Local AI Advisor

Can MacBook Air M5 24 GB run Gemma 4 26B-A4B?

24 GB unified · 153 GB/s memory bandwidth · Gemma 4 26B-A4B has 25.8B parameters (3.8B active per token).

Short answer

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

Best quantization for chat: 4-bit (14 GB of weights) on LM Studio. Generation: 25–45 tok/s, leaving about 1.6 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 WebUIComfortable4-bit25–45 tok/sunder 1 s8KDetails
Coding questionswith ContinueComfortable3-bit30–55 tok/s≈4 s16KDetails
Coding in a repositorywith AiderAcceptable3-bit30–45 tok/s≈15 s32KDetails
Agentic codingwith OpenCodeAcceptable3-bit25–45 tok/s≈20 s32KDetails
Long documents (64K)with Open WebUIComfortable3-bit20–35 tok/s≈50 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 3-bit 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-Q3_K_XL

Downloads 12.91 GB (UD-Q3_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.