Local AI Advisor

Can Surface Laptop Ultra · RTX Spark N1X (20-core) · 128 GB run Qwen3.5 122B-A10B?

128 GB unified · 300 GB/s memory bandwidth · Qwen3.5 122B-A10B has 125B parameters (10B active per token).

Short answer

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

Best quantization for chat: Q5_K_M (84 GB of weights) on Ollama. Generation: 15–25 tok/s, leaving about 31 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 WebUIExcellentQ5_K_M15–25 tok/sunder 1 s8KDetails
Coding questionswith ContinueComfortableQ5_K_M15–25 tok/s≈2 s16KDetails
Coding in a repositorywith AiderComfortableQ4_K_M17–30 tok/s≈6 s32KDetails
Agentic codingwith OpenCodeAcceptableQ5_K_M14–25 tok/s≈8 s32KDetails
Long documents (64K)with Open WebUIExcellentQ4_K_M15–25 tok/s≈25 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 Q4_K_M build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3.5-122B-A10B-GGUF:Q4_K_M

This build is split into 3 files, which Ollama can't import from Hugging Face. Use llama.cpp or LM Studio instead.

Different tool, context size or workload?

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