Local AI Advisor

Can Desktop PC · RTX PRO 6000 Blackwell · 128 GB RAM run Qwen3 14B?

96 GB VRAM + 128 GB RAM · 1792 GB/s memory bandwidth · Qwen3 14B has 14.8B parameters.

Short answer

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

Best quantization for chat: Q6_K (11 GB of weights) on Ollama. Generation: 70–95 tok/s, leaving about 81 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_K70–95 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellentQ6_K70–90 tok/s≈1 s16KDetails
Coding in a repositorywith AiderAcceptableQ6_K60–80 tok/s≈3 s32KDetails
Agentic codingwith OpenCodeAcceptableQ6_K55–75 tok/s≈4 s32KDetails
Long documents (64K)with Open WebUIAcceptableQ6_K50–70 tok/s≈7 s32KDetails

Speeds are estimates calibrated against measured benchmarks on similar hardware. “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 32K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3-14B-GGUF:Q6_K

Downloads 12.12 GB (Q6_K). 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.