Local AI Advisor

Can Surface Laptop Ultra · RTX Spark N1X (20-core) · 128 GB run Qwen3.5 27B?

128 GB unified · 300 GB/s memory bandwidth · Qwen3.5 27B has 27.8B parameters.

Short answer

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

Best quantization for chat: Q6_K (22 GB of weights) on Ollama. Generation: 6–11 tok/s, leaving about 93 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 WebUIAcceptableQ6_K6–11 tok/sunder 1 s8KDetails
Coding questionswith ContinueAcceptableQ4_K_M9–14 tok/s≈3 s16KDetails
Coding in a repositorywith AiderAcceptableQ4_K_M8–14 tok/s≈8 s32KDetails
Agentic codingwith OpenCodeAcceptableQ3_K_M10–16 tok/s≈15 s32KDetails
Long documents (64K)with Open WebUIAcceptableQ6_K6–9 tok/s≈35 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 Q6_K build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3.5-27B-GGUF:Q6_K

Downloads 22.45 GB (Q6_K). 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.