<?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[7900 XTX + 128GB：Strata RDNA3 三档模型精度测试]]></title><description><![CDATA[<p dir="auto">128G ram， 最高跑到UD-Q5_K_XL， 最低测了IQ4_XS-v31 + N-gramQ8. 发出来方便大家参考</p>
<h2>硬件 / 软件环境</h2>
<table class="table table-bordered table-striped">
<thead>
<tr>
<th>项目</th>
<th>环境</th>
</tr>
</thead>
<tbody>
<tr>
<td>CPU</td>
<td>AMD Ryzen 9 5900XT，16核32线程，AVX2</td>
</tr>
<tr>
<td>GPU</td>
<td>AMD Radeon RX 7900 XTX，24GB，gfx1100</td>
</tr>
<tr>
<td>内存</td>
<td>128GB；系统可用约125.7GiB</td>
</tr>
<tr>
<td>模型存储</td>
<td>XG7000 1TB NVMe，ZFS</td>
</tr>
<tr>
<td>系统</td>
<td>TrueNAS 25.10.7，Linux 6.12.105-production+truenas</td>
</tr>
<tr>
<td>AMDGPU</td>
<td>7.1.9.31600000</td>
</tr>
<tr>
<td>ROCm</td>
<td>10.1.0，系统路径 /opt/rocm</td>
</tr>
<tr>
<td>hipBLASLt</td>
<td>100401，匹配 gfx1100 调优表（43行）</td>
</tr>
<tr>
<td>推理后端</td>
<td>Strata RDNA3 fork，具体引擎版本见下表</td>
</tr>
<tr>
<td>GPU 队列</td>
<td>引擎 GPU_MAX_HW_QUEUES=6；视觉助手限制为2</td>
</tr>
<tr>
<td>ZFS ARC</td>
<td>自动管理，zfs_arc_max=0</td>
</tr>
</tbody>
</table>
<h2>文件体积与速度总表</h2>
<p dir="auto">PP = prefill，TG = decode，速度单位均为tokens/s；每格按 <strong>PP / TG</strong> 排列。GB按10⁹字节，括号GiB按2³⁰字节。</p>
<table class="table table-bordered table-striped">
<thead>
<tr>
<th>配置</th>
<th>引擎版本</th>
<th style="text-align:right">原GGUF分片合计 GB（GiB）</th>
<th>实际N-gram / PLE</th>
<th style="text-align:right">GGUF＋所用外置表 GB</th>
<th style="text-align:right">约8K输入 PP / TG</th>
<th>长输入 PP / TG</th>
</tr>
</thead>
<tbody>
<tr>
<td>IQ4_XS-v31</td>
<td>0.1.49.1</td>
<td style="text-align:right">93.67（87.24）</td>
<td>外置Q8_0，54.40GB；mmap；1MiB块</td>
<td style="text-align:right">148.07</td>
<td style="text-align:right">802.3 / 65.5</td>
<td>32K：1126.8 / 62.7；128K：1489.9 / 60.0</td>
</tr>
<tr>
<td>UD-Q4_K_XL</td>
<td>0.1.43</td>
<td style="text-align:right">111.33（103.69）</td>
<td>内置IQ4_NL，28.80GB；ram</td>
<td style="text-align:right">111.33</td>
<td style="text-align:right">2082.7 / 57.4</td>
<td>64,358 tokens：3670.2 / 58.3</td>
</tr>
<tr>
<td>UD-Q5_K_XL</td>
<td>0.1.49.1</td>
<td style="text-align:right">158.29（147.42）</td>
<td>外置Q8_0，54.40GB；direct；16KiB块</td>
<td style="text-align:right">212.69</td>
<td style="text-align:right">1600.5 / 46.0</td>
<td>32K：2368.5 / 50.9；128K：3432.4 / 47.4</td>
</tr>
</tbody>
</table>
<p dir="auto"><strong>体积口径：</strong> 原GGUF分片合计包含包内N-gram。IQ4_XS和Q5_K_XL另外使用独立Q8表，所以额外计入54.40GB；Q4_K_XL直接使用包内28.80GB的IQ4_NL表，不再重复加一次。合计不含native pack、MTP、视觉投影及备用文件，不等于RAM占用。</p>
<p dir="auto"><strong>测速口径：</strong> IQ4_XS与Q5_K_XL取对应最终配置的固定新输入记录，temperature=0、top_k=1、top_p=1、seed=42，各输出384 tokens，禁止prompt复用。Q4_K_XL取最终版本的完整无缓存日常日志：约8K一栏为11条记录的中位数，输入7,961～8,072、输出158～506 tokens；64,358一栏为单次150-token输出。Q4日常请求的实际采样未统一。<strong>不同输入语料、输出长度、PLE方式和引擎版本的结果只作整套配置参考，不作为严格同条件量化排名。</strong></p>
<h2>三套共用参数</h2>
<p dir="auto">以下是启动参数片段，公共段与对应方案段合并使用。模型、pack、MTP和profile路径按各自安装目录填写；数值取实际最终配置。</p>
<pre><code class="language-bash">--pack "$PACK_DIR" \
--native "$MODEL_GGUF" \
--expert-profile "$EXPERT_PROFILE" \
--mtp "$MTP_DIR" \
--expert-cache auto \
--max-context 153600 \
--spec 5 \
--spec-min-p 0.70 \
--mtp-window 4096 \
--mtp-q4 all \
--pcie-frac 0.20 \
--kv int8 \
--kv-resident 32768 \
--pool-tasks 96 \
--ple-row-cache 1048576 \
--prompt-cache 12 \
--prompt-cache-tail \
--adapt-every 1 \
--adapt-decay 0.97 \
--conversation-cache-min-tokens 2000 \
--conversation-cache-min-free-mib 2048 \
--stats \
--vision
</code></pre>
<p dir="auto">公共MTP数据目录为rt格式。三套默认采样均为temperature=0.5、top_p=0.95、top_k=40；客户端显式参数可以覆盖默认值。</p>
<h2>1. IQ4_XS-v31</h2>
<ul>
<li>引擎：RDNA3 0.1.49.1。</li>
<li>主模型：UD-IQ4_XS-v31，4个GGUF分片。</li>
<li>PLE：独立Q8_0表，mmap读取；实际文件为1MiB ZFS数据块。</li>
<li>专家arena约56.24GiB，不含N-gram缓存、KV和系统开销。</li>
</ul>
<pre><code class="language-bash">--pool-workers 12 \
--prefill 32768 \
--vram-reserve-mib 768 \
--ple-io mmap \
--ple-gguf "$Q8_ENGRAM" \
--adapt-swaps 80 \
--conversation-cache-mib 4096 \
--conversation-cache-slots 4 \
--mtp-draft-vocab "$ZH60K_DRAFT_VOCAB"
</code></pre>
<p dir="auto">对应方案环境变量：</p>
<pre><code class="language-bash">export STRATA_PREFILL_CPU_SHARE=0.6
export STRATA_PREFILL_CPU_SHARE_MAX=7936
export STRATA_ADAPT_OVERLAP=1
export STRATA_TOPK_SPLIT_MIN=98304
export STRATA_QSA_ROWS=1
export STRATA_ENGINE_NICE=0
</code></pre>
<h2>2. UD-Q4_K_XL</h2>
<ul>
<li>引擎：RDNA3 0.1.43。</li>
<li>主模型：UD-Q4_K_XL，4个GGUF分片。</li>
<li>PLE：直接使用包内的<strong>IQ4_NL</strong>表，ram模式锁入内存。</li>
<li>专家arena约71.73GiB；加上约26.82GiB的PLE表及其他开销，128GB内存接近满载。</li>
<li>Prefill为auto，最终上限32768；未额外指定mtp-draft-vocab。</li>
</ul>
<pre><code class="language-bash">--pool-workers 12 \
--prefill auto \
--vram-reserve-mib 512 \
--ple-io ram \
--adapt-swaps 160 \
--conversation-cache-mib 2048 \
--conversation-cache-slots 2
</code></pre>
<p dir="auto">对应方案环境变量：</p>
<pre><code class="language-bash">export STRATA_PREFILL_AUTO_MAX=32768
export STRATA_PREFILL_CPU_SHARE=auto
export STRATA_PREFILL_CPU_SHARE_MAX=6144
export STRATA_PREFILL_MMQ_TILE_ROWS=32
export STRATA_PF_SWITCH_MIN_T=4096
export STRATA_WMMA_GEMM=1
</code></pre>
<h2>3. UD-Q5_K_XL（当前在用）</h2>
<ul>
<li>引擎：RDNA3 0.1.49.1。</li>
<li>主模型：UD-Q5_K_XL，6个GGUF分片。</li>
<li>PLE：独立Q8_0表，direct读取；使用<strong>16KiB ZFS数据块</strong>的文件副本。</li>
<li>专家arena约91.61GiB。N-gram表放在NVMe按需读取，不将整张54.40GB表锁入RAM。</li>
<li>所用Q8表与原表字节相同，SHA256校验通过；主模型专家仍完整放在RAM。</li>
</ul>
<pre><code class="language-bash">--pool-workers 10 \
--prefill 32768 \
--vram-reserve-mib 832 \
--ple-io direct \
--ple-gguf "$Q8_ENGRAM_16K" \
--adapt-swaps 160 \
--conversation-cache-mib 4096 \
--conversation-cache-slots 4 \
--mtp-draft-vocab "$ZH60K_DRAFT_VOCAB"
</code></pre>
<p dir="auto">对应方案环境变量：</p>
<pre><code class="language-bash">export STRATA_PREFILL_CPU_SHARE=0.6
export STRATA_PREFILL_CPU_SHARE_MAX=7936
export STRATA_ADAPT_OVERLAP=1
export STRATA_TOPK_SPLIT_MIN=98304
export STRATA_QSA_ROWS=1
export STRATA_ENGINE_NICE=0
</code></pre>
<p dir="auto">当前Q5配置另通过了两轮150,000-token输入检索验证。GPU加载模型后的待机实测为0%占用、约23W。</p>
<h2>共用环境变量</h2>
<p dir="auto">下面是三套配置中取值相同的环境变量；与各方案自己的环境段一起使用。HIPBLASLT_TABLE应指向与本机gfx1100、hipBLASLt 100401相匹配的调优表。</p>
<pre><code class="language-bash">export HIP_PLATFORM=amd
export ROCM_PATH=/opt/rocm
export HIP_PATH=/opt/rocm
export HSA_OVERRIDE_GFX_VERSION=11.0.0
export LD_LIBRARY_PATH=/opt/rocm/lib
export GPU_MAX_HW_QUEUES=6
export STRATA_HIP_WMMA=1
export STRATA_SELECT_WMMA=1
export STRATA_HC_UPMIX=1
export STRATA_HEAD_MIX_MULTI=1
export STRATA_GR_RDNA=1
export STRATA_GR_RDNA_Q8=1
export STRATA_GR_RDNA_A8=1
export STRATA_GR_FUSED=1
export STRATA_GR_INJ_UP=1
export STRATA_GDN_CHUNKED=1
export STRATA_GDN_HEAD=1
export STRATA_GDN_CONVL2=1
export STRATA_GDN_NOY=1
export STRATA_GDN_PP=1
export STRATA_GDN_CHUNK_GDV=0
export STRATA_PF_FUSED=1
export STRATA_PF_GEMM=1
export STRATA_PF_F16=2
export STRATA_PF_HCDOWN=1
export STRATA_PF_PAD=1
export STRATA_PF_GROUP_RES=1
export STRATA_PREFILL_RING=384
export STRATA_QFUSE=1
export STRATA_PA_FAST=1
export STRATA_PA_KLDS=1
export STRATA_KV_HOST_DMA=1
export STRATA_COMBINE_CPU=1
export STRATA_WAIT_FUSE=1
export STRATA_ROUTE_PUBLISH=1
export STRATA_VERIFY_QDEDUP=1
export STRATA_RDNA_MMVQ_WAVE=3
export STRATA_RDNA_Q6K=1
export STRATA_STAGER_RING=64
export STRATA_PEER_TRANSPORT=host
export STRATA_HIPBLASLT_TUNING="$HIPBLASLT_TABLE"
export STRATA_PF_FUSED_KQ=1
export STRATA_FUSE_CONVAB=1
export STRATA_SH_STREAM=0
</code></pre>
<p dir="auto">ZFS的16KiB块需要对应文件实际以该块大小写入；改变dataset的recordsize不会自动改变旧文件的数据块。当前整个models dataset仍为1MiB，Q5所用独立Q8表副本为16KiB。</p>
<p dir="auto">项目：<a href="https://github.com/nicejunjie/Strata-rdna3" rel="nofollow ugc">Strata RDNA3</a>；模型：<a href="https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF" rel="nofollow ugc">Unsloth Qwen3.8-Flash-Next GGUF</a>。</p>
]]></description><link>https://lcz.me/topic/2922</link><generator>RSS for Node</generator><lastBuildDate>Mon, 09 Nov 2026 05:25:37 GMT</lastBuildDate><atom:link href="https://lcz.me/topic/2922.rss" rel="self" type="application/rss+xml"/><pubDate>Sun, 08 Nov 2026 17:49:22 GMT</pubDate><ttl>60</ttl><item><title><![CDATA[Reply to 7900 XTX + 128GB：Strata RDNA3 三档模型精度测试 on Mon, 09 Nov 2026 01:15:56 GMT]]></title><description><![CDATA[<p dir="auto"><a class="plugin-mentions-user plugin-mentions-a" href="/user/terry" aria-label="Profile: terry">@<bdi>terry</bdi></a> 我也不知道是哪里， codex说它改好了。。。</p>
]]></description><link>https://lcz.me/post/32007</link><guid isPermaLink="true">https://lcz.me/post/32007</guid><dc:creator><![CDATA[J Z 0]]></dc:creator><pubDate>Mon, 09 Nov 2026 01:15:56 GMT</pubDate></item><item><title><![CDATA[Reply to 7900 XTX + 128GB：Strata RDNA3 三档模型精度测试 on Sun, 08 Nov 2026 19:35:18 GMT]]></title><description><![CDATA[<p dir="auto">有些地方用了HTML标记，让AI换成Markdown</p>
]]></description><link>https://lcz.me/post/31986</link><guid isPermaLink="true">https://lcz.me/post/31986</guid><dc:creator><![CDATA[terry]]></dc:creator><pubDate>Sun, 08 Nov 2026 19:35:18 GMT</pubDate></item><item><title><![CDATA[Reply to 7900 XTX + 128GB：Strata RDNA3 三档模型精度测试 on Sun, 08 Nov 2026 19:33:44 GMT]]></title><description><![CDATA[<p dir="auto"><img src="https://lcz.me/assets/plugins/nodebb-plugin-emoji/emoji/android/2705.png?v=5be5614064c" class="not-responsive emoji emoji-android emoji--white_check_mark" style="height:1.4em;width:auto;vertical-align:middle" title="✅" alt="✅" /> 版主审定：本帖设为<strong>优秀</strong>，作者 <strong>+3 积分</strong>奖励，继续加油！</p>
<blockquote>
<p dir="auto">奖励凭证：优秀 +3 分 · 编号 e2922-3843-1</p>
</blockquote>
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