Local AI Advisor

Can Laptop · Ryzen AI 9 HX 370 (Radeon 890M) · 32 GB run Qwen3.6 35B-A3B?

32 GB unified · 120 GB/s memory bandwidth · Qwen3.6 35B-A3B has 36B parameters (3B active per token).

Short answer

Yes, comfortable for chat; borderline for agentic coding.

Best quantization for chat: Q3_K_M (17 GB of weights) on Lemonade. Generation: 30–50 tok/s, leaving about 6.0 GB free for your other apps.

How it rates for each workload

Same model and machine, different jobs. Agents and long documents send far bigger prompts than chat, so they need much more speed and memory.

WorkloadVerdictQuantGenerationFirst replyContext
Chatwith Open WebUIComfortableQ3_K_M30–50 tok/s≈3 s8KDetails
Coding questionswith ContinueAcceptableQ3_K_M30–50 tok/s≈15 s16KDetails
Coding in a repositorywith AiderAcceptableQ3_K_M25–40 tok/s≈45 s32KDetails
Agentic codingwith OpenCodeBorderlineQ4_K_M20–35 tok/s≈55 s32KDetails
Long documents (64K)with Open WebUIAcceptableQ3_K_M18–30 tok/s≈3 min64KDetails

Speeds are bandwidth-based estimates; no direct benchmark exists for this pair. “First reply” is the time to read the workload’s opening prompt. How we calculate

Get it running

Copy-paste commands for the Q3_K_M build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q3_K_XL

Downloads 16.85 GB (UD-Q3_K_XL). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=65536 before `ollama serve` for long prompts.

Different tool, context size or workload?

These pages use typical settings. The full check models your exact setup.