Local AI Advisor

Can Surface Laptop Ultra · RTX Spark N1X (20-core) · 128 GB run gpt-oss-120b?

128 GB unified · 300 GB/s memory bandwidth · gpt-oss-120b has 116.8B parameters (5.1B active per token).

Short answer

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

Best quantization for chat: MXFP4 (59 GB of weights) on Ollama. Generation: 40–65 tok/s, leaving about 56 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 WebUIExcellentMXFP440–65 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellentMXFP435–60 tok/s≈1 s16KDetails
Coding in a repositorywith AiderExcellentMXFP435–55 tok/s≈3 s32KDetails
Agentic codingwith OpenCodeComfortableMXFP430–50 tok/s≈5 s32KDetails
Long documents (64K)with Open WebUIExcellentMXFP425–40 tok/s≈15 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 MXFP4 build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/gpt-oss-120b-GGUF:F16

Downloads 65.37 GB (F16). 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.