Local AI Advisor

Can Desktop PC · GeForce RTX 5080 · 32 GB RAM run Gemma 4 26B-A4B?

16 GB VRAM + 32 GB RAM · 960 GB/s memory bandwidth · Gemma 4 26B-A4B has 25.8B parameters (3.8B active per token).

Short answer

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

Best quantization for chat: Q3_K_M (12 GB of weights) on Ollama. Generation: 230–310 tok/s, leaving about 2.0 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 WebUIExcellentQ3_K_M230–310 tok/sunder 1 s8KDetails
Coding questionswith ContinueComfortableQ3_K_M220–300 tok/s≈1 s16KDetails
Coding in a repositorywith AiderAcceptableQ3_K_M200–270 tok/s≈3 s32KDetails
Agentic codingwith OpenCodeAcceptableQ3_K_M190–260 tok/s≈3 s32KDetails
Long documents (64K)with Open WebUIBorderlineQ3_K_M150–210 tok/s≈10 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 Q3_K_M build with a 32K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q3_K_XL

Downloads 12.91 GB (UD-Q3_K_XL). 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.