Local AI Advisor

Can Surface Laptop Ultra · RTX Spark N1X (20-core) · 128 GB run Qwen3-Coder 30B-A3B Instruct?

128 GB unified · 300 GB/s memory bandwidth · Qwen3-Coder 30B-A3B Instruct has 30.5B parameters (3.3B active per token).

Short answer

Yes, excellent for chat and comfortable or better for every workload we test.

Best quantization for chat: Q6_K (24 GB of weights) on Ollama. Generation: 35–60 tok/s, leaving about 91 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 WebUIExcellentQ6_K35–60 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellentQ6_K35–55 tok/sunder 1 s16KDetails
Coding in a repositorywith AiderComfortableQ6_K25–45 tok/s≈3 s32KDetails
Agentic codingwith OpenCodeComfortableQ5_K_M25–40 tok/s≈3 s32KDetails
Long documents (64K)with Open WebUIExcellentQ5_K_M15–25 tok/s≈10 s64KDetails

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 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q5_K_M

Downloads 21.73 GB (Q5_K_M). 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.