Local AI Advisor

Can Desktop PC · GeForce RTX 5090 · 64 GB RAM run Phi-4 (14B)?

32 GB VRAM + 64 GB RAM · 1792 GB/s memory bandwidth · Phi-4 (14B) has 14.7B parameters.

Short answer

Yes, excellent for chat; borderline for 64K-token documents.

Best quantization for chat: Q6_K (11 GB of weights) on Ollama. Generation: 75–100 tok/s, leaving about 17 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_K75–100 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellentQ8_055–75 tok/s≈1 s16KDetails
Coding in a repositorywith AiderAcceptableQ6_K60–85 tok/s≈3 s16KDetails
Agentic codingwith OpenCodeAcceptableQ6_K60–85 tok/s≈3 s16KDetails
Long documents (64K)with Open WebUIBorderlineQ6_K60–85 tok/s≈3 s16KDetails

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 16K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/phi-4-GGUF:Q6_K

Downloads 12.03 GB (Q6_K). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=16384 before `ollama serve` for long prompts.

Different tool, context size or workload?

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