Local AI Advisor

Can Desktop PC · Ryzen 9 9950X (no GPU) · 64 GB DDR5 run Gemma 4 12B?

64 GB RAM, no GPU · Gemma 4 12B has 12B parameters.

Short answer

Yes, acceptable for chat; borderline for coding in a repository.

Best quantization for chat: Q4_K_M (6.6 GB of weights) on llama.cpp (llama-server). Generation: 5–9 tok/s, leaving about 45 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 WebUIAcceptableQ4_K_M5–9 tok/s≈5 s8KDetails
Coding questionswith ContinueAcceptableQ3_K_M6–10 tok/s≈35 s16KDetails
Coding in a repositorywith AiderBorderlineQ4_K_M5–8 tok/s≈2 min32KDetails
Agentic codingwith OpenCodeBorderlineQ5_K_M4.1–7 tok/s≈2 min32KDetails
Long documents (64K)with Open WebUIAcceptableQ3_K_M5–9 tok/s≈8 min64KDetails

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 Q3_K_M build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/gemma-4-12B-it-GGUF:Q3_K_M

Downloads 5.69 GB (Q3_K_M). 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.