Qwen3.8-Flash-Next (UD-Q4_K_XL) on a single RTX 3090 24GB + 128GB DDR4 — is this config optimal?

Hardware

• CPU: Intel Core i5-12600K
• RAM: 128 GB DDR4 @ 3600 MHz
• GPU: NVIDIA RTX 3090, 24 GB VRAM
• OS: Windows 11
• llama.cpp: freshly compiled from today's master (build b10794, Sep 4 2026)

Model

Qwen3.8-Flash-Next, UD-Q4_K_XL

Launch command

C:\llama\llama-server.exe -m "D:\Modelos\Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf" ^ -ngl 99 -ncmoe 42 -fit off -c 204800 -fa on -ctk f16 -ctv f16 ^ -b 2048 -ub 1024 -t -1 --jinja --port 8083 --tools all ^ --temp 1.0 --top-p 0.95 --top-k 20 --min-p 0.00 --repeat-penalty 1.0 ^ --reasoning on --reasoning-effort xhigh --reasoning-budget -1 --reasoning-preserve ^ --parallel 1 --metrics --host 0.0.0.0 --load-mode none 

Key points of the config:

• -ngl 99 → offload all layers to GPU where possible
• -ncmoe 42 → force 42 MoE (expert) layers to stay on CPU/RAM
• -c 204800 → 200k context window
• -fa on with f16 K/V cache
• -b 2048 / -ub 1024 → batch / micro-batch sizes
• --reasoning-effort xhigh, --reasoning-budget -1, --reasoning-preserve → full reasoning mode, no truncation

State right after loading

• Free system RAM: 52 GB
• Free VRAM: 0.6 GB

So the GPU is essentially maxed out (good VRAM utilization), and there's still a healthy ~52 GB of RAM headroom for the CPU-offloaded experts and context.

Real-world performance (from server logs)

Prompt processing (long context, ~12k tokens):

Text generation (sustained, up to ~480 tokens generated):

Generation speed stays remarkably stable around ~15.5-16 t/s even as context grows past 12k tokens, and prompt processing holds steady around ~185-190 t/s.

My question to the community

Given this is a large MoE (~180B total / ~5B active params) split across a single 24GB GPU + 128GB DDR4 RAM:

• Is -ncmoe 42 a good number here, or should I push more/fewer expert layers to the GPU given I still have some VRAM headroom before hitting OOM?
• Any tips on squeezing more t/s out of prompt processing (-b/-ub tuning) or generation given the CPU-offloaded experts are likely the bottleneck?
• MTP speculative decoding doesn't seem to work reliably for this model yet on mainline — has anyone gotten it stable, or is everyone just running without it for now?
• Does anyone recommend other configs/parameters for this exact model + hardware combo?

Open to any suggestions — happy to share more logs if useful!