What Is Gemma 4 and Why Wait for It?
Google released the Gemma 4 family as its most capable set of open-source models to date. Models are available ranging from 2 billion to 31 billion parameters, with each size targeting a different segment — from fast edge deployments to demanding professional applications. The key novelty is the Gemma 4 31B model with a context window of 256,000 tokens, and the Gemma 4 26B A4B built on a Mixture-of-Experts architecture, where only 4 billion parameters are activated per pass — dramatically reducing memory requirements while maintaining high accuracy.
On the independent MMLU Pro benchmark, Gemma 4 31B running on AMD MI355 scored 84.94% — comparable to the same model on NVIDIA B200 (84.72%), as demonstrated by the Modular team in their MAX framework test. This is one of the first public proofs that AMD hardware can compete with green cards even at the top level when running open models for inference.
Day Zero: What Does It Really Mean?
"Day Zero" support means that AMD ensured compatibility before or at the moment of the model's official release. This isn't just a marketing statement — in practice it means developers didn't have to wait for ROCm stack updates, patch fixes, or new library versions.
AMD announced this support on April 4, 2026 covering three main segments:
- AMD Instinct accelerators — professional cards for cloud data centers and enterprise deployments (MI300X, MI355, and others)
- Radeon graphics cards — for AI developer workstations and enthusiasts with powerful desktop or workstation GPUs
- Ryzen AI processors — laptops and mini PCs with a dedicated NPU unit on XDNA 2 architecture
Which Frameworks Are Supported?
One of AMD's main arguments is the breadth of ecosystem support. Gemma 4 can be run on AMD hardware through these tools:
- vLLM — the industry standard for LLM inference; available as a Docker image and Python package
- SGLang — alternative inference framework popular for low latency
- llama.cpp — lightweight solution for CPU and hybrid CPU+GPU deployments
- Ollama — the most popular tool for running models locally; as simple as
ollama pull gemma4 - LM Studio — graphical interface for users who don't want to work with the command line
- Lemonade — specialized server for NPU deployment on Ryzen AI devices
As WCCFTech notes, Gemma 4 models can be deployed on AMD GPUs via the vLLM framework almost immediately — both via Docker and via standard pip installation with ROCm backend.
Ryzen AI and NPU: The Future of Local AI on Laptops
A special chapter is the Ryzen AI processors with XDNA 2 architecture. AMD confirmed that the smaller Gemma 4 variants — specifically models labeled E2B and E4B — will receive full support for NPU units in the upcoming Ryzen AI software update. This means users of modern laptops (for example with Ryzen AI 300 or Ryzen AI MAX processors) will be able to run these models directly on the NPU chip, without burdening the GPU or CPU and with significantly lower energy consumption.
The Lemonade Server tool, which AMD prepared as part of the Ryzen AI Software stack, handles NPU inference management. Support will arrive as part of an upcoming update — AMD didn't specify an exact date but mentioned a "near-term" timeline.
AMD vs. NVIDIA: An Even Playing Field on Open Models
Historically the AI ecosystem was strongly tilted toward NVIDIA — thanks to the CUDA platform and years of tooling lead. But open-source models like Gemma 4 are changing this dynamic. Thanks to ROCm and growing support from frameworks like vLLM or SGLang, AMD hardware is becoming a viable alternative at least for inference.
Modular's comparative test showed their MAX framework achieving comparable results on AMD MI355 as on NVIDIA B200 — with the MMLU Pro benchmark on AMD actually two tenths higher. It's not a dramatic difference, but it's a clear signal: AMD is a real player in the race for AI inference.
What This Means for the Global AI Community
For developers, businesses, and enthusiasts worldwide, this news is relevant for several reasons:
First, Radeon GPUs are more accessible and price-friendly than equivalent NVIDIA cards — especially in the high-end desktop segment. Anyone with an RX 7900 XTX or a workstation with Radeon Pro can now fully experiment with Gemma 4 models.
Second, Ryzen AI laptops are widely available at major retailers globally. Once the update with NPU support for E2B/E4B models arrives, local AI inference will literally fit in your pocket — with minimal power consumption and no need for cloud connectivity.
Third, Gemma 4 is fully open-source under the Apache 2.0 license — it can be used commercially for free, without licensing fees, without sharing data with Google. For companies sensitive to GDPR or data sovereignty, this is an important factor.
Can I run Gemma 4 on a regular Radeon card, like an RX 7800 XT?
It depends on the model size and available VRAM. The Gemma 4 2B model can run on a card with 8 GB VRAM, while the 9B and 12B variants need 16 GB. For 27B and 31B models a minimum of 24 GB VRAM is recommended, or a combination of GPU+CPU offloading via llama.cpp. An RX 7800 XT with 16 GB VRAM handles mid-sized models without issue.
Do I need to install special drivers for AMD ROCm on Windows?
It depends on the chosen tool. LM Studio works via standard Adrenalin Edition drivers and doesn't require ROCm. For vLLM or SGLang, ROCm is necessary — but it's primarily designed for Linux. On Windows the easiest path is WSL2 with ROCm or directly LM Studio with the Ollama backend.
How does Gemma 4 differ from Gemma 3, which was released earlier?
Gemma 4 brings a significantly larger context window (256,000 tokens vs. 128,000 for Gemma 3), improved multimodal support, and a new Mixture-of-Experts architecture in the 26B A4B model. Results on standard benchmarks are consistently higher for Gemma 4, while hardware requirements for the MoE variant remain comparable to smaller dense models.