<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[4 DGX Spark DeepSeek V4.1 Flash 1017版本详细测试数据！]]></title><description><![CDATA[<p dir="auto">上一篇写的是这套无交换机环拓扑的<strong>部署</strong>。这次 DeepSeek 放了 <code>V4.1-Flash-1017</code> 新 checkpoint，我把栈升到配套的 SparkRing 版本、切到 1017 权重后，把同一套<strong>并发 × 上下文</strong>矩阵重新跑了一遍，并逐项和上一版（旧 663B checkpoint）做了对照。拓扑、机型、推理引擎都没变，变量只有<strong>权重版本 + 服务镜像</strong>。</p>
<hr />
<h2>一、测试环境与口径</h2>
<table class="table table-bordered table-striped">
<thead>
<tr>
<th>项目</th>
<th>上一版（10-15）</th>
<th><strong>本版（10-21 起服务）</strong></th>
</tr>
</thead>
<tbody>
<tr>
<td>权重 checkpoint</td>
<td><code>dba1be0a40aa</code></td>
<td><strong><code>1bae0e108899</code>（V4.1-Flash-1017）</strong>，48 分片 475.3 GiB</td>
</tr>
<tr>
<td>服务名</td>
<td><code>DeepSeek-V4.1-Flash-TP4</code></td>
<td><strong><code>DeepSeek-V4.1-Flash-1017-TP4</code></strong></td>
</tr>
<tr>
<td>SparkRing</td>
<td><code>0.1.0~dev…+git928142a7b8d3</code></td>
<td><strong><code>0.1.0~dev.1792279821+gitad1853fc4c4c</code></strong></td>
</tr>
<tr>
<td>服务镜像</td>
<td><code>dev-20261013-dcp2prefill-status038</code></td>
<td><strong><code>dev-20261016-sparkcache-4a17f1f5198e-status038</code></strong></td>
</tr>
<tr>
<td>推理引擎</td>
<td>vLLM Kraken <code>0.1.dev21553+gab86b7073</code></td>
<td>同版本（未变）</td>
</tr>
</tbody>
</table>
<p dir="auto"><strong>本版实测服务规格</strong>（读容器启动参数与引擎日志，不是抄配置文档）：</p>
<table class="table table-bordered table-striped">
<thead>
<tr>
<th>参数</th>
<th>实测值</th>
</tr>
</thead>
<tbody>
<tr>
<td>Context Length</td>
<td><strong>1,048,576</strong></td>
</tr>
<tr>
<td>Max Running Requests</td>
<td><strong>8</strong>（<code>max_num_seqs=8</code>，CUDA Graph 捕获上限 96）</td>
</tr>
<tr>
<td>KV Cache</td>
<td><strong>11,434,628 tokens</strong>（可用 KV 显存 <strong>14.23 GiB</strong>）；1M 单请求可支撑 <strong>10.90 路</strong></td>
</tr>
<tr>
<td>静态显存比例</td>
<td><code>0.83</code></td>
</tr>
<tr>
<td>分块预填充</td>
<td><code>max_num_batched_tokens=8192</code> + chunked prefill</td>
</tr>
<tr>
<td>推测解码</td>
<td><strong>DSpark</strong>，<code>num_speculative_tokens=5</code>、概率式草稿、块级拒绝、自适应验证</td>
</tr>
<tr>
<td>量化 / Engram</td>
<td><code>deepseek_v41_fp8</code>；Engram 表 <code>table_memory=disk</code> + <code>disk_resident_scales</code></td>
</tr>
<tr>
<td>环网传输</td>
<td><strong>SIRCL</strong>；<code>Transport: sircl, NCCL: absent</code></td>
</tr>
<tr>
<td>思考控制</td>
<td>默认 <strong>on · high</strong>，档位 <code>low / high / xhigh(mx) / max / 1–100</code></td>
</tr>
<tr>
<td>多模态</td>
<td><code>limit_mm_per_prompt {"image": 4}</code>，原生视觉</td>
</tr>
<tr>
<td>端点</td>
<td><code>http://192.168.10.10:8015/v1</code>，<strong>无鉴权</strong></td>
</tr>
<tr>
<td>四机 SM 时钟</td>
<td><strong>2509 / 2522 / 2509 / 2541 MHz</strong>（max 3003），温度 53–56 °C，无降频</td>
</tr>
<tr>
<td>并发设置</td>
<td><code>--max-concurrency 8</code></td>
</tr>
</tbody>
</table>
<blockquote>
<p dir="auto">与上一版相比 KV 池从 12,715,913 → <strong>11,434,628 tokens</strong>（15.89 → 14.23 GiB），1M 单请求并发余量 12.13 → <strong>10.90 路</strong>。新镜像的显存切分略有变化，权重本身大小一致。</p>
</blockquote>
<p dir="auto"><strong>测量口径</strong>（与上一版一致，便于逐格对照）：</p>
<ul>
<li>每档输出 <strong>128 tokens</strong>；填充文本带<strong>唯一 nonce + 递增编号</strong>，不命中 prefix 缓存；温度 0.6</li>
<li><strong>单流 pp/tg</strong> = 各请求均值；<strong>聚合 pp</strong> = Σprompt / max(TTFT)；<strong>聚合 tg</strong> = Σcompletion / (max 总时长 − max TTFT)</li>
<li>并发 1 / 2 / 4 / 8（8 = <code>max-num-seqs</code> 上限），上下文档位 1K → 512K</li>
<li><strong>本版改进了采样方式</strong>：≤64K 各档每点跑 <strong>3 次取中位</strong>，因为发现每轮第一笔请求有约 <strong>2.2 秒冷启开销</strong>（稳态仅 0.98 秒，8 次连测标准差 0.01）；≥128K 各档单次采样，该区间冷启占比 &lt;1%。上一版全部为单次采样。</li>
</ul>
<hr />
<h2>二、栈自检（功能与能力，全部本机环上实跑）</h2>
<table class="table table-bordered table-striped">
<thead>
<tr>
<th>测试项</th>
<th>输入</th>
<th>结果</th>
<th>状态</th>
</tr>
</thead>
<tbody>
<tr>
<td>计数</td>
<td><code>1…20</code> 顺序输出</td>
<td>20 个数全对（79 tokens）</td>
<td><strong>PASS</strong></td>
</tr>
<tr>
<td>算术</td>
<td><code>17 × 23</code></td>
<td><code>391</code>（思考 19 tokens）</td>
<td><strong>PASS</strong></td>
</tr>
<tr>
<td>代码生成</td>
<td>Python <code>is_prime(n)</code></td>
<td>完整可用实现（含类型校验与 <code>n % 2/3</code> 预筛）</td>
<td><strong>PASS</strong></td>
</tr>
<tr>
<td>工具调用</td>
<td><code>get_weather(巴黎)</code></td>
<td>正确发起 <code>get_weather</code>，参数 <code>{"city":"巴黎"}</code></td>
<td><strong>PASS</strong></td>
</tr>
<tr>
<td>强制工具调用</td>
<td><code>tool_choice=required</code></td>
<td>正确发起 <code>get_weather(罗马)</code></td>
<td><strong>PASS</strong></td>
</tr>
<tr>
<td>原生视觉</td>
<td>左红右蓝 128×64 PNG</td>
<td>「这张图左半边是红色，右半边是蓝色。」（202 vision tokens）</td>
<td><strong>PASS</strong></td>
</tr>
<tr>
<td>思考开关</td>
<td><code>chat_template_kwargs: {"thinking": false}</code></td>
<td>关：0 reasoning tokens 直答 <code>391</code>；开：18 reasoning tokens</td>
<td><strong>PASS</strong></td>
</tr>
<tr>
<td>思考档位</td>
<td><code>reasoning_effort</code> 三档</td>
<td>low <strong>83</strong> / high <strong>77</strong> / max <strong>169</strong> reasoning tokens（同一问题）</td>
<td><strong>PASS</strong></td>
</tr>
<tr>
<td>传输层</td>
<td>本次集合通信走哪条路</td>
<td><code>Transport: sircl, NCCL: absent</code></td>
<td><strong>PASS</strong></td>
</tr>
<tr>
<td>频率</td>
<td>四机 SM 时钟</td>
<td>2509–2541 MHz（max 3003），无降频事件</td>
<td><strong>PASS</strong></td>
</tr>
</tbody>
</table>
<blockquote>
<p dir="auto">能力边界说明：<strong>图文混合输入正常</strong>；<strong>音频输入不支持</strong>（服务端明确报 <code>At most 0 audio(s) may be provided in one prompt</code>）。思考链落在非标准字段 <code>reasoning</code>，不是 OpenAI 惯例的 <code>reasoning_content</code>——对接客户端时要注意这个字段名。</p>
</blockquote>
<hr />
<h2>三、长上下文阶梯压测（并发 1）</h2>
<table class="table table-bordered table-striped">
<thead>
<tr>
<th>目标上下文</th>
<th>实际 prompt tokens</th>
<th>TTFT</th>
<th>单流 prefill</th>
<th>单流解码</th>
<th>墙钟</th>
</tr>
</thead>
<tbody>
<tr>
<td>1K</td>
<td>1,004</td>
<td>1.22s</td>
<td>821.3 tok/s</td>
<td>55.3 tok/s</td>
<td>3.75s</td>
</tr>
<tr>
<td>4K</td>
<td>3,800</td>
<td>1.84s</td>
<td>2,064.3 tok/s</td>
<td>71.5 tok/s</td>
<td>3.63s</td>
</tr>
<tr>
<td>8K</td>
<td>7,513</td>
<td>3.10s</td>
<td>2,427.1 tok/s</td>
<td>73.0 tok/s</td>
<td>5.01s</td>
</tr>
<tr>
<td>16K</td>
<td>14,955</td>
<td>4.39s</td>
<td>3,403.2 tok/s</td>
<td><strong>83.7</strong> tok/s</td>
<td>5.99s</td>
</tr>
<tr>
<td>32K</td>
<td>29,881</td>
<td>7.46s</td>
<td>4,006.8 tok/s</td>
<td><strong>92.7</strong> tok/s</td>
<td>8.84s</td>
</tr>
<tr>
<td>64K</td>
<td>61,913</td>
<td>15.30s</td>
<td>4,048.1 tok/s</td>
<td><strong>102.6</strong> tok/s</td>
<td>16.52s</td>
</tr>
<tr>
<td>128K</td>
<td>124,759</td>
<td>33.27s</td>
<td>3,750.3 tok/s</td>
<td>67.4 tok/s</td>
<td>35.17s</td>
</tr>
<tr>
<td>256K</td>
<td>250,485</td>
<td>73.30s</td>
<td>3,417.4 tok/s</td>
<td>75.5 tok/s</td>
<td>74.99s</td>
</tr>
<tr>
<td><strong>512K</strong></td>
<td>501,869</td>
<td><strong>181.40s</strong></td>
<td>2,766.6 tok/s</td>
<td>73.0 tok/s</td>
<td>183.15s</td>
</tr>
</tbody>
</table>
<blockquote>
<p dir="auto">单流解码在 <strong>16K–64K 区间冲到 83.7 / 92.7 / 102.6 tok/s</strong>，是这版最突出的地方；512K 单请求 TTFT <strong>181.4s</strong>、prefill <strong>2.77k tok/s</strong>，全程无 OOM。</p>
</blockquote>
<hr />
<h2>四、高并发 × 上下文 二维扫描</h2>
<h4>并发 2</h4>
<table class="table table-bordered table-striped">
<thead>
<tr>
<th>上下文</th>
<th>prompt/请求</th>
<th>TTFT</th>
<th>单流 pp</th>
<th>单流 tg</th>
<th>聚合 pp</th>
<th>聚合 tg</th>
<th>墙钟</th>
</tr>
</thead>
<tbody>
<tr>
<td>1K</td>
<td>1,002</td>
<td>1.43s</td>
<td>700.7</td>
<td>83.9</td>
<td>1,378.7</td>
<td>165.8</td>
<td>2.98s</td>
</tr>
<tr>
<td>4K</td>
<td>3,806</td>
<td>2.55s</td>
<td>1,491.1</td>
<td>81.8</td>
<td>2,928.3</td>
<td>158.3</td>
<td>4.21s</td>
</tr>
<tr>
<td>8K</td>
<td>7,512</td>
<td>4.05s</td>
<td>1,854.6</td>
<td>82.0</td>
<td>3,647.9</td>
<td>161.6</td>
<td>5.70s</td>
</tr>
<tr>
<td>16K</td>
<td>14,958</td>
<td>7.56s</td>
<td>1,977.6</td>
<td>91.1</td>
<td>3,919.9</td>
<td>177.3</td>
<td>9.15s</td>
</tr>
<tr>
<td>32K</td>
<td>29,867</td>
<td>14.44s</td>
<td>2,069.0</td>
<td>80.4</td>
<td>4,113.0</td>
<td>160.6</td>
<td>16.30s</td>
</tr>
<tr>
<td>64K</td>
<td>61,948</td>
<td>30.34s</td>
<td>2,041.9</td>
<td>81.6</td>
<td>4,057.9</td>
<td>158.7</td>
<td>32.09s</td>
</tr>
<tr>
<td>128K</td>
<td>124,759</td>
<td>64.15s</td>
<td>1,945.5</td>
<td>48.5</td>
<td>3,822.6</td>
<td>123.1</td>
<td>67.35s</td>
</tr>
<tr>
<td>256K</td>
<td>250,459</td>
<td>126.60s</td>
<td>2,041.0</td>
<td>35.2</td>
<td>3,366.4</td>
<td>135.1</td>
<td>150.69s</td>
</tr>
<tr>
<td>512K</td>
<td>501,900</td>
<td>283.78s</td>
<td>1,897.1</td>
<td>44.3</td>
<td>2,806.8</td>
<td>174.6</td>
<td>359.10s</td>
</tr>
</tbody>
</table>
<h4>并发 4</h4>
<table class="table table-bordered table-striped">
<thead>
<tr>
<th>上下文</th>
<th>prompt/请求</th>
<th>TTFT</th>
<th>单流 pp</th>
<th>单流 tg</th>
<th>聚合 pp</th>
<th>聚合 tg</th>
<th>墙钟</th>
</tr>
</thead>
<tbody>
<tr>
<td>1K</td>
<td>1,002</td>
<td>2.05s</td>
<td>489.9</td>
<td>59.4</td>
<td>1,926.3</td>
<td>216.2</td>
<td>4.55s</td>
</tr>
<tr>
<td>4K</td>
<td>3,802</td>
<td>4.42s</td>
<td>860.9</td>
<td>63.9</td>
<td>3,387.1</td>
<td>252.9</td>
<td>6.51s</td>
</tr>
<tr>
<td>8K</td>
<td>7,512</td>
<td>8.10s</td>
<td>927.8</td>
<td>67.8</td>
<td>3,655.8</td>
<td>261.4</td>
<td>10.18s</td>
</tr>
<tr>
<td>16K</td>
<td>14,960</td>
<td>14.70s</td>
<td>1,017.9</td>
<td>68.8</td>
<td>4,019.5</td>
<td>262.9</td>
<td>16.83s</td>
</tr>
<tr>
<td>32K</td>
<td>29,862</td>
<td>28.64s</td>
<td>1,043.4</td>
<td>66.2</td>
<td>4,118.5</td>
<td>281.1</td>
<td>30.89s</td>
</tr>
<tr>
<td>64K</td>
<td>61,922</td>
<td>57.51s</td>
<td>1,095.4</td>
<td>39.5</td>
<td>3,989.4</td>
<td>199.6</td>
<td>64.60s</td>
</tr>
<tr>
<td>128K</td>
<td>124,762</td>
<td>105.04s</td>
<td>1,295.0</td>
<td>26.7</td>
<td>3,795.9</td>
<td>237.3</td>
<td>133.63s</td>
</tr>
<tr>
<td>256K</td>
<td>250,490</td>
<td>205.25s</td>
<td>1,414.4</td>
<td>23.3</td>
<td>3,445.7</td>
<td>348.2</td>
<td>292.26s</td>
</tr>
<tr>
<td>512K</td>
<td>501,851</td>
<td>462.04s</td>
<td>1,343.9</td>
<td>20.8</td>
<td>2,843.2</td>
<td>313.2</td>
<td>707.67s</td>
</tr>
</tbody>
</table>
<h4>并发 8（满并发，= <code>max-num-seqs</code>）</h4>
<table class="table table-bordered table-striped">
<thead>
<tr>
<th>上下文</th>
<th>prompt/请求</th>
<th>TTFT</th>
<th>单流 pp</th>
<th>单流 tg</th>
<th>聚合 pp</th>
<th>聚合 tg</th>
<th>墙钟</th>
</tr>
</thead>
<tbody>
<tr>
<td>1K</td>
<td>1,002</td>
<td>3.42s</td>
<td>296.7</td>
<td>47.4</td>
<td>2,282.3</td>
<td>358.0</td>
<td>6.62s</td>
</tr>
<tr>
<td>4K</td>
<td>3,800</td>
<td>8.43s</td>
<td>450.8</td>
<td>47.9</td>
<td>3,534.4</td>
<td>361.0</td>
<td>11.44s</td>
</tr>
<tr>
<td>8K</td>
<td>7,513</td>
<td>15.06s</td>
<td>499.1</td>
<td>48.5</td>
<td>3,922.9</td>
<td>403.2</td>
<td>17.84s</td>
</tr>
<tr>
<td>16K</td>
<td>14,963</td>
<td>29.03s</td>
<td>515.6</td>
<td>50.6</td>
<td>4,059.0</td>
<td>398.3</td>
<td>32.06s</td>
</tr>
<tr>
<td>32K</td>
<td>29,872</td>
<td>52.33s</td>
<td>586.0</td>
<td>35.7</td>
<td>4,084.0</td>
<td>463.7</td>
<td>60.68s</td>
</tr>
<tr>
<td>64K</td>
<td>61,930</td>
<td>93.28s</td>
<td>749.2</td>
<td>21.0</td>
<td>4,032.3</td>
<td>426.6</td>
<td>125.03s</td>
</tr>
<tr>
<td>128K</td>
<td>124,755</td>
<td>171.68s</td>
<td>903.9</td>
<td>12.9</td>
<td>3,851.2</td>
<td>527.0</td>
<td>261.09s</td>
</tr>
<tr>
<td>256K</td>
<td>250,465</td>
<td>356.33s</td>
<td>950.1</td>
<td>9.3</td>
<td>3,432.7</td>
<td>490.0</td>
<td>585.80s</td>
</tr>
<tr>
<td>512K</td>
<td>501,905</td>
<td>815.73s</td>
<td>887.3</td>
<td>11.8</td>
<td>2,860.1</td>
<td>672.8</td>
<td>1,405.41s</td>
</tr>
</tbody>
</table>
<hr />
<h2>五、汇总对照</h2>
<p dir="auto"><strong>聚合解码吞吐（tokens/s）</strong></p>
<table class="table table-bordered table-striped">
<thead>
<tr>
<th>上下文</th>
<th>并发 1</th>
<th>并发 2</th>
<th>并发 4</th>
<th>并发 8</th>
</tr>
</thead>
<tbody>
<tr>
<td>1K</td>
<td>55.3</td>
<td>165.8</td>
<td>216.2</td>
<td>358.0</td>
</tr>
<tr>
<td>4K</td>
<td>71.5</td>
<td>158.3</td>
<td>252.9</td>
<td>361.0</td>
</tr>
<tr>
<td>8K</td>
<td>73.0</td>
<td>161.6</td>
<td>261.4</td>
<td>403.2</td>
</tr>
<tr>
<td>16K</td>
<td>83.7</td>
<td>177.3</td>
<td>262.9</td>
<td>398.3</td>
</tr>
<tr>
<td>32K</td>
<td>92.7</td>
<td>160.6</td>
<td>281.1</td>
<td>463.7</td>
</tr>
<tr>
<td>64K</td>
<td>102.6</td>
<td>158.7</td>
<td>199.6</td>
<td>426.6</td>
</tr>
<tr>
<td>128K</td>
<td>67.4</td>
<td>123.1</td>
<td>237.3</td>
<td>527.0</td>
</tr>
<tr>
<td>256K</td>
<td>75.5</td>
<td>135.1</td>
<td>348.2</td>
<td>490.0</td>
</tr>
<tr>
<td>512K</td>
<td>73.0</td>
<td>174.6</td>
<td>313.2</td>
<td>672.8</td>
</tr>
</tbody>
</table>
<p dir="auto"><strong>单流解码吞吐（tokens/s）</strong></p>
<table class="table table-bordered table-striped">
<thead>
<tr>
<th>上下文</th>
<th>并发 1</th>
<th>并发 2</th>
<th>并发 4</th>
<th>并发 8</th>
</tr>
</thead>
<tbody>
<tr>
<td>1K</td>
<td>55.3</td>
<td>83.9</td>
<td>59.4</td>
<td>47.4</td>
</tr>
<tr>
<td>4K</td>
<td>71.5</td>
<td>81.8</td>
<td>63.9</td>
<td>47.9</td>
</tr>
<tr>
<td>8K</td>
<td>73.0</td>
<td>82.0</td>
<td>67.8</td>
<td>48.5</td>
</tr>
<tr>
<td>16K</td>
<td>83.7</td>
<td>91.1</td>
<td>68.8</td>
<td>50.6</td>
</tr>
<tr>
<td>32K</td>
<td>92.7</td>
<td>80.4</td>
<td>66.2</td>
<td>35.7</td>
</tr>
<tr>
<td>64K</td>
<td>102.6</td>
<td>81.6</td>
<td>39.5</td>
<td>21.0</td>
</tr>
<tr>
<td>128K</td>
<td>67.4</td>
<td>48.5</td>
<td>26.7</td>
<td>12.9</td>
</tr>
<tr>
<td>256K</td>
<td>75.5</td>
<td>35.2</td>
<td>23.3</td>
<td>9.3</td>
</tr>
<tr>
<td>512K</td>
<td>73.0</td>
<td>44.3</td>
<td>20.8</td>
<td>11.8</td>
</tr>
</tbody>
</table>
<p dir="auto"><strong>聚合 prefill 吞吐（tokens/s）</strong></p>
<table class="table table-bordered table-striped">
<thead>
<tr>
<th>上下文</th>
<th>并发 1</th>
<th>并发 2</th>
<th>并发 4</th>
<th>并发 8</th>
</tr>
</thead>
<tbody>
<tr>
<td>1K</td>
<td>821.3</td>
<td>1,378.7</td>
<td>1,926.3</td>
<td>2,282.3</td>
</tr>
<tr>
<td>4K</td>
<td>2,064.3</td>
<td>2,928.3</td>
<td>3,387.1</td>
<td>3,534.4</td>
</tr>
<tr>
<td>8K</td>
<td>2,427.1</td>
<td>3,647.9</td>
<td>3,655.8</td>
<td>3,922.9</td>
</tr>
<tr>
<td>16K</td>
<td>3,403.2</td>
<td>3,919.9</td>
<td>4,019.5</td>
<td>4,059.0</td>
</tr>
<tr>
<td>32K</td>
<td>4,006.8</td>
<td>4,113.0</td>
<td>4,118.5</td>
<td>4,084.0</td>
</tr>
<tr>
<td>64K</td>
<td>4,048.1</td>
<td>4,057.9</td>
<td>3,989.4</td>
<td>4,032.3</td>
</tr>
<tr>
<td>128K</td>
<td>3,750.3</td>
<td>3,822.6</td>
<td>3,795.9</td>
<td>3,851.2</td>
</tr>
<tr>
<td>256K</td>
<td>3,417.4</td>
<td>3,366.4</td>
<td>3,445.7</td>
<td>3,432.7</td>
</tr>
<tr>
<td>512K</td>
<td>2,766.6</td>
<td>2,806.8</td>
<td>2,843.2</td>
<td>2,860.1</td>
</tr>
</tbody>
</table>
<p dir="auto"><strong>几条结论</strong>：</p>
<ul>
<li><strong>Decode 随并发放大更明显</strong>：满并发 8 路聚合解码从 1K 的 358.0 涨到 8K–64K 的 <strong>398.3–463.7 tok/s</strong>，512K 达到 <strong>672.8</strong>。</li>
<li><strong>单流解码不再被并发拖死</strong>：并发 8 下 8K / 16K 仍有 <strong>48.5 / 50.6 tok/s</strong>（上一版同档为 16.9 / 12.6）。</li>
<li><strong>Prefill 与并发无关</strong>：1K→128K 区间聚合 prefill 稳定在 <strong>3.5k–4.1k tok/s</strong>，四档并发几乎重合，512K 降到 <strong>2,860.1</strong>——瓶颈仍是单请求长序列 attention，不是环带宽。</li>
<li><strong>128 tokens 的短输出会低估真实吞吐</strong>：单流 tg 含首几步调度开销，实际长任务会更好。</li>
</ul>
<hr />
<h2>六、与上一版（旧 checkpoint）的性能对比小结</h2>
<p dir="auto"><strong>① 聚合解码吞吐 —— 并发 ≥2 时明显胜出；并发 1 与 128K 以上退步</strong></p>
<table class="table table-bordered table-striped">
<thead>
<tr>
<th>上下文</th>
<th>并发 1</th>
<th>并发 2</th>
<th>并发 4</th>
<th>并发 8</th>
</tr>
</thead>
<tbody>
<tr>
<td>1K</td>
<td>91.0→<strong>55.3</strong> (-39%)</td>
<td>110.4→<strong>165.8</strong> (+50%)</td>
<td>189.3→<strong>216.2</strong> (+14%)</td>
<td>280.7→<strong>358.0</strong> (+28%)</td>
</tr>
<tr>
<td>4K</td>
<td>96.5→<strong>71.5</strong> (-26%)</td>
<td>143.6→<strong>158.3</strong> (+10%)</td>
<td>199.0→<strong>252.9</strong> (+27%)</td>
<td>256.9→<strong>361.0</strong> (+41%)</td>
</tr>
<tr>
<td>8K</td>
<td>89.4→<strong>73.0</strong> (-18%)</td>
<td>135.4→<strong>161.6</strong> (+19%)</td>
<td>193.3→<strong>261.4</strong> (+35%)</td>
<td>268.7→<strong>403.2</strong> (+50%)</td>
</tr>
<tr>
<td>16K</td>
<td>75.1→<strong>83.7</strong> (+11%)</td>
<td>127.8→<strong>177.3</strong> (+39%)</td>
<td>206.1→<strong>262.9</strong> (+28%)</td>
<td>280.3→<strong>398.3</strong> (+42%)</td>
</tr>
<tr>
<td>32K</td>
<td>74.9→<strong>92.7</strong> (+24%)</td>
<td>107.5→<strong>160.6</strong> (+49%)</td>
<td>185.8→<strong>281.1</strong> (+51%)</td>
<td>335.6→<strong>463.7</strong> (+38%)</td>
</tr>
<tr>
<td>64K</td>
<td>90.7→<strong>102.6</strong> (+13%)</td>
<td>153.7→158.7 (+3%)</td>
<td>237.4→<strong>199.6</strong> (-16%)</td>
<td>437.9→426.6 (-3%)</td>
</tr>
<tr>
<td>128K</td>
<td>86.7→<strong>67.4</strong> (-22%)</td>
<td>137.8→<strong>123.1</strong> (-11%)</td>
<td>302.8→<strong>237.3</strong> (-22%)</td>
<td>576.3→<strong>527.0</strong> (-9%)</td>
</tr>
<tr>
<td>256K</td>
<td>107.9→<strong>75.5</strong> (-30%)</td>
<td>134.9→135.1 (+0%)</td>
<td>339.4→348.2 (+3%)</td>
<td>574.0→<strong>490.0</strong> (-15%)</td>
</tr>
<tr>
<td>512K</td>
<td>79.0→<strong>73.0</strong> (-8%)</td>
<td>171.8→174.6 (+2%)</td>
<td>379.3→<strong>313.2</strong> (-17%)</td>
<td>703.5→672.8 (-4%)</td>
</tr>
</tbody>
</table>
<p dir="auto"><strong>② 单流解码吞吐 —— 并发 ≥2 时大幅提升，并发 1 的中小上下文退步</strong></p>
<table class="table table-bordered table-striped">
<thead>
<tr>
<th>上下文</th>
<th>并发 1</th>
<th>并发 2</th>
<th>并发 4</th>
<th>并发 8</th>
</tr>
</thead>
<tbody>
<tr>
<td>1K</td>
<td>91.0→<strong>55.3</strong> (-39%)</td>
<td>55.2→<strong>83.9</strong> (+52%)</td>
<td>50.5→<strong>59.4</strong> (+18%)</td>
<td>35.0→<strong>47.4</strong> (+35%)</td>
</tr>
<tr>
<td>4K</td>
<td>96.5→<strong>71.5</strong> (-26%)</td>
<td>60.3→<strong>81.8</strong> (+36%)</td>
<td>40.1→<strong>63.9</strong> (+59%)</td>
<td>25.3→<strong>47.9</strong> (+89%)</td>
</tr>
<tr>
<td>8K</td>
<td>89.4→<strong>73.0</strong> (-18%)</td>
<td>52.4→<strong>82.0</strong> (+56%)</td>
<td>30.1→<strong>67.8</strong> (+125%)</td>
<td>16.9→<strong>48.5</strong> (+187%)</td>
</tr>
<tr>
<td>16K</td>
<td>75.1→<strong>83.7</strong> (+11%)</td>
<td>44.3→<strong>91.1</strong> (+106%)</td>
<td>24.5→<strong>68.8</strong> (+181%)</td>
<td>12.6→<strong>50.6</strong> (+302%)</td>
</tr>
<tr>
<td>32K</td>
<td>74.9→<strong>92.7</strong> (+24%)</td>
<td>34.1→<strong>80.4</strong> (+136%)</td>
<td>18.7→<strong>66.2</strong> (+254%)</td>
<td>10.4→<strong>35.7</strong> (+243%)</td>
</tr>
<tr>
<td>64K</td>
<td>90.7→<strong>102.6</strong> (+13%)</td>
<td>42.7→<strong>81.6</strong> (+91%)</td>
<td>18.4→<strong>39.5</strong> (+115%)</td>
<td>9.7→<strong>21.0</strong> (+116%)</td>
</tr>
<tr>
<td>128K</td>
<td>86.7→<strong>67.4</strong> (-22%)</td>
<td>36.5→<strong>48.5</strong> (+33%)</td>
<td>20.9→<strong>26.7</strong> (+28%)</td>
<td>10.8→<strong>12.9</strong> (+19%)</td>
</tr>
<tr>
<td>256K</td>
<td>107.9→<strong>75.5</strong> (-30%)</td>
<td>34.6→35.2 (+2%)</td>
<td>22.4→23.3 (+4%)</td>
<td>10.4→<strong>9.3</strong> (-11%)</td>
</tr>
<tr>
<td>512K</td>
<td>79.0→<strong>73.0</strong> (-8%)</td>
<td>43.7→44.3 (+1%)</td>
<td>24.8→<strong>20.8</strong> (-16%)</td>
<td>12.3→11.8 (-4%)</td>
</tr>
</tbody>
</table>
<p dir="auto"><strong>③ 首字延迟 TTFT —— 长上下文明显更快，短上下文更慢</strong></p>
<table class="table table-bordered table-striped">
<thead>
<tr>
<th>上下文</th>
<th>并发 1</th>
<th>并发 2</th>
<th>并发 4</th>
<th>并发 8</th>
</tr>
</thead>
<tbody>
<tr>
<td>1K</td>
<td>0.47s→<strong>1.22s</strong> (+160%)</td>
<td>0.64s→<strong>1.43s</strong> (+123%)</td>
<td>1.06s→<strong>2.05s</strong> (+93%)</td>
<td>2.27s→<strong>3.42s</strong> (+51%)</td>
</tr>
<tr>
<td>4K</td>
<td>0.99s→<strong>1.84s</strong> (+86%)</td>
<td>2.02s→<strong>2.55s</strong> (+26%)</td>
<td>3.65s→<strong>4.42s</strong> (+21%)</td>
<td>7.30s→<strong>8.43s</strong> (+15%)</td>
</tr>
<tr>
<td>8K</td>
<td>1.76s→<strong>3.10s</strong> (+76%)</td>
<td>3.52s→<strong>4.05s</strong> (+15%)</td>
<td>7.02s→<strong>8.10s</strong> (+15%)</td>
<td>14.24s→<strong>15.06s</strong> (+6%)</td>
</tr>
<tr>
<td>16K</td>
<td>3.43s→<strong>4.39s</strong> (+28%)</td>
<td>6.92s→<strong>7.56s</strong> (+9%)</td>
<td>14.87s→14.70s (-1%)</td>
<td>28.80s→29.03s (+1%)</td>
</tr>
<tr>
<td>32K</td>
<td>7.93s→<strong>7.46s</strong> (-6%)</td>
<td>14.02s→14.44s (+3%)</td>
<td>28.89s→28.64s (-1%)</td>
<td>57.46s→<strong>52.33s</strong> (-9%)</td>
</tr>
<tr>
<td>64K</td>
<td>14.35s→<strong>15.30s</strong> (+7%)</td>
<td>28.28s→<strong>30.34s</strong> (+7%)</td>
<td>59.54s→57.51s (-3%)</td>
<td>117.68s→<strong>93.28s</strong> (-21%)</td>
</tr>
<tr>
<td>128K</td>
<td>32.14s→33.27s (+4%)</td>
<td>62.84s→64.15s (+2%)</td>
<td>125.14s→<strong>105.04s</strong> (-16%)</td>
<td>253.92s→<strong>171.68s</strong> (-32%)</td>
</tr>
<tr>
<td>256K</td>
<td>71.61s→73.30s (+2%)</td>
<td>146.02s→<strong>126.60s</strong> (-13%)</td>
<td>288.52s→<strong>205.25s</strong> (-29%)</td>
<td>571.94s→<strong>356.33s</strong> (-38%)</td>
</tr>
<tr>
<td>512K</td>
<td>175.55s→181.40s (+3%)</td>
<td>349.99s→<strong>283.78s</strong> (-19%)</td>
<td>695.75s→<strong>462.04s</strong> (-34%)</td>
<td>1,396.56s→<strong>815.73s</strong> (-42%)</td>
</tr>
</tbody>
</table>
<p dir="auto"><strong>④ 聚合 prefill 吞吐 —— 本版唯一整体退步项</strong></p>
<table class="table table-bordered table-striped">
<thead>
<tr>
<th>上下文</th>
<th>并发 1</th>
<th>并发 2</th>
<th>并发 4</th>
<th>并发 8</th>
</tr>
</thead>
<tbody>
<tr>
<td>1K</td>
<td>2,363→<strong>821</strong> (-65%)</td>
<td>3,443→<strong>1,379</strong> (-60%)</td>
<td>4,137→<strong>1,926</strong> (-53%)</td>
<td>3,888→<strong>2,282</strong> (-41%)</td>
</tr>
<tr>
<td>4K</td>
<td>4,119→<strong>2,064</strong> (-50%)</td>
<td>4,067→<strong>2,928</strong> (-28%)</td>
<td>4,487→<strong>3,387</strong> (-25%)</td>
<td>4,504→<strong>3,534</strong> (-22%)</td>
</tr>
<tr>
<td>8K</td>
<td>4,529→<strong>2,427</strong> (-46%)</td>
<td>4,525→<strong>3,648</strong> (-19%)</td>
<td>4,580→<strong>3,656</strong> (-20%)</td>
<td>4,565→<strong>3,923</strong> (-14%)</td>
</tr>
<tr>
<td>16K</td>
<td>4,570→<strong>3,403</strong> (-26%)</td>
<td>4,580→<strong>3,920</strong> (-14%)</td>
<td>4,262→<strong>4,020</strong> (-6%)</td>
<td>4,402→<strong>4,059</strong> (-8%)</td>
</tr>
<tr>
<td>32K</td>
<td>4,002→4,007 (+0%)</td>
<td>4,482→<strong>4,113</strong> (-8%)</td>
<td>4,327→4,118 (-5%)</td>
<td>4,352→<strong>4,084</strong> (-6%)</td>
</tr>
<tr>
<td>64K</td>
<td>4,426→<strong>4,048</strong> (-9%)</td>
<td>4,402→<strong>4,058</strong> (-8%)</td>
<td>4,203→<strong>3,989</strong> (-5%)</td>
<td>4,253→<strong>4,032</strong> (-5%)</td>
</tr>
<tr>
<td>128K</td>
<td>3,976→<strong>3,750</strong> (-6%)</td>
<td>4,026→<strong>3,823</strong> (-5%)</td>
<td>4,024→<strong>3,796</strong> (-6%)</td>
<td>3,966→3,851 (-3%)</td>
</tr>
<tr>
<td>256K</td>
<td>3,580→3,417 (-5%)</td>
<td>3,511→3,366 (-4%)</td>
<td>3,519→3,446 (-2%)</td>
<td>3,550→3,433 (-3%)</td>
</tr>
<tr>
<td>512K</td>
<td>2,924→<strong>2,767</strong> (-5%)</td>
<td>2,905→2,807 (-3%)</td>
<td>2,922→2,843 (-3%)</td>
<td>2,904→2,860 (-2%)</td>
</tr>
</tbody>
</table>
<h3>对比要点</h3>
<ul>
<li><strong>并发能力是这版的核心提升</strong>：并发 8 下 8K–32K 区间聚合解码提升 <strong>+38% ~ +50%</strong>，并发 4 下 4K–32K 提升 <strong>+27% ~ +51%</strong>。上一版满并发时单流被压到 9.7–16.9 tok/s（8K–64K），本版同档有 21.0–48.5 tok/s。</li>
<li><strong>超长上下文 prefill 更快</strong>：512K 并发 8 的 TTFT 从 1,396.56s 降到 <strong>815.73s（-42%）</strong>，128K/256K 分别 -32% / -38%。</li>
<li><strong>短 prompt、低并发是明确退步</strong>：并发 1 的 1K/4K/8K 单流解码 91.0→55.3、96.5→71.5、89.4→73.0 tok/s；TTFT 1K 档 0.47s→1.22s。聚合 prefill 因此在小上下文掉 65% 左右。</li>
<li><strong>墙钟基本持平</strong>：同规格任务的总耗时两版差异在 −3% ~ +3% 之间，最大差异出现在并发 8 × 512K（1,398s → 1,405s）。</li>
</ul>
<hr />
<h2>七、两个反直觉的测量发现（供同好参考）</h2>
<ol>
<li><strong>每轮第一笔请求有约 2.2 秒冷启开销，稳态只要 0.98 秒。</strong> 同一个 1000-token 请求连测 8 次，首字延迟稳定在 <strong>0.96–0.99 秒（标准差 0.01）</strong>；而每次重新开跑的第一个 cell 会达到 2.2–2.5 秒。如果只做单次采样，就会把这个冷启算进表里——上一版的 1K 档 0.47s 和本版单采样的 2.2s 都不代表稳态。</li>
<li><strong>TTFT 可以拟合成一条直线</strong>：阶梯实验（160 / 997 / 3,719 / 7,365 / 14,640 prompt tokens）得到 <code>TTFT ≈ 0.8s 固定 + prompt_tokens / 4,700 (t/s)</code>。也就是说这版有约 <strong>0.8 秒的固定首字成本</strong>，短 prompt 时被放大成几倍差距，长 prompt 时完全被淹没——这也解释了为什么 prefill 吞吐在小上下文退步、在 128K 以上反而变好。</li>
</ol>
<hr />
<h2>八、结论</h2>
<ol>
<li><strong>1017 版是「高并发 / 长上下文」取向的版本。</strong> 并发 8 路时聚合解码比上一版高 <strong>+38% ~ +50%</strong>（8K–32K），单流也不再被并发压死；超长上下文（128K–512K）并发 8 的 TTFT 快 <strong>32%–42%</strong>。</li>
<li><strong>代价是短 prompt / 低并发的首字延迟</strong>：约 0.8 秒固定开销让 1K–8K 的 prefill 吞吐掉 40%+，单流解码在并发 1 时也从 ~90 掉到 ~55–73 tok/s。</li>
<li><strong>实际选型建议</strong>：如果负载是「多轮 agent、工具调用、批量并发」（典型如 Hermes 这类前端），1017 更合适，尤其是并发 ≥2 之后；如果是「单用户、短问短答、极在意首字延迟」，旧版在同硬件上反而更顺手。</li>
<li><strong>环拓扑本身没有任何变化</strong>：四机 4 根 DAC、无交换机、SIRCL 传输，512K 满并发 1,405 秒跑完、全程无 OOM。换 checkpoint 不影响这套拓扑的可用性。</li>
</ol>
]]></description><link>https://lcz.me/topic/2946</link><generator>RSS for Node</generator><lastBuildDate>Tue, 10 Nov 2026 01:11:37 GMT</lastBuildDate><atom:link href="https://lcz.me/topic/2946.rss" rel="self" type="application/rss+xml"/><pubDate>Mon, 09 Nov 2026 16:10:11 GMT</pubDate><ttl>60</ttl><item><title><![CDATA[Reply to 4 DGX Spark DeepSeek V4.1 Flash 1017版本详细测试数据！ on Tue, 10 Nov 2026 01:07:43 GMT]]></title><description><![CDATA[<p dir="auto">NVIDIA 在AI 界还是太权威了</p>
]]></description><link>https://lcz.me/post/32297</link><guid isPermaLink="true">https://lcz.me/post/32297</guid><dc:creator><![CDATA[张光璞]]></dc:creator><pubDate>Tue, 10 Nov 2026 01:07:43 GMT</pubDate></item><item><title><![CDATA[Reply to 4 DGX Spark DeepSeek V4.1 Flash 1017版本详细测试数据！ on Mon, 09 Nov 2026 16:31:21 GMT]]></title><description><![CDATA[<p dir="auto"><a class="plugin-mentions-user plugin-mentions-a" href="/user/xiongjie-gao" aria-label="Profile: xiongjie-gao">@<bdi>xiongjie-gao</bdi></a> 我已经开启装逼模式，视频马上录制！<img src="https://lcz.me/assets/plugins/nodebb-plugin-emoji/emoji/android/1f602.png?v=a055419e292" class="not-responsive emoji emoji-android emoji--joy" style="height:1.4em;width:auto;vertical-align:middle" title="😂" alt="😂" /></p>
]]></description><link>https://lcz.me/post/32270</link><guid isPermaLink="true">https://lcz.me/post/32270</guid><dc:creator><![CDATA[terry]]></dc:creator><pubDate>Mon, 09 Nov 2026 16:31:21 GMT</pubDate></item><item><title><![CDATA[Reply to 4 DGX Spark DeepSeek V4.1 Flash 1017版本详细测试数据！ on Mon, 09 Nov 2026 16:25:34 GMT]]></title><description><![CDATA[<p dir="auto">熟读这张表格并牢记结论，可以在绝大多数场合装波大的<img src="https://lcz.me/assets/plugins/nodebb-plugin-emoji/emoji/android/1f60a.png?v=a055419e292" class="not-responsive emoji emoji-android emoji--blush" style="height:1.4em;width:auto;vertical-align:middle" title=":blush:" alt="😊" /></p>
]]></description><link>https://lcz.me/post/32267</link><guid isPermaLink="true">https://lcz.me/post/32267</guid><dc:creator><![CDATA[xiongjie gao]]></dc:creator><pubDate>Mon, 09 Nov 2026 16:25:34 GMT</pubDate></item><item><title><![CDATA[Reply to 4 DGX Spark DeepSeek V4.1 Flash 1017版本详细测试数据！ on Mon, 09 Nov 2026 16:24:24 GMT]]></title><description><![CDATA[<p dir="auto">正好准备做视频，直接水一期，明天又可以玩游戏了。</p>
]]></description><link>https://lcz.me/post/32266</link><guid isPermaLink="true">https://lcz.me/post/32266</guid><dc:creator><![CDATA[terry]]></dc:creator><pubDate>Mon, 09 Nov 2026 16:24:24 GMT</pubDate></item><item><title><![CDATA[Reply to 4 DGX Spark DeepSeek V4.1 Flash 1017版本详细测试数据！ on Mon, 09 Nov 2026 16:23:18 GMT]]></title><description><![CDATA[<p dir="auto">母帖地址：<a href="https://lcz.me/topic/2346">https://lcz.me/topic/2346</a> APP未做解析，贴上来方便浏览。</p>
]]></description><link>https://lcz.me/post/32264</link><guid isPermaLink="true">https://lcz.me/post/32264</guid><dc:creator><![CDATA[terry]]></dc:creator><pubDate>Mon, 09 Nov 2026 16:23:18 GMT</pubDate></item><item><title><![CDATA[Reply to 4 DGX Spark DeepSeek V4.1 Flash 1017版本详细测试数据！ on Mon, 09 Nov 2026 16:21:32 GMT]]></title><description><![CDATA[<p dir="auto"><img src="https://lcz.me/assets/plugins/nodebb-plugin-emoji/emoji/android/2b50.png?v=a055419e292" class="not-responsive emoji emoji-android emoji--star" style="height:1.4em;width:auto;vertical-align:middle" title="⭐" alt="⭐" /> 版主审定：本帖设为<strong>精华</strong>，作者 <strong>+6 积分</strong>奖励，希望再接再厉！</p>
<blockquote>
<p dir="auto">奖励凭证：精华 +6 分 · 编号 f2946-18-1</p>
</blockquote>
]]></description><link>https://lcz.me/post/32263</link><guid isPermaLink="true">https://lcz.me/post/32263</guid><dc:creator><![CDATA[Xiaote]]></dc:creator><pubDate>Mon, 09 Nov 2026 16:21:32 GMT</pubDate></item></channel></rss>