Local AI Advisor

Can Desktop PC · GeForce RTX 4080 SUPER · 32 GB RAM run Gemma 4 26B-A4B?

16 GB VRAM + 32 GB RAM · 736 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: 170–230 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_M170–230 tok/sunder 1 s8KDetails
Coding questionswith ContinueComfortableQ3_K_M170–220 tok/s≈1 s16KDetails
Coding in a repositorywith AiderAcceptableQ3_K_M150–200 tok/s≈2 s32KDetails
Agentic codingwith OpenCodeAcceptableQ3_K_M140–190 tok/s≈3 s32KDetails
Long documents (64K)with Open WebUIBorderlineQ3_K_M110–150 tok/s≈9 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.