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双RTX 5060 Ti本地推理配置

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一名 Reddit 用户报告用两张 RTX 5060 Ti 16GB 运行 Qwen3.8-27B NVFP4,并结合 capy-flash、OpenCode、张量并行、MTP 推测解码和 int8 KV cache。作者称,将两张显卡都连接到 CPU 通道后,首 token 时间约缩短至原来的 1/2.5,批处理吞吐量也翻倍。该配置据称可实现每秒 70–95 个 token 的解码速度,但 PCIe Gen4 x8/x8 的相关数据仍为估算。

深度见解

该报告提供了本地大模型推理的具体硬件与软件建议及实测对比,对相关开发者有参考价值,但数据仅来自单一用户,尚无独立复现。

Over the last few months I've been switching harnesses and engines until I finally landed on something good. I started with LM Studio + the Copilot extension and ended up here, so here are some tips for anyone running two 5060 Tis. Prefill is painful on these cards, so for hardware, make sure you have:

A motherboard with both GPUs on CPU lanes, either two slots wired to the CPU or one x16 slot with PCIe bifurcation (x8/x8). My second card used to hang off the chipset; moving it to CPU lanes cut time-to-first-token by about 2.5x and doubled my batching throughput.

A separate low-profile GPU just for system graphics. If one of your 5060 Tis drives the display, it burns 2-3 GB of VRAM, and since the layer split is even, the other card loses the same amount too. That's context you don't get.

Model: these are Blackwell cards, so an NVFP4 quant gives you better prefill and decode.

What I run: capy-flash (formerly ninfer-tp2) + OpenCode, Qwen3.8-27B NVFP4 with tensor parallel across both cards, MTP speculative decoding and int8 KV cache. I get 70-95 t/s decode and ~1,500 t/s prefill at 176K context (~3,500-4,000 t/s on shorter prompts). You can go to full context, I just don't have a third card.

#NVIDIA GeForce RTX 5060 Ti#capy-flash#OpenCode#Qwen3.8-27B NVFP4#AMD Ryzen 7 5800X

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