Local AI Advisor

Can Laptop · Ryzen AI 9 HX 370 (Radeon 890M) · 32 GB run DeepSeek-R1-0528-Qwen3-8B?

32 GB unified · 120 GB/s memory bandwidth · DeepSeek-R1-0528-Qwen3-8B has 8.2B parameters.

Short answer

Yes, comfortable for chat; borderline for agentic coding.

Best quantization for chat: Q5_K_M (5.6 GB of weights) on Lemonade. Generation: 10–16 tok/s, leaving about 16 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 WebUIComfortableQ5_K_M10–16 tok/s≈3 s8KDetails
Coding questionswith ContinueAcceptableQ6_K8–13 tok/s≈20 s16KDetails
Coding in a repositorywith AiderAcceptableQ5_K_M7–12 tok/s≈65 s32KDetails
Agentic codingwith OpenCodeBorderlineQ6_K6–10 tok/s≈90 s32KDetails
Long documents (64K)with Open WebUIBorderlineQ6_K4.2–7 tok/s≈5 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 Q5_K_M build with a 32K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF:Q5_K_M

Downloads 5.85 GB (Q5_K_M). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=32768 before `ollama serve` for long prompts.

Different tool, context size or workload?

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