Qwen3.8-27B在16GB显卡上的高效部署方案
Made a really good Qwen3.8 27B 16GB setup, no strata/streaming-kv
16GB显存跑27B模型是常见痛点，这篇提供了完整的硬件规避思路与llama.cpp关键参数，实测数据详实，直接可抄作业。
I wanted to share my setup thats working really well for my fellow 16GB desktop suffrage crew. I am able to get 110k context with ~1.5GB to spare and around 55 t/s on my 5070 TI.
我想分享一下我的配置，这对我们16GB显存的桌面端“苦力”团队来说效果非常好。在5070 TI上，我能获得约110k的上下文窗口，剩余约1.5GB显存，推理速度约为55 token/s。
First off I found this model https://huggingface.co/hitsfmdj/Qwen3.8-27B-GGUF-4.2BPW-16GB with custom weights that seems to be pretty good and only 11.8GB. However even with this model, I ran into issues with context windows greater than ~60k. Sometimes it would fit on the GPU and other times it didnt. It was a good size, but not enough for larger coding tasks and network automation stuff.
首先，我找到了这个模型 https://huggingface.co/hitsfmdj/Qwen3.8-27B-GGUF-4.2BPW-16GB，它使用了自定义权重，表现相当不错，且仅占用11.8GB。然而，即使使用这个模型，当上下文窗口超过约60k时，我仍会遇到问题。有时它能放入GPU，有时又不能。虽然体积合适，但对于更大的编码任务和网络自动化工作来说还不够。
So I setup my computer to utilize my iGPU on my 9800X3D as my main display driver instead of the 5070 TI 16GB. Xorg alone was eating up 600-800MB of VRAM. The more applications I opened, the more VRAM I used the more problems/slowness I had. With my motherboard MSI MAG x870e Tomahawk, I can enable Hybrid Graphics which allows me to set the primary display driver as the iGPU instead of the 5070 TI but still utilize the 5070, reserving the full 16GB of VRAM for llama and gaming. I tested playing my main game Overwatch on my main display at 1440P 144hz and the game ran just fine at my FPS cap of 300. Ive been very happy so far with this setup, the extra context size has been a real game changer for longer and more complex tasks, its fast enough to persuade me away from ChatGPT.
因此，我将电脑设置为使用9800X3D上的核显（iGPU）作为主显示驱动，而不是使用5070 TI 16GB。仅Xorg就占用了600-800MB的显存。我打开的应用程序越多，使用的显存就越多，遇到的问题/卡顿也就越严重。借助我的主板MSI MAG X870E Tomahawk，我可以启用混合图形模式（Hybrid Graphics），将主显示驱动设置为核显而不是5070 TI，同时仍能使用5070 TI，从而为llama和游戏保留完整的16GB显存。我在主显示器上以1440P 144Hz分辨率测试了主要游戏《守望先锋》，游戏在我的FPS上限300帧下运行良好。到目前为止，我对这套配置非常满意，额外的上下文大小对于更长、更复杂的任务来说是一个真正的改变者，它的速度足以让我远离ChatGPT。
I do also have a pretty decent overclock on my GPU. +350 Core, +4000 Mem, +350 Xbar and 350 watt bios, I have 32GB DDR5.
我也对GPU进行了不错的超频：核心+350，内存+4000，Xbar +350，BIOS功耗限制350瓦，我有32GB DDR5内存。
=========================================================================================|
| 0 N/A N/A 1638 G /usr/lib/xorg/Xorg 55MiB |
| 0 N/A N/A 44165 C ...ma.cpp/build/bin/llama-server 14734MiB |
+-----------------------------------------------------------------------------------------+
ExecStart=/home/jayecin/AI/llama.cpp/build/bin/llama-server \
--model /home/jayecin/AI/Models/Qwen3.8-27B-4.2BPW-16GB.gguf \
--alias Qwen3.8-27B \
--host 0.0.0.0 \
--port 8080 \
--ctx-size 110000 \
--fit on \
--flash-attn on \
--cache-type-k q8_0 \
--cache-type-v q4_0 \
--batch-size 256 \
--ubatch-size 256 \
--parallel 1 \
--reasoning-effort medium \
--reasoning-budget 4096 \
--no-reasoning-preserve \
--metrics
Oct 30 18:25:11 9800X3D llama-server[44165]: 26.55.877.747 I slot print_timing: id 0 | task 3302 | n_gen = 3460, tg = 54.69 t/s, tg_3s = 54.09 t/s
Oct 30 18:25:11 9800X3D llama-server[44165]: 26.56.136.317 I slot print_timing: id 0 | task 3302 | prompt eval time = 70.53 ms / 4 tokens ( 17.63 ms per token, 56.71 tokens per second)
10月30日 18:25:11 9800X3D llama-server[44165]: 26.56.136.317 I slot print_timing: id 0 | task 3302 | prompt eval time = 70.53 ms / 4 tokens ( 17.63 ms per token, 56.71 tokens per second)
Oct 30 18:25:11 9800X3D llama-server[44165]: 26.56.136.319 I slot print_timing: id 0 | task 3302 | eval time = 63510.16 ms / 3474 tokens ( 18.29 ms per token, 54.68 tokens per second)
10月30日 18:25:11 9800X3D llama-server[44165]: 26.56.136.319 I slot print_timing: id 0 | task 3302 | eval time = 63510.16 ms / 3474 tokens ( 18.29 ms per token, 54.68 tokens per second)
Oct 30 18:25:11 9800X3D llama-server[44165]: 26.56.136.320 I slot print_timing: id 0 | task 3302 | total time = 63580.69 ms / 3478 tokens
10月30日 18:25:11 9800X3D llama-server[44165]: 26.56.136.320 I slot print_timing: id 0 | task 3302 | total time = 63580.69 ms / 3478 tokens
Oct 30 18:25:11 9800X3D llama-server[44165]: 26.56.136.320 I slot print_timing: id 0 | task 3302 | graphs reused = 6840
10月30日 18:25:11 9800X3D llama-server[44165]: 26.56.136.320 I slot print_timing: id 0 | task 3302 | graphs reused = 6840
Oct 30 18:25:11 9800X3D llama-server[44165]: 26.56.136.321 I slot print_timing: id 0 | task 3302 | graph reallocs = 0 / 0 (same size) / (changed size)
10月30日 18:25:11 9800X3D llama-server[44165]: 26.56.136.321 I slot print_timing: id 0 | task 3302 | graph reallocs = 0 / 0 (same size) / (changed size)
Oct 30 18:25:11 9800X3D llama-server[44165]: 26.56.136.582 I slot release: id 0 | task 3302 | stop processing: n_tokens = 16449, truncated = 0
10月30日 18:25:11 9800X3D llama-server[44165]: 26.56.136.582 I slot release: id 0 | task 3302 | stop processing: n_tokens = 16449, truncated = 0
Oct 30 17:58:52 9800X3D llama-server[44165]: 0.36.587.381 I slot print_timing: id 0 | task 3 | prompt processing, n_tokens = 6912, progress = 0.53, t = 3.10 s / 2227.71 tokens per second
10月30日 17:58:52 9800X3D llama-server[44165]: 0.36.587.381 I slot print_timing: id 0 | task 3 | prompt processing, n_tokens = 6912, progress = 0.53, t = 3.10 s / 2227.71 tokens per second
Oct 30 17:58:52 9800X3D llama-server[44165]: 0.36.707.961 I slot print_timing: id 0 | task 3 | prompt processing, n_tokens = 7168, progress = 0.55, t = 3.22 s / 2223.80 tokens per second
10月30日 17:58:52 9800X3D llama-server[44165]: 0.36.707.961 I slot print_timing: id 0 | task 3 | prompt processing, n_tokens = 7168, progress = 0.55, t = 3.22 s / 2223.80 tokens per second
Oct 30 17:58:52 9800X3D llama-server[44165]: 0.36.828.660 I slot print_timing: id 0 | task 3 | prompt processing, n_tokens = 7424, progress = 0.57, t = 3.34 s / 2220.08 tokens per second
10月30日 17:58:52 9800X3D llama-server[44165]: 0.36.828.660 I slot print_timing: id 0 | task 3 | prompt processing, n_tokens = 7424, progress = 0.57, t = 3.34 s / 2220.08 tokens per second
Oct 30 17:58:52 9800X3D llama-server[44165]: 0.36.949.495 I slot print_timing: id 0 | task 3 | prompt processing, n_tokens = 7680, progress = 0.59, t = 3.46 s / 2216.54 tokens per second
10月30日 17:58:52 9800X3D llama-server[44165]: 0.36.949.495 I slot print_timing: id 0 | task 3 | prompt processing, n_tokens = 7680, progress = 0.59, t = 3.46 s / 2216.54 tokens per second
更进一步：量化金融体系
看懂新闻只是起点——沿量化金融路径，把它变成能交付的工程能力