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按模型参数规模反向装机选卡梯队指南

拒绝盲目装机！根据你想跑的目标模型，直接挑选显存能够完全包容、不触发 PCIe 降速的显卡。以下为 5 大参数级别的官方工程选卡对照：

7B – 8B Models

Llama 3.1 8B, Qwen 2.5 7B, Mistral 7B
Required: 8GB – 12GB VRAM
性价比入门首选
RTX 3060 12G ($280)
甜点主力推荐
RTX 4070 12G ($540)
旗舰全速天花板
RTX 4070 Ti Super 16G ($799)
Mac 统一内存推荐
Apple M3 16G ($1,299)

架构避坑指南: Avoid 8GB cards if you plan to use long contexts (16k+) or background desktop apps.

12B – 14B Models

DeepSeek-R1-Distill-Qwen-14B, Mistral NeMo 12B, Qwen 2.5 Coder 14B
Required: 12GB – 16GB VRAM
性价比入门首选
RTX 3060 12G ($280) / Arc B580 12G ($249)
甜点主力推荐
RTX 4070 Ti Super 16G ($799)
旗舰全速天花板
RTX 4080 Super 16G ($999)
Mac 统一内存推荐
Apple M3 Pro 18G / 36G

架构避坑指南: The RTX 3060 12G is the absolute entry champion for 14B models at Q4_K_M.

32B Models

DeepSeek-R1-Distill-Qwen-32B, Qwen 2.5 Coder 32B
Required: 22GB – 26GB VRAM
性价比入门首选
Used RTX 3090 24G ($750)
甜点主力推荐
RTX 4090 24G ($1,750)
旗舰全速天花板
RTX 5090 32G ($1,999)
Mac 统一内存推荐
Apple M3 Max 36G / 48G

架构避坑指南: 24GB VRAM is the minimum requirement to run 32B coding models without PCIe offloading.

70B – 72B Models

DeepSeek-R1-Distill-Llama-70B, Llama 3.3 70B, Qwen 2.5 72B
Required: 42GB – 48GB VRAM
性价比入门首选
2x RTX 3090 24G NVLink ($1,500 total)
甜点主力推荐
RTX 5090 32G (IQ4_XS) or 2x 4090
旗舰全速天花板
NVIDIA RTX 6000 Ada 48G ($6,800)
Mac 统一内存推荐
Mac Studio M2 Ultra 64G / 128G ($3,999)

架构避坑指南: Dual 24GB GPUs or a Mac Studio M2 Ultra 128GB provide the lowest cost per token.

671B Full MoE Models

DeepSeek-R1 Full 671B, DeepSeek-V3 671B
Required: 140GB – 180GB VRAM
性价比入门首选
Mac Studio M2 Ultra 192GB Unified Memory ($6,999)
甜点主力推荐
8x RTX 3090/4090 Distributed Cluster
旗舰全速天花板
8x NVIDIA H100 80G SXM5 ($300,000+)
Mac 统一内存推荐
Mac Studio M2 Ultra 192GB

架构避坑指南: For single-box desktop deployment, Apple Silicon 192GB Unified Memory is the only consumer solution.

大模型装机选卡常见高频疑问

基于显存架构、总线带宽与真实测试基准给出的权威解答：

What is the best budget GPU for running local AI models in 2025/2026?
The NVIDIA GeForce RTX 3060 12GB is widely recognized as the entry-level budget champion for local AI. With its 12GB VRAM buffer and 360 GB/s bandwidth, it can run 7B and 14B models (such as DeepSeek-R1-Distill-Qwen-14B) fully within VRAM at Q4_K_M quantization without memory offloading.
How much VRAM do I need to run DeepSeek R1 locally?
VRAM requirements depend on parameter count: DeepSeek-R1 1.5B/7B/8B requires 6GB–8GB; 14B requires 12GB; 32B requires 24GB (e.g., RTX 3090/4090); 70B requires 48GB+ (dual 24GB GPUs or RTX 5090 32GB at IQ4_XS); and the full 671B MoE requires 140GB–180GB (Apple Mac Studio M2 Ultra 192GB or multi-node cluster).
Why is 8GB VRAM considered insufficient for modern local LLMs?
Even a 7B model at 4-bit quantization occupies ~4.8 GB for weights alone. Adding an 8k context window KV cache (1.5 GB), CUDA context buffers (0.5 GB), and OS desktop display overhead (0.8 GB on Windows) pushes memory consumption past 7.6 GB. Any multi-turn conversation or browser usage breaches the 8GB ceiling, triggering severe PCIe offload throttling.
Is Apple Silicon unified memory better than dedicated NVIDIA GPUs for local AI?
Apple Silicon excels at memory capacity: a Mac Studio M2 Ultra offers up to 192GB unified memory, allowing you to load massive 70B and 671B models on a single desktop machine. However, NVIDIA GPUs deliver significantly higher memory bandwidth (up to 1,792 GB/s on RTX 5090 vs 800 GB/s on M2 Ultra), providing much faster token generation speeds for models that fit within VRAM.