I Ran Google's latest Gemma 4 Models on 48GB GPU. Here's What Actually Happened.
gemini gemma google gpu
| Source: Dev.to | Original article
Google’s latest Gemma 4 family landed on the open‑model market this week, and a hands‑on test on a single 48 GB GPU shows the line is more than a publicity stunt. The author of a popular AI‑dev blog ran the four released variants—2 B, 4 B, a 26 B mixture‑of‑experts (MoE) that activates only 4 B at inference time, and a dense 31 B model—on an RTX 4090‑class workstation. All four loaded without swapping, the MoE and dense models fitting comfortably within the 48 GB memory budget thanks to activation‑gating and efficient quantisation. Latency figures hovered around 12 ms per token for the 2 B and 4 B models, 22 ms for the MoE, and 35 ms for the 31 B, putting them on par with Llama 3‑8 B and noticeably faster than many proprietary offerings when run locally.
Why it matters is twofold. First, the results prove that Google’s claim of “small, fast, omni‑capable” open models holds up on consumer‑grade hardware, opening the door to truly offline AI assistants, on‑device code‑generation tools, and privacy‑preserving workloads that previously required cloud‑scale GPUs. Second, the performance parity with larger closed‑source models signals a shift in the open‑model ecosystem: developers can now choose a Google‑backed alternative without sacrificing speed or quality, potentially reshaping the market that has been dominated by Meta’s Llama and Mistral families.
What to watch next includes Google’s rollout of Agent Mode on Android, where the 4 B and MoE variants will power on‑device code refactoring and app‑building workflows. Community benchmarks on Arena.ai will soon reveal how Gemma 4 stacks up against the latest Llama 3 and Mistral‑7B releases. Finally, the upcoming integration of TurboQuant‑WASM for browser‑side inference could push the same models onto even lighter devices, extending the “local‑first” promise beyond high‑end workstations. As we reported on 4 April, deploying Gemma 4 on Cloud Run already demonstrated its cloud‑efficiency; the new workstation results complete the picture by confirming its edge‑ready credentials.
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