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<main>
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<h1 id="在-avx2-cpu-上使用-ktransformers"><a class="header" href="#在-avx2-cpu-上使用-ktransformers">在 AVX2 CPU 上使用 KTransformers</a></h1>
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<p>本教程介绍如何在仅支持 AVX2 的机器上运行 KTransformers（无需 AVX512 或 AMX）。</p>
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<h2 id="目录"><a class="header" href="#目录">目录</a></h2>
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<ul>
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<li><a href="#支持的精度格式">支持的精度格式</a></li>
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<li><a href="#硬件要求">硬件要求</a></li>
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<li><a href="#安装">安装</a></li>
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<li><a href="#验证">验证</a></li>
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<li><a href="#启动推理服务">启动推理服务</a>
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<ul>
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<li><a href="#示例qwen3-30b-a3b-bf16">示例：Qwen3-30B-A3B (BF16)</a></li>
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<li><a href="#示例qwen35-35b-a3b-fp8-fp8">示例：Qwen3.5-35B-A3B-FP8 (FP8)</a></li>
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<li><a href="#示例qwen3-30b-a3b-gptq-int4-gptq_int4">示例：Qwen3-30B-A3B-GPTQ-Int4 (GPTQ_INT4)</a></li>
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<li><a href="#发送请求">发送请求</a></li>
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</ul>
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</li>
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<li><a href="#性能调优">性能调优</a></li>
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<li><a href="#常见问题">常见问题</a></li>
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</ul>
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<h2 id="支持的精度格式"><a class="header" href="#支持的精度格式">支持的精度格式</a></h2>
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<div class="table-wrapper">
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<table>
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<thead>
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<tr><th><code>--kt-method</code></th><th>精度</th><th>说明</th></tr>
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</thead>
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<tbody>
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<tr><td><code>BF16</code></td><td>BF16 原精度</td><td>零精度损失，直接使用 BF16 权重</td></tr>
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<tr><td><code>FP8</code></td><td>FP8 分块量化</td><td></td></tr>
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<tr><td><code>GPTQ_INT4</code></td><td>INT4 GPTQ</td><td></td></tr>
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</tbody>
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</table>
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</div>
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<h2 id="硬件要求"><a class="header" href="#硬件要求">硬件要求</a></h2>
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<ul>
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<li><strong>CPU</strong>：x86-64 + AVX2 + FMA（Intel Haswell 2013+ / AMD Zen+）</li>
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<li><strong>GPU</strong>：NVIDIA 24GB+ 显存（RTX 3090/4090/5090 等）</li>
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<li><strong>内存</strong>：不少于模型权重大小（如 Qwen3-30B-A3B BF16 需 64GB+）</li>
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<li><strong>系统</strong>：Linux</li>
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</ul>
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<h2 id="安装"><a class="header" href="#安装">安装</a></h2>
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<p>从源码编译安装（一键安装 kt-kernel + SGLang）：</p>
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<pre><code class="language-bash">git clone https://github.com/kvcache-ai/ktransformers.git
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cd ktransformers
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git submodule update --init --recursive
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||
|
||
# 一键安装
|
||
./install.sh
|
||
</code></pre>
|
||
<p>在AVX512， AMX机器上， 也可以手动强制 AVX2 编译：</p>
|
||
<pre><code class="language-bash">export CPUINFER_CPU_INSTRUCT=AVX2
|
||
export CPUINFER_ENABLE_AMX=OFF
|
||
./install.sh kt-kernel --manual
|
||
</code></pre>
|
||
<h2 id="验证"><a class="header" href="#验证">验证</a></h2>
|
||
<pre><code class="language-bash"># 检查 CPU 是否支持 AVX2
|
||
lscpu | grep -i avx2
|
||
|
||
# 检查 kt-kernel 加载的变体
|
||
python -c "import kt_kernel; print(kt_kernel.__cpu_variant__)"
|
||
# 预期输出：avx2
|
||
|
||
# 系统诊断
|
||
kt doctor
|
||
</code></pre>
|
||
<h2 id="启动推理服务"><a class="header" href="#启动推理服务">启动推理服务</a></h2>
|
||
<p>使用 <code>--kt-method BF16</code>、<code>FP8</code> 或 <code>GPTQ_INT4</code>，KT-Kernel 会<strong>自动检测</strong> CPU 并在缺少 AVX512/AMX 时回退到 AVX2 后端。</p>
|
||
<h3 id="示例qwen3-30b-a3b-bf16"><a class="header" href="#示例qwen3-30b-a3b-bf16">示例：Qwen3-30B-A3B (BF16)</a></h3>
|
||
<pre><code class="language-bash"># 下载模型
|
||
huggingface-cli download Qwen/Qwen3-30B-A3B --local-dir /path/to/Qwen3-30B-A3B
|
||
|
||
# 查看物理核心数和 NUMA 节点数
|
||
lscpu | grep -E "^CPU\(s\)|Thread\(s\) per core|NUMA node\(s\)"
|
||
|
||
# 启动服务（按实际硬件调整 kt-cpuinfer 和 kt-threadpool-count）
|
||
python -m sglang.launch_server \
|
||
--host 0.0.0.0 --port 30000 \
|
||
--model /path/to/Qwen3-30B-A3B \
|
||
--kt-weight-path /path/to/Qwen3-30B-A3B \
|
||
--kt-cpuinfer 16 \
|
||
--kt-threadpool-count 1 \
|
||
--kt-num-gpu-experts 32 \
|
||
--kt-method BF16 \
|
||
--attention-backend flashinfer \
|
||
--trust-remote-code \
|
||
--mem-fraction-static 0.80 \
|
||
--chunked-prefill-size 8192 \
|
||
--max-running-requests 2 \
|
||
--served-model-name Qwen3 \
|
||
--enable-mixed-chunk \
|
||
--tensor-parallel-size 1 \
|
||
--enable-p2p-check \
|
||
--disable-shared-experts-fusion
|
||
</code></pre>
|
||
<h3 id="示例qwen35-35b-a3b-fp8-fp8"><a class="header" href="#示例qwen35-35b-a3b-fp8-fp8">示例：Qwen3.5-35B-A3B-FP8 (FP8)</a></h3>
|
||
<pre><code class="language-bash"># 下载模型
|
||
huggingface-cli download Qwen/Qwen3.5-35B-A3B-FP8 --local-dir /path/to/Qwen3.5-35B-A3B-FP8
|
||
|
||
# 启动服务
|
||
python -m sglang.launch_server \
|
||
--host 0.0.0.0 --port 30000 \
|
||
--model /path/to/Qwen3.5-35B-A3B-FP8 \
|
||
--kt-weight-path /path/to/Qwen3.5-35B-A3B-FP8 \
|
||
--kt-cpuinfer 16 \
|
||
--kt-threadpool-count 1 \
|
||
--kt-num-gpu-experts 2 \
|
||
--kt-method FP8 \
|
||
--kt-gpu-prefill-token-threshold 400 \
|
||
--attention-backend triton \
|
||
--trust-remote-code \
|
||
--mem-fraction-static 0.85 \
|
||
--chunked-prefill-size 4096 \
|
||
--max-running-requests 1 \
|
||
--max-total-tokens 32000 \
|
||
--enable-mixed-chunk \
|
||
--tensor-parallel-size 1 \
|
||
--disable-shared-experts-fusion
|
||
</code></pre>
|
||
<h3 id="示例qwen3-30b-a3b-gptq-int4-gptq_int4"><a class="header" href="#示例qwen3-30b-a3b-gptq-int4-gptq_int4">示例：Qwen3-30B-A3B-GPTQ-Int4 (GPTQ_INT4)</a></h3>
|
||
<pre><code class="language-bash"># 下载模型
|
||
huggingface-cli download Qwen/Qwen3-30B-A3B-GPTQ-Int4 --local-dir /path/to/Qwen3-30B-A3B-GPTQ-Int4
|
||
|
||
# 启动服务
|
||
python -m sglang.launch_server \
|
||
--host 0.0.0.0 --port 30000 \
|
||
--model /path/to/Qwen3-30B-A3B-GPTQ-Int4 \
|
||
--kt-weight-path /path/to/Qwen3-30B-A3B-GPTQ-Int4 \
|
||
--kt-cpuinfer 16 \
|
||
--kt-threadpool-count 1 \
|
||
--kt-num-gpu-experts 2 \
|
||
--kt-method GPTQ_INT4 \
|
||
--attention-backend triton \
|
||
--trust-remote-code \
|
||
--mem-fraction-static 0.85 \
|
||
--chunked-prefill-size 4096 \
|
||
--max-running-requests 1 \
|
||
--max-total-tokens 32000 \
|
||
--enable-mixed-chunk \
|
||
--tensor-parallel-size 1 \
|
||
--disable-shared-experts-fusion
|
||
</code></pre>
|
||
<h3 id="发送请求"><a class="header" href="#发送请求">发送请求</a></h3>
|
||
<pre><code class="language-bash"># 交互聊天
|
||
kt chat
|
||
|
||
# OpenAI 兼容 API
|
||
curl http://localhost:30000/v1/chat/completions \
|
||
-H "Content-Type: application/json" \
|
||
-d '{"model":"Qwen3","messages":[{"role":"user","content":"你好"}],"stream":true}'
|
||
</code></pre>
|
||
<h2 id="性能调优"><a class="header" href="#性能调优">性能调优</a></h2>
|
||
<ul>
|
||
<li><code>--kt-cpuinfer</code> 设为<strong>物理核心数</strong></li>
|
||
<li><code>--kt-threadpool-count</code> 设为 <strong>NUMA 节点数</strong></li>
|
||
<li><code>--kt-num-gpu-experts</code> 越大 CPU 负担越小，但 GPU 显存占用越高</li>
|
||
<li>内存带宽往往是瓶颈，DDR5 高频内存有明显帮助</li>
|
||
</ul>
|
||
<h2 id="常见问题"><a class="header" href="#常见问题">常见问题</a></h2>
|
||
<p><strong>GPU OOM</strong></p>
|
||
<ul>
|
||
<li>减小 <code>--kt-num-gpu-experts</code>、<code>--chunked-prefill-size</code>、<code>--max-total-tokens</code></li>
|
||
<li>降低 <code>--mem-fraction-static</code></li>
|
||
</ul>
|
||
<p>更多问题参见 <a href="../en/FAQ.html">FAQ</a>。</p>
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