▲ 14 r/ollama+1 crossposts

I might have found the perfect config parameters for qwen 3.8 27b

Hello everyone, tried so hard to optimize my config and finally I simply get up to 70 t/s with q6 variant. And wanted to share with you guys so that other people with the same setup can enjoy. Please check out and see if that improves your performance in any kind of way.

Also huge thanks to qwen and unsloth teams.

"C:\Users\dsdt\llama\llama-server.exe" ^
  -m "C:\Users\dsdt\models\Qwen3.8-27B-UD-Q6_K.gguf" ^
  --mmproj "C:\Users\dsdt\models\mmproj-BF16.gguf" ^
  --jinja ^
  --chat-template-kwargs "{\"reasoning_effort\":\"medium\"}" ^
  --reasoning on ^
  --reasoning-preserve ^
  -c 100000 ^
  --split-mode tensor ^
  --flash-attn on ^
  --cache-type-k q8_0 ^
  --cache-type-v q8_0 ^
  --spec-type draft-mtp,ngram-mod ^
  --spec-draft-n-max 2 ^
  --spec-ngram-mod-n-match 24 ^
  --spec-ngram-mod-n-min 24 ^
  --spec-ngram-mod-n-max 86 ^
  -t 8 ^
  --batch-size 8869 ^
  --ubatch-size 531 ^
  -ngl 105 ^
  -np 1 ^
  --fit off ^
  --temp 1.0 ^
  --top-p 0.95 ^
  --top-k 20 ^
  --min-p 0.00 ^
  --presence-penalty 0.0 ^
  --host 0.0.0.0 ^
  --port 8080
  1. System & Model Configuration
Parameter Value
Model Qwen3.8-27B-UD-Q6_K.gguf
Context Length 100,000 tokens
GPUs 2 × RTX 5060 Ti (Total VRAM: 32 GB)
Vision Enabled (VRAM allocated, not used during generation)
Task ID / Slot Task 2536 / Slot 0
  1. Core Performance Metrics
Metric Value
Prompt Processing 646.62 ms / 27 tokens → 41.76 t/s (23.95 ms/token)
Generation (Eval) 126,199.16 ms / 8,624 tokens → 68.33 t/s (14.64 ms/token)
Overall Throughput 68.33 t/s (prompt overhead negligible)
Total Time 126,845.77 ms (~2 min 6.8 s)
Total Tokens Processed 8,651 (27 prompt + 8,624 generated)
Final Tokens Count (with cache) 8,973 (clean stop, no truncation)
Graphs Reused 5,569 (excellent computational graph reuse)
  1. Generation Speed Stability (Short-term)
Window Observed Speed Range
3-second rolling window (tg_3s) ~50 – 79 t/s (early fluctuations)
Steady-state (later stages) ~62 – 72 t/s
Overall average (tg) Converged to stable 68.5 – 69.5 t/s
  1. Speculative Decoding Efficiency
Metric Value
Draft Acceptance Rate 80.04% (5,510 accepted / 6,884 drafted)
Average Draft Length 2.77 tokens per forward pass of the target model
reddit.com
u/dsdt — 6 hours ago

Hit me with your favorite long name model.

My favorite one is "DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF". I want to find other gems like this.

reddit.com
u/dsdt — 1 month ago

Bana profesyonel hayatımda linux kullanıyorum diyebilecek kaç kişi çıkabilir?

Evet, soru net. Dikkatimi çeken linux topluluklarındaki gerçekten uzak tutumu eleştirmek adına yazıyorum bunu. Evet açık kaynak, alternatif, özgürlük bunlar güzel kafalar ama yazılım dünyası haricinde ; "Evet ya ben iş yerimde veya işim için linux kullanıyorum ve bundan para kazanıyorum." diyebilen kaç kişi var aranızda?

Yaptığınız işleri merak ediyorum. Özellikle benimde içinde bulunduğum tasarım alanında, 3d 2d fark etmez böyle bir durum söz konusu bile değil. Blender var diyebilirsiniz. Sektörde bir tane blender kullanan adam yok en azından Türkiye pazarı için. Kaldı ki 3ds max veya rhinoda 30 saniye alacak bir iş blenderda öğrenme eğrisinden dolayı 30 dakika sürüyor. Bugüne kadar hangi distroyu denediysem ve kullanmak için kendimi zorladıysam eninde sonunda herhangi bir sebepten, iş, özel hayat, farklı bir gereksinim sebebiyle windows'a geri döndüm. Bu beni windows fanı yapmıyor günün sonunda aktif olarak en az bloat hali olan Windows 11 LTSC + debloat scriptleri ile içinde hiç bir şey kalmamış haliyle çalışıyorum. Böyle bir alternatifte var.

Eski cihazları yeniden hayata döndürmek, server ve homelab setupları için harika bir sistem diyebiliriz linux distroları için evet, ama bu soruyu cevaplamakta zorlanıyorum. Yardımcı olabilirseniz sevinirim.

reddit.com
u/dsdt — 1 month ago
▲ 11 r/LocalLLM+1 crossposts

Laravel dev running Qwen 3.6 35B A3B—do we really need all these languages?

I run Qwen3.6-35B-A3B daily for Laravel + Vue full-stack work, and it genuinely bugs me that a 20GB+ model spends weights on French, Chinese, Spanish—languages I will never prompt in.

My variables are $user, $product, $order. Laravel errors, Vue docs, PHP RFCs—all English. Every parameter spent on Mandarin fluency is a parameter not spent on Laravel 11 syntax, Vue 3 Composition API edge cases, or PHP 8.3 behavior. Yet the model can discuss Chinese poetry and still tell me php artisan serve defaults to port 8080 (it's 8000).

Yes, it's MoE—only ~3B active. But the dense Qwen3.6-27B has no such luxury. No sparse routing, no free lunch: it needs the full ~16-20GB loaded, multilingual weights included. That locks out anyone on a single 3060 12GB, 4060 8GB, or older card from running a genuinely strong local coding model—not because the reasoning capacity isn't there, but because a chunk of it is spent on languages they'll never use.

Two questions:

  1. Will we see English+code-only models trained from scratch? Zero multilingual data, all budget into English technical text and code.
  2. Can existing models be pruned post-hoc? Is there a way to strip non-English weights from something like Qwen or Gemma after training—not just quantize, but actually remove the multilingual capacity and reclaim the VRAM? A pruned Qwen-27B that fits in 10GB instead of 18GB would put real coding models within reach of a 3060 12GB, and a quantized version within reach of an 8GB card.

I'd trade a chunk of general multilingual reasoning for better Laravel/Vue accuracy and half the VRAM footprint. Is that technically feasible, or are we stuck with bloated dense models for the foreseeable future?

Edit : for those who say multilingualism help reasoning or coding please check this out : https://ar5iv.labs.arxiv.org/html/2509.24405

Summary : State-of-the-art reasoning models like DeepSeek-R1 and OpenAI o1 only reach about 4% execution accuracy relying on their own intrinsic reasoning, compared to roughly 60% on the earlier, easier MultiSpider 1.0. That's a massive difficulty jump

u/dsdt — 1 month ago