB GDDR6X + 1TB/s+ 带宽,可流畅运行 13B~30B 量化模型,是当前最推荐的高端 AI 卡(即便 5090 发布后仍极具性价比)。 |
| 顶级预算,尝鲜最新技术 | RTX 5090(预计) | ¥20,000 – ¥25,000 | Blackwell 架构,支持 FP4/FP8、GDDR7、PCIe 5.0;性能飞跃,适合大规模本地推理或微调。但价格昂贵,性价比不如 4090。 |
✅ 总结:
- “玩玩 AI” → 2080 Ti 魔改版(22GB 是关键)
- “认真搞点小项目” → 3090
- “追求前沿体验 & 长期使用” → 4090(首选)或 5090(尝鲜)
📊 核心参数对比表
| 参数类别 | 参数名称 | RTX 2080 Ti | RTX 3090 | RTX 4090 | RTX 5090 |
|---|---|---|---|---|---|
| 基础信息 | GPU Codename | TU102 | GA102 | AD102 | GB202 |
| GPU Architecture | NVIDIA Turing | NVIDIA Ampere | NVIDIA Ada Lovelace | NVIDIA Blackwell | |
| GPCs (Graphics Processing Clusters) | 6 | 7 | 11 | 11 | |
| TPCs (Texture Processing Clusters) | 34 | 41 | 64 | 85 | |
| SMs (Streaming Multiprocessors) | 68 | 82 | 128 | 170 | |
| CUDA Cores / SM | 64 | 128 | 128 | 128 | |
| CUDA Cores / GPU | 4,352 | 10,496 | 16,384 | 21,760 | |
| Tensor Cores / SM | 8 (2nd Gen) | 4 (3rd Gen) | 4 (4th Gen) | 4 (5th Gen) | |
| Tensor Cores / GPU | 544 | 328 | 512 | 680 | |
| RT Cores | 68 (1st Gen) | 82 (2nd Gen) | 128 (3rd Gen) | 170 (4th Gen) | |
| GPU Boost Clock (MHz) | 1,545 / 1,635 | 1,695 | 2,520 | 2,407 | |
| CUDA 性能 | Peak FP16 TFLOPS (non-Tensor) | 26.9 / 28.5 | 35.6 | 82.6 | 104.8 |
| Peak BF16 TFLOPS (non-Tensor) | N/A | 35.6 | 82.6 | 104.8 | |
| Peak FP32 TFLOPS (non-Tensor) | 13.4 / 14.2 | 35.6 | 82.6 | 104.8 | |
| Peak INT32 TOPS (non-Tensor) | 13.4 / 14.2 | 17.8 | 41.3 | 104.8 | |
| RT TFLOPS (Giga Rays/sec) | 10 | 69.5 | 191 | 317.5 | |
| Tensor 性能 | Peak FP4 Tensor TFLOPS (FP32 Accumulate) | N/A | N/A | N/A | 1,676 / 3,352 |
| Peak INT4 Tensor TOPS | 430.3 / 455.4 | 568 / 1136 | 1321.2 / 2642.4 | N/A | |
| Peak FP8 Tensor TFLOPS (FP16 Accumulate) | N/A | N/A | 660.6 / 1,321.2 | 838 / 1,676 | |
| Peak FP8 Tensor TFLOPS (FP32 Accumulate) | N/A | N/A | 330.3 / 660.6 | 419 / 838 | |
| Peak INT8 Tensor TOPS | 215.2 / 227.7 | 284 / 568 | 660.6 / 1,321.2 | 838 / 1,676 | |
| Peak FP16 Tensor TFLOPS (FP16 Accumulate) | 107.6 / 113.8 | 142 / 284 | 330.3 / 660.6 | 419 / 838 | |
| Peak FP16 Tensor TFLOPS (FP32 Accumulate) | 53.8 / 56.9 | 71 / 142 | 165.2 / 330.4 | 209.5 / 419 | |
| Peak BF16 Tensor TFLOPS (FP32 Accumulate) | N/A | 71 / 142 | 165.2 / 330.4 | 209.5 / 419 | |
| Peak TF32 Tensor TFLOPS | N/A | 35.6 / 71 | 82.6 / 165.2 | 104.8 / 209.5 | |
| 显存系统 | Frame Buffer Memory Size and Type | 11 GB GDDR6 | 24 GB GDDR6X | 24 GB GDDR6X | 32 GB GDDR7 |
| Memory Interface | 352-bit | 384-bit | 384-bit | 512-bit | |
| Memory Clock (Data Rate) | 14 Gbps | 19.5 Gbps | 21 Gbps | 28 Gbps | |
| Memory Bandwidth | 616 GB/s | 936 GB/s | 1,008 GB/s | 1,792 GB/s | |
| 渲染输出 | ROPs | 88 | 112 | 176 | 176 |
| Pixel Fill-rate (GPixel/s) | N/A | 193 | 443.5 | 423.6 | |
| Texture Units | 272 | 328 | 512 | 680 | |
| Texel Fill-rate (GTexel/s) | 420.2 / 444.7 | 566 | 1,290.20 | 1,636.80 | |
| 缓存与寄存器(单SM) | L1 Data Cache / Shared Memory (KB)(SM) | 96 | 128 | 128 | 128 |
| L2 Cache Size (KB)(GPU) | 5,632 | 6,144 | 73,728 | 98,304 | |
| Register File Size (KB)(SM) | 256 | 256 | 256 | 256 | |
| 其他信息 | Video Engines (NVENC/NVDEC) | 2×NVENC (7th Gen) 1×NVDEC (4th Gen) |
1×NVENC (7th Gen) 1×NVDEC (5th Gen) |
2×NVENC (8th Gen) 1×NVDEC (5th Gen) |
3×NVENC (9th Gen) 2×NVDEC (6th Gen) |
| TGP (Total Graphics Power) (W) | 250 / 260 | 350 | 450 | 575 | |
| Transistor Count (Billion) | 18.6 | 28.3 | 76.3 | 92.2 | |
| Die Size (mm²) | 754 | 628.4 | 608.5 | 750 | |
| Manufacturing Process | 12 nm FFN | Samsung 8nm (8N) | TSMC 4nm (4N) | TSMC 4nm (4N) | |
| PCI Express Interface | Gen 3 | Gen 4 | Gen 4 | Gen 5 |
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