gemma 4

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Editorial infographic of a mixture-of-experts model: a few active expert blocks on a GPU and the rest resting in system memory, near-black background, indigo-violet with a coral accent

Gemma 4 26B-A4B locally: the Gemma that runs fast on 16GB

Gemma 4's mixture-of-experts model holds about 26B parameters but activates only 3.8B — so it's quick, and it fits a 16GB card at Q4. The VRAM math, the GPU, and the run command.

Aliteq Team · Sep 26 · 7 min

Realistic painterly illustration of a large glowing processor die radiating soft light on a dark surface, deep indigo-violet with a warm coral highlight

Gemma 4 31B locally: the 24GB-card Gemma

Gemma 4 31B is the big dense Gemma — the one that asks for a 24GB card and rewards it. The VRAM per quant, the GPU that fits, and how to run it without pulling the wrong tag.

Aliteq Team · Sep 26 · 7 min

Isometric diorama of a friendly glowing AI chip seated on a mid-range graphics card, near-black background, indigo-violet with a coral accent

Gemma 4 12B locally: the VRAM, the GPU, and one Ollama gotcha

Google's Gemma 4 12B is the multimodal open model that fits a mid-range card — if you pull the right Ollama tag. The honest VRAM math, the cheapest GPU that runs it, and how to start.

Aliteq Team · Sep 26 · 7 min

Flat illustration of a person choosing between small, medium and large boxes to fit into a slot on a graphics card, a small robot helper watching

Agents

Run OpenClaw or Hermes on a local model: which fits your GPU

Ollama's default gemma4 is the small E4B and qwen3.5 is the 9B, not what most people assume. What each tag really downloads, what the bigger models need at the 64K context agents use, and what to pick for 12, 16 and 24GB cards.

Aliteq Team · Sep 25 · 7 min

A desktop PC with a mid-range graphics card installed, the kind of 8GB hardware this guide is written for

your 8GB graphics card can run real local AI — you're probably picking the wrong model

Qwen3.5 9B, Gemma 4 and the older Gemma 3 and Phi-4 Mini all claim to fit. We ran each through our VRAM calculator at 8K and 32K context to see which ones actually leave room to breathe.

Aliteq Team · Aug 4 · 6 min

Ollama, the local-model runtime used to run open-weight LLMs on consumer GPUs

a free model on your 24GB GPU codes within 4 points of Claude Opus. meet Qwen3.6-27B

on a single 24GB card there's still no one 'best' model. Qwen3.6-27B for coding (Q5_K_M with 32K context on our numbers), Qwen3.6 35B-A3B or Gemma 4 26B-A4B for fast general use, gpt-oss-20b at Q8_0, and Gemma 4 31B for vision if you keep context short.

Aliteq Team · Jul 19 · 7 min