Local AI Advisor

Can Laptop · GeForce RTX 5090 Laptop · 32 GB RAM run Gemma 4 26B-A4B?

24 GB VRAM + 32 GB RAM · 896 GB/s memory bandwidth · Gemma 4 26B-A4B has 25.8B parameters (3.8B active per token).

Short answer

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

Best quantization for chat: Q5_K_M (17 GB of weights) on Ollama. Generation: 120–200 tok/s, leaving about 4.6 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 WebUIExcellentQ5_K_M120–200 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellentQ5_K_M120–190 tok/s≈1 s16KDetails
Coding in a repositorywith AiderComfortableQ6_K95–160 tok/s≈3 s32KDetails
Agentic codingwith OpenCodeComfortableQ6_K90–150 tok/s≈3 s32KDetails
Long documents (64K)with Open WebUIExcellentQ5_K_M85–150 tok/s≈10 s64KDetails

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 Q5_K_M build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q5_K_XL

Downloads 21.22 GB (UD-Q5_K_XL). 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.