1× Mac
155tok/sEveryday
1 request, llmprobe standard prompt, thinking on, with MTP + PLD (per recipe) Source
Intelligence40with thinking
- Experimental
- License unclear
Local AI · Recipe · 2× RTX 3090
Qwen3.8-Flash-Next 125B UD-Q4_K_XL with llama.cpp on 2× RTX 3090
Qwen3.8-Flash-Next 125B UD-Q4_K_XL with llama.cpp on 2× RTX 3090: 36.8 tok/s according to github.com (dataset as of Nov 3, 2026).
by noonghunna
Engine
- Engine
- llama.cpp
- Quantization
- GGUF UD-Q4_K_XL, q8_0 KV
- Model family
- Qwen3.8-Flash-Next
- Context
- 204,800
- Parameters
- 125B MoE
- Creator
- noonghunna
- GitHub stars
- 2,471
- Repo updated
- Nov 2, 2026
Measurements
Every sourced value of this recipe, each with its condition and source. Bars relative to the largest value in the group.
Decode · 1 request
Prefill by context
What you need
- Hardware
- 2 × NVIDIA RTX 3090
- Weights
- Main weightsunsloth/Qwen3.8-Flash-Next-GGUF other
- Engine
- llama.cpp
- Context
- 204,800 tokens
Notes
What matters before you rebuild it.
- Partly in system RAM
- Experimental
- License unclear
Sources
Related recipes
4× RTX 3090
110tok/sEveryday
1 request, prose prompt, with MTP (per recipe) Source
Intelligence40with thinking
- License unclear
2× DGX Spark
89.3tok/sEveryday
1 request, prose prompt, with MTP (per recipe) Source
Intelligenceno independent value
- Custom kernel required
- License unclear
2× DGX Spark
74tok/sEveryday
1 request, prose prompt (long story), thinking off, with MTP Source
Intelligence40with thinking
- Custom kernel required
- License unclear
4× DGX Spark
73tok/sEveryday
1 request, decode at 32K context (speed table, ring of four), thinking on, xhigh Source
Intelligence40with thinking
- Custom kernel required
2× DGX Spark
69.6tok/sEveryday
1 request, prose prompt, with MTP (per recipe) Source
Intelligence40with thinking
- License unclear
Local AI in your company?
In a workshop we work out which models and which hardware fit your tasks, and build the first agent on your infrastructure.