Local AI Advisor

Can Desktop PC · Intel Arc Pro B60 24GB · 64 GB RAM run Gemma 3 27B?

24 GB VRAM + 64 GB RAM · 456 GB/s memory bandwidth · Gemma 3 27B has 27.4B parameters.

Short answer

Yes, comfortable for chat; borderline for agentic coding.

Best quantization for chat: Q4_K_M (15 GB of weights) on LM Studio. Generation: 10–17 tok/s, leaving about 5.3 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 WebUIComfortableQ4_K_M10–17 tok/s≈5 s8KDetails
Coding questionswith ContinueAcceptableQ5_K_M9–14 tok/s≈35 s16KDetails
Coding in a repositorywith AiderAcceptableQ4_K_M10–16 tok/s≈2 min32KDetails
Agentic codingwith OpenCodeBorderlineQ4_K_M10–16 tok/s≈2 min32KDetails
Long documents (64K)with Open WebUIAcceptableQ3_K_M10–16 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-3-27b-it-GGUF:Q3_K_M

Downloads 13.44 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.

Open weights (restricted)Check my exact setup