Local AI Advisor

Can Desktop PC · GeForce RTX 5070 Ti · 32 GB RAM run Gemma 4 12B?

16 GB VRAM + 32 GB RAM · 896 GB/s memory bandwidth · Gemma 4 12B has 12B parameters.

Short answer

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

Best quantization for chat: Q6_K (9.3 GB of weights) on Ollama. Generation: 60–80 tok/s, leaving about 4.4 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_K60–80 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellentQ8_045–60 tok/s≈2 s16KDetails
Coding in a repositorywith AiderAcceptableQ6_K55–75 tok/s≈4 s32KDetails
Agentic codingwith OpenCodeAcceptableQ6_K55–75 tok/s≈5 s32KDetails
Long documents (64K)with Open WebUIExcellentQ6_K55–70 tok/s≈15 s64KDetails

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 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/gemma-4-12B-it-GGUF:Q6_K

Downloads 9.79 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.