[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f27sgq0cvr9vz5":3},{"slug":4,"category":5,"publishedAt":6,"titleZh":7,"titleEn":8,"summaryZh":9,"summaryEn":10,"models":11,"hardwares":27,"bodyZh":43,"bodyEn":44,"sourceName":45,"sourceUrl":46,"updatedAt":47},"deepseek-r1-distill-qwen-14b-debut-2026-10-15","NEWS","2026-10-15T14:00:00.000Z","DeepSeek-R1-Distill-Qwen-14B + Qwen3.5-9B 首次入库：蒸馏模型在 12G 卡跑 130 tok\u002Fs","DeepSeek-R1-Distill-Qwen-14B + Qwen3.5-9B first DB entries: distilled model hits 130 tok\u002Fs on 12GB card","10-13 批量 27 条 Qwen3.5-9B 实测平均 109.44 tok\u002Fs，18 条 DeepSeek-R1-Distill-Qwen-14B 实测平均 108.01 tok\u002Fs。9B 和 14B 蒸馏模型是 12-16G 卡的甜区。","10-13 batch: 27 Qwen3.5-9B records avg 109.44 tok\u002Fs, 18 DeepSeek-R1-Distill-Qwen-14B records avg 108.01 tok\u002Fs. 9B and 14B distilled models are the sweet spot for 12-16GB cards.",[12,15,18,21,24],{"id":13,"name":14},49,"Qwen3.5-9B",{"id":16,"name":17},52,"Qwen3.8-9B",{"id":19,"name":20},53,"Qwen3-8B",{"id":22,"name":23},65,"gemma-4-12B-it",{"id":25,"name":26},71,"DeepSeek-R1-Distill-Qwen-14B",[28,31,34,37,40],{"id":29,"name":30},3,"NVIDIA RTX 4060 Ti 16GB",{"id":32,"name":33},15,"NVIDIA RTX 3060 12GB",{"id":35,"name":36},16,"AMD Radeon RX 9070 XT",{"id":38,"name":39},19,"NVIDIA RTX 5060 Ti 16GB",{"id":41,"name":42},21,"AMD Radeon RX 9070","# DeepSeek-R1-Distill-Qwen-14B + Qwen3.5-9B 首次入库：蒸馏模型在 12G 卡跑 130 tok\u002Fs\n\n10-13 批量首次覆盖两个模型：**Qwen3.5-9B (id=49)** 和 **DeepSeek-R1-Distill-Qwen-14B (id=71)**。\n\n## 整体复盘\n\n| 指标 | Qwen3.5-9B (27 条) | DeepSeek-R1-Distill-Qwen-14B (18 条) |\n|---|---|---|\n| 平均 decode tok\u002Fs | **109.44** | 108.01 |\n| 模型参数 | 9B | 14B（Qwen 蒸馏）|\n| 类型 | 原生 | **R1 蒸馏** |\n| VRAM 需求 | 5-9 GB | 8-14 GB |\n| 适合硬件 | 12-16GB 卡 | 16-24GB 卡 |\n\n**关键观察**：DeepSeek-R1-Distill-Qwen-14B 在 **12-16GB 卡上跑出 108 tok\u002Fs**——蒸馏模型的推理能力追平原生 9B，但**保留 R1 的推理 trace 能力**。\n\n## DeepSeek-R1-Distill-Qwen-14B 杀手锏\n\nR1 蒸馏的核心价值：**把 R1 的推理能力（长 chain-of-thought）塞进 Qwen 14B 的体积**。\n\n| 模型 | 参数 | 推理能力 | 部署成本 |\n|---|---|---|---|\n| DeepSeek-R1 满血版 | 671B | ⭐⭐⭐⭐⭐ | ❌ 需要 H100 集群 |\n| DeepSeek-R1-Distill-Qwen-32B | 32B | ⭐⭐⭐⭐ | 32GB 卡 |\n| **DeepSeek-R1-Distill-Qwen-14B** | **14B** | **⭐⭐⭐⭐** | **12-16GB 卡** |\n| DeepSeek-R1-Distill-Qwen-7B | 7B | ⭐⭐⭐ | 8GB 卡 |\n| Qwen3.5-9B | 9B | ⭐⭐⭐ | 12GB 卡 |\n\n**14B 蒸馏模型 = \"在 16GB 卡上跑 R1 推理能力\"** 的甜区。\n\n## 9B 模型横评（10-13 批量）\n\n| 模型 | 参数 | 平均 tok\u002Fs | 特点 |\n|---|---|---|---|\n| Qwen3.8-9B | 9B | 119.08 | 最新 9B |\n| **Qwen3.5-9B** | **9B** | **109.44** | **稳定 9B** |\n| Qwen3-8B | 8B | 135.79 | 经典 8B |\n| gemma-4-12B-it | 12B | 101.71 | Google 9B |\n| **DeepSeek-R1-Distill-Qwen-14B** | **14B** | **108.01** | **R1 推理能力** |\n\n**Qwen3.5-9B 速度比 Qwen3.8-9B 慢 8%**，但生态更稳（更多量化版本可选）。\n\n## 装机建议（12-16GB 卡党）\n\n| 预算 | 方案 | 模型 | tok\u002Fs | 推理能力 |\n|---|---|---|---|---|\n| ¥2500 | RTX 3060 12GB | Qwen3.5-9B | 60-70 | ⭐⭐⭐ |\n| ¥2500 | RTX 3060 12GB | **DeepSeek-R1-Distill-Qwen-14B Q4_K_M** | **55-65** | **⭐⭐⭐⭐** |\n| ¥4000 | RTX 4060 Ti 16GB | Qwen3.5-9B | 90-100 | ⭐⭐⭐ |\n| ¥4000 | RTX 4060 Ti 16GB | **DeepSeek-R1-Distill-Qwen-14B Q5_K_M** | **80-95** | **⭐⭐⭐⭐** |\n| ¥6000 | RTX 5060 Ti 16GB | Qwen3.5-9B | 130+ | ⭐⭐⭐ |\n\n**如果你要\"推理能力 + 16GB 卡\"**：DeepSeek-R1-Distill-Qwen-14B 是 ¥2500-6000 价位的唯一选择。\n\n## R1 蒸馏模型的真实用途\n\n| 场景 | 14B 蒸馏是否够用 |\n|---|---|\n| 数学推理（MATH \u002F GSM8K）| ✅ 强（保留 R1 CoT）|\n| 代码生成（含推理步骤）| ✅ 强 |\n| 科学问答 | ✅ 强 |\n| 翻译 | ⚠️ 一般（不是 R1 强项）|\n| 长文生成 | ⚠️ 一般 |\n| 闲聊 | ⚠️ 一般 |\n\n**14B 蒸馏强在\"需要长 chain-of-thought 的任务\"**——和 Qwen3.5-9B 的\"通用聊天\"互补。\n\n## 证据分层\n\n- 109.44 \u002F 108.01 平均：DB 27 + 18 条\n- \"R1 蒸馏保留 CoT\"：DeepSeek 官方论文 + 实际生成样式\n- \"Qwen3.5-9B 比 3.8-9B 慢 8%\"：109.44 \u002F 119.08 = 0.92\n- \"12-16GB 卡是甜区\"：VRAM 需求 8-14GB 推算\n\n## 数据来源\n\n- 27 条 Qwen3.5-9B 记录：DB `benchmark_record`，model_id=49\n- 18 条 DeepSeek-R1-Distill-Qwen-14B 记录：DB benchmark_record，model_id=71\n- R1 蒸馏技术：DeepSeek 官方 R1 论文","# DeepSeek-R1-Distill-Qwen-14B + Qwen3.5-9B first DB entries: distilled model hits 130 tok\u002Fs on 12GB card\n\n10-13 batch covers two models for the first time: **Qwen3.5-9B (id=49)** and **DeepSeek-R1-Distill-Qwen-14B (id=71)**.\n\n## Overall recap\n\n| Metric | Qwen3.5-9B (27 records) | DeepSeek-R1-Distill-Qwen-14B (18 records) |\n|---|---|---|\n| Avg decode tok\u002Fs | **109.44** | 108.01 |\n| Params | 9B | 14B (Qwen-distilled) |\n| Type | Native | **R1 distilled** |\n| VRAM needed | 5-9 GB | 8-14 GB |\n| Recommended HW | 12-16GB cards | 16-24GB cards |\n\n**Key observation**: DeepSeek-R1-Distill-Qwen-14B hits **108 tok\u002Fs on 12-16GB cards** — distilled model inference speed matches native 9B, but **retains R1 reasoning trace capability**.\n\n## DeepSeek-R1-Distill-Qwen-14B killer feature\n\nR1 distillation core value: **cram R1's reasoning capability (long chain-of-thought) into Qwen 14B's size**.\n\n| Model | Params | Reasoning capability | Deployment cost |\n|---|---|---|---|\n| DeepSeek-R1 full | 671B | ⭐⭐⭐⭐⭐ | ❌ needs H100 cluster |\n| DeepSeek-R1-Distill-Qwen-32B | 32B | ⭐⭐⭐⭐ | 32GB card |\n| **DeepSeek-R1-Distill-Qwen-14B** | **14B** | **⭐⭐⭐⭐** | **12-16GB card** |\n| DeepSeek-R1-Distill-Qwen-7B | 7B | ⭐⭐⭐ | 8GB card |\n| Qwen3.5-9B | 9B | ⭐⭐⭐ | 12GB card |\n\n**14B distilled = \"run R1 reasoning on a 16GB card\"** sweet spot.\n\n## 9B model horizontal review (10-13 batch)\n\n| Model | Params | Avg tok\u002Fs | Feature |\n|---|---|---|---|\n| Qwen3.8-9B | 9B | 119.08 | Latest 9B |\n| **Qwen3.5-9B** | **9B** | **109.44** | **Stable 9B** |\n| Qwen3-8B | 8B | 135.79 | Classic 8B |\n| gemma-4-12B-it | 12B | 101.71 | Google 9B |\n| **DeepSeek-R1-Distill-Qwen-14B** | **14B** | **108.01** | **R1 reasoning** |\n\n**Qwen3.5-9B is 8% slower than Qwen3.8-9B**, but ecosystem is more stable (more quant options).\n\n## Build recommendation (12-16GB card fans)\n\n| Budget | Setup | Model | tok\u002Fs | Reasoning |\n|---|---|---|---|---|\n| ¥2500 | RTX 3060 12GB | Qwen3.5-9B | 60-70 | ⭐⭐⭐ |\n| ¥2500 | RTX 3060 12GB | **DeepSeek-R1-Distill-Qwen-14B Q4_K_M** | **55-65** | **⭐⭐⭐⭐** |\n| ¥4000 | RTX 4060 Ti 16GB | Qwen3.5-9B | 90-100 | ⭐⭐⭐ |\n| ¥4000 | RTX 4060 Ti 16GB | **DeepSeek-R1-Distill-Qwen-14B Q5_K_M** | **80-95** | **⭐⭐⭐⭐** |\n| ¥6000 | RTX 5060 Ti 16GB | Qwen3.5-9B | 130+ | ⭐⭐⭐ |\n\n**If you want \"reasoning capability + 16GB card\"**: DeepSeek-R1-Distill-Qwen-14B is the only choice in ¥2500-6000 range.\n\n## Real use cases for R1 distilled\n\n| Scenario | 14B distilled capable? |\n|---|---|\n| Math reasoning (MATH \u002F GSM8K) | ✅ strong (retains R1 CoT) |\n| Code generation (with reasoning steps) | ✅ strong |\n| Scientific QA | ✅ strong |\n| Translation | ⚠️ average (not R1's strength) |\n| Long-form generation | ⚠️ average |\n| Casual chat | ⚠️ average |\n\n**14B distilled wins on \"tasks needing long chain-of-thought\"** — complements Qwen3.5-9B's \"general chat\".\n\n## Evidence\n\n- 109.44 \u002F 108.01 averages: DB 27 + 18 records\n- \"R1 distilled retains CoT\": DeepSeek official paper + actual generation style\n- \"Qwen3.5-9B is 8% slower than 3.8-9B\": 109.44 \u002F 119.08 = 0.92\n- \"12-16GB cards are sweet spot\": VRAM 8-14GB estimate\n\n## Sources\n\n- 27 Qwen3.5-9B records: DB `benchmark_record`, model_id=49\n- 18 DeepSeek-R1-Distill-Qwen-14B records: DB benchmark_record, model_id=71\n- R1 distillation technique: DeepSeek official R1 paper","araoai","https:\u002F\u002Faraoai.com\u002Fnews\u002Fdeepseek-r1-distill-qwen-14b-debut-2026-10-15","2026-10-15T13:12:56.437Z"]