[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f1rlzhjzggg583":3},{"slug":4,"category":5,"publishedAt":6,"titleZh":7,"titleEn":8,"summaryZh":9,"summaryEn":10,"models":11,"hardwares":15,"bodyZh":16,"bodyEn":17,"sourceName":18,"sourceUrl":19,"updatedAt":20},"strata-vs-llama-cpp","NEWS","2026-10-05T04:36:00.000Z","同硬件下 Strata 65 tok\u002Fs vs llama.cpp 15 tok\u002Fs：差距从哪来？","Same hardware, Strata 65 tok\u002Fs vs llama.cpp 15 tok\u002Fs — where does the gap come from?","在 RTX 5070 12GB + 64GB DDR5 同配置下，Strata 跑 Qwen3.8-Flash-Next IQ2_XS 约 65.1 tok\u002Fs，llama.cpp 约 15 tok\u002Fs。差距主要来自 SSD 流式权重 + MTP 推测 + AVX-512 VNNI 内核三件套。","On RTX 5070 12GB + 64GB DDR5, Strata hits ~65.1 tok\u002Fs vs llama.cpp's ~15 tok\u002Fs on Qwen3.8-Flash-Next IQ2_XS — the gap comes from SSD-streamed weights, MTP speculation, and AVX-512 VNNI kernels.",[12],{"id":13,"name":14},11,"Qwen3.8-Flash-Next",[],"### 是什么\n\n同一台 RTX 5070 12GB + Ryzen 5 7600 + 64GB DDR5-5600 工作站，**Strata 与 llama.cpp 跑 Qwen3.8-Flash-Next IQ2_XS 的差距是 4.3 倍**：\n\n| 框架 | decode | prompt 处理 |\n|---|---|---|\n| Strata | ~65.1 tok\u002Fs | ~2,090 tok\u002Fs |\n| llama.cpp | ~15 tok\u002Fs | ~330 tok\u002Fs |\n| **差距** | **4.3×** | **6.3×** |\n\n### 差距的三个来源\n\n1. **SSD 流式权重**：llama.cpp 默认要求权重全部在 RAM 里，遇到 180B + 64GB RAM 装不下就只能大量 CPU↔RAM 抖动；Strata 把权重分层到 SSD（28.8GB n-gram + 64GB RAM 主专家），避开了这一抖动\n\n2. **MTP 推测解码**：Qwen3.8-Flash-Next 自带 draft layer，Strata 直接消费它做 1.6–1.8× 推测；llama.cpp 的 speculative decoding 还在实验分支，主线没合并\n\n3. **AVX-512 VNNI 内核**：Strata 为 Q2_0 量化写了专用 CPU 解压路径，吃满现代桌面 CPU 的 SIMD 带宽；llama.cpp 的量化矩阵 kernel 偏通用，VNNI 加速不充分\n\n### 为什么 llama.cpp 慢不一定是\"落后\"\n\nllama.cpp 的设计目标是**通用性 + 跨平台**——从 4-bit ARM CPU 到 Apple Silicon 到 NVIDIA 消费卡都跑。Strata 专攻 Qwen3.8-Flash-Next + 桌面 CPU\u002FGPU 这一个细分场景，把所有优化都砸在它身上。这种\"专才 vs 通才\"的取舍，在 12GB 跑 180B 这种边缘场景里差距会被放大到 4 倍。\n\n### 选择建议\n\n- 跑 Qwen3.8-Flash-Next 180B 在 12–16GB 显卡 + 64GB RAM：选 **Strata**\n- 跑 Llama、Mistral、DeepSeek 等其他模型：选 **llama.cpp**（生态最广）\n- 跑多模型工作流（同时 5+ 个不同模型）：选 **llama.cpp \u002F Ollama**\n- 跑多卡 A100\u002FH100 服务端：选 **vLLM \u002F SGLang**\n\n### 本地部署角度\n\nStrata 仅针对 Qwen3.8-Flash-Next 优化（含未来的 Qwen4 代号 qwen4exp 系列）。如果你只用这一代 Qwen Flash 档，Strata 是桌面侧最强方案；如果还要跑别的模型，llama.cpp 更稳。","### What it is\n\nOn the same RTX 5070 12GB + Ryzen 5 7600 + 64GB DDR5-5600 workstation, the **gap between Strata and llama.cpp on Qwen3.8-Flash-Next IQ2_XS is 4.3×**:\n\n| Framework | decode | prompt |\n|---|---|---|\n| Strata | ~65.1 tok\u002Fs | ~2,090 tok\u002Fs |\n| llama.cpp | ~15 tok\u002Fs | ~330 tok\u002Fs |\n| **gap** | **4.3×** | **6.3×** |\n\n### Three sources of the gap\n\n1. **SSD-streamed weights**: llama.cpp assumes weights must all live in RAM; with 180B and 64GB RAM it doesn't fit, causing heavy CPU↔RAM thrashing. Strata tiers weights to SSD (28.8GB n-gram + 64GB RAM for hot experts) and avoids that thrash.\n2. **MTP speculative decoding**: Qwen3.8-Flash-Next ships with a built-in draft layer; Strata consumes it directly for 1.6–1.8× speculation. llama.cpp's speculative decoding is still in an experimental branch, not on main.\n3. **AVX-512 VNNI kernels**: Strata has dedicated CPU unpacking paths for Q2_0 quantization, saturating modern desktop SIMD bandwidth. llama.cpp's quantization matrix kernels are more general-purpose; VNNI acceleration is under-used.\n\n### Why llama.cpp being slower ≠ \"lagging\"\n\nllama.cpp's design goal is **generality + cross-platform** — runs on everything from 4-bit ARM CPUs to Apple Silicon to NVIDIA consumer cards. Strata specializes in Qwen3.8-Flash-Next + desktop CPU\u002FGPU and pours all its optimization into that one target. The \"specialist vs generalist\" trade-off gets magnified to 4× in edge cases like 180B on 12GB.\n\n### Selection guidance\n\n- 180B on 12–16GB GPU + 64GB RAM: pick **Strata**\n- Llama \u002F Mistral \u002F DeepSeek and others: pick **llama.cpp** (widest ecosystem)\n- Multi-model workflow (5+ different models at once): pick **llama.cpp \u002F Ollama**\n- Multi-GPU A100\u002FH100 server-side: pick **vLLM \u002F SGLang**\n\n### Local deployment angle\n\nStrata only optimizes for Qwen3.8-Flash-Next (and future Qwen4 \u002F qwen4exp-family) models. If you only use this generation's Qwen Flash tier, Strata is the strongest desktop option. If you also run other models, llama.cpp is the safer choice.","Bittide AI Compass","http:\u002F\u002Fbittide.aicompass.dev\u002Farticle\u002F2dd62616-62ab-498a-ac39-5f194dbe5c02","2026-10-05T02:23:19.790Z"]