余试Qwen 3.6 27B与35B之推测解码（多词预测，MTP）于RTX 4080，显存16GB.

欲观诸模型于同硬件下词速与显存权衡之全貌，可参16GB显存LLM基准测试与llama.cpp.

MTP（多词预测）何为

多标记预测，乃一种揣测解码之术，直植于某些模型之存档。非每前向一通预测一标记，此模型携额外“多标记预测首脑”，于单步中提议数未来标记——继而并行验证之。若揣测获纳，则有效吞吐量升，而输出之质无改。

Qwen 3.6系列，既发标准之型号。 GGUF 文件與 MTP -啟用變體。於 llama.cpp 中，MTP 透過以下方式啟動：

--spec-type draft-mtp --spec-draft-n-max 3

--spec-draft-n-max 為關鍵調節旋鈕。其設定 MTP 每步所提議的推測詞元數量。較高值或可提升潛在速度，然則須額外 VRAM 以供草稿緩衝區，此實為 16 GB 卡之實際限制。

所测者何？所测者如何？

吾测二Qwen 3.6模型，具MTP之能，较之标准解码，于GPU（16GB VRAM之RTX 4080）之表现若何。

欲使模型权重及KV缓存入VRAM，吾用重度量化之变体：

• Qwen3.6-27B-UD-IQ3_XXS、Qwen3.6-27B-UD-IQ3_XXS-MTP
• Qwen3.6-35B-A3B-UD-IQ3_S及Qwen3.6-35B-A3B-UD-IQ3_S-MTP

。 __JHSNS_SEG_a1f1ba12_22__每运行，计二语境预算：

• 平均上下文 — 此乃 llama.cpp 所需之上下文大小，需约 14.8 GB VRAM，余留其他应用（Xorg、GNOME Shell、Cursor）约 500 MB 之缓冲。
• 最大上下文 — 此乃 llama.cpp 所能分配之最大上下文，然同桌面应用已占约 500 MB VRAM。

持平均上下文于实用之域，其要因在于 赫尔墨斯代理。 — 吾以此为要务之智囊，通联此机之 llama.cpp — 须至少六十四 K 之境 — 乃其常例，若模型之窗较小，则于启程时必拒之。阈下之模型，其工作之忆量不足，遂不能持多步之工器。至於 llama.cpp，此即需传之理也。--ctx-size 65536 或更大。凡 MTP 配置使平均可用上下文显著低于六十四 K 者，皆不适宜于日常赫尔墨斯工作负载，故下表中之平均上下文数，实为最关决断者。

皆试 KV 缓存量化层级：q8（质高，VRAM 多）及 q5（减低虚拟显存，延展情境）。须知自q8迁至q5 KV缓存，可致质素显著损落——于吾之测试，其劣化之甚，足令q5不适宜吾之作业。q5之速与情境数，列于下文，以全其备，然尔宜于己之任务，先试其应答之质，而后决意采用之。

Qwen 3.6 27B MTP 对比 标准

KV缓存 q8

With q8 KV cache, MTP at--spec-draft-n-max 2送之~百分之六十七之速生成（75比45 t/s）然以平均语境窗口减半为代价，自80 K降至40 K。提示摄入速率自200降至~150 t/s，盖因MTP需于预填充阶段进行设备至主机的数据传输。

KV缓存q5

转至q5 KV缓存，恢复意蕴之境：--spec-draft-n-max 1赐70 K之平均境，57 t/s之速——39%之生成加速。超乎寻常之解码，而犹存有用之境。至--spec-draft-n-max 3，境降六十千，而生成速达六十七每秒（增六十三%）。

Qwen 3.6 27B所得

MTP诚为27B密模型所宜。十六GB显存之妙境：

• q8 KV +--spec-draft-n-max 2 — 最佳原始速度（75 t/s），上下文缩减至40–60 K
• q5 KV + --spec-draft-n-max 1 — 最佳速度与上下文平衡（57 t/s，70 K平均上下文)

Qwen 3.6 35B MTP 与标准之比较

35B模型为专家混合架构（Mixture-of-Experts, MoE）35B-A3B者，三十五亿参数也，每词约有三亿活跃参数。MoE模型常受益于MTP，盖因稀疏路由使MTP头相对于全前向传递而言，计算成本甚低。

KV缓存，q8

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493__至极境__JHSNS_SEG_a1f1ba12_494__至极境__JHSNS_SEG_a1f1ba12_495__至极境__JHSNS_SEG_a1f1ba12_496__至极境__JHSNS_SEG_a1f1ba12_497__至极境__JHSNS_SEG_a1f1ba12_498__至极境__JHSNS_SEG_a1f1ba12_499__至极境__JHSNS_SEG_a1f1ba12_500__至极境--spec-draft-n-max 1所予平均境域仅十五K，仅足应寻常之务。高深草稿之境，于十六GB之卡，竟无可用之平均境域。

此乃MTP于民用硬件之核心VRAM耗损之问：额外草稿之缓冲，直食剩余VRAM之预算，而35B-A3B之模，配以q8 KV缓存，所余甚微。

KV缓存q5

之KV cache，仅稍增平均上下文之效。--spec-draft-n-max 1则示十K上下文，时率151。标准解码于

，时率122，上下文平均百二十K。

十六GB GPU上，三十五B MoE模型配MTP，遇硬障：平均可用上下文骤降至十至十五K词元，实难应实际工作。标准解码每秒122至146，上下文八十至十二万词元，则大有用处。

若汝有二十四GB+ VRAM，则三十五B+ MTP之组合大增其美——上下文窗口之患消，且存速之益。

择适--spec-draft-n-max值

每步拟议之虚币几何数（--spec-draft-n-max）非有定解——视乎模架构与VRAM之可资：

• 27B密于16GB：--spec-draft-n-max 2以q8 KV为速--spec-draft-n-max 1 配 q5 KV 者，最合乎境也。
• 至若 35B MoE 之於 16 GB：--spec-draft-n-max 1 乃唯一可存可用之境者，然亦仅略尔而已。
• 值若更高 (3)，4增VRAM之压，而无相称之速——至四，所费VRAM与至二无异，然速增不与之俱。

如何使MTP于llama.cpp

务使用MTP之GGUF（文件名含MTP）。若初涉llama.cpp之旗，则参llama.cpp之快启于CLI与服务器。 覆盖万基。乃启 llama-server 抑或 llama-cli，以

llama-server \
  --model Qwen3.6-27B-UD-IQ3_XXS-MTP.gguf \
  --ctx-size 40000 \
  -ngl 99 --flash-attn on \
  --cache-type-k q8_0 --cache-type-v q8_0 \
  --spec-type draft-mtp \
  --spec-draft-n-max 2

若为 q5 KV 缓存，则易 q8_0 为 q5_1 或 q5_0，并调 --ctx-size 之上：

llama-server \
  --model Qwen3.6-27B-UD-IQ3_XXS-MTP.gguf \
  --ctx-size 80000 \
  -ngl 99 --flash-attn on \
  --cache-type-k q5_1 --cache-type-v q5_1 \
  --spec-type draft-mtp \
  --spec-draft-n-max 1

MTP自 llama.cpp睹GGUF之文见MTP首而--spec-type draft-mtp立。
故Qwen3.6-27B-UD-IQ3_XXS.gguf不可行于MTP之式，须Qwen3.6-27B-UD-IQ3_XXS-MTP.gguf。
然Qwen3.6-27B-UD-IQ3_XXS-MTP.gguf可通于推演解之式与自归一之式。

结论

于十六吉字节GPU（RTX 4080）之上，此量化参数，llama.cpp之MTP，于Qwen 3.6 27B实为胜，于Qwen 3.6 35B则实为负，于实用：

Qwen 3.6 27B (IQ3_XXS) — MTP实为可取：

• q8 KV + MTP至多二 → 速生约六十七分之百。，文境四十至六十千（较无MTP之八十至百千）
• q5 KV加MTP至一，则较前速生约三十九分，文境七十至百千（较无MTP之百三十至百六十）
• 速与VRAM效，得中于--spec-draft-n-max 2

Qwen 3.6 35B（IQ3_S）—十六GB处，MTP非实用

• 生速增廿七至廿九成，然平均境缩至十至十五千于q8，五千于q5
• 标准解码以百廿二至百四十六每秒，境涵八十至一百二十千，于实务更宜
• 境况于廿四吉字以上VRAM大有改善

纸面而言，q5 KV缓存乃最大化上下文窗口而存MTP速效之显答——然实践之中，自q8降至q5，质降殊甚。宜于己务自试q5，方采之；于吾工作，其劣不可受，q8以更紧之上下文预算，犹为较优之权衡。

欲观大略于LLM服务之选与基础设权衡，请参二零二六年大模型托管之柱，二零二六年大模型性能。若汝调校Qwen 3.6采样器之设，与MTP同，则Qwen 3.6与Gemma 4能动式大模型推理参数参考，实为良伴。