Google Gemma 4 for Local AI: GPU Sizing and Performance Guide (2026)

The landscape of local AI changed significantly on April 2, 2026, with the release of Google's Gemma 4. For the first time, Google has moved away from its restrictive custom licenses to the industry-standard Apache 2.0 license. This shift is a massive win for developers and enterprise legal teams who previously flagged Gemma as a blocker for production use.

One of the most striking metrics coming from the community is the performance of the 26B Mixture-of-Experts (MoE) model. On an NVIDIA RTX 4090, it achieves a staggering 149 tokens per second (tok/s). This is not a clerical error; it is the result of the new 'A4B' architecture. While the model has 26 billion total parameters, the MoE routing only activates approximately 4 billion parameters per token inference. This allows for the reasoning quality of a 26B model with the inference speed of a 4B-class model.

However, deploying these models locally requires a nuanced understanding of hardware, specifically VRAM and memory bandwidth. If you find local deployment too resource-intensive, platforms like n1n.ai provide a streamlined way to access high-performance LLMs without the hardware overhead.

The Gemma 4 Lineup: Architecture and Specs

Google has structured Gemma 4 into two distinct tiers: the 'E-series' (Efficient) and the high-capacity models. Understanding the labels is crucial for GPU selection.

The 'E' Series: Efficient and Multimodal

The E2B and E4B models utilize Per-Layer Embeddings (PLE). This technique allows the model to pack higher parameter capacity into less active computation. These models are optimized for edge devices and tight memory budgets. Interestingly, they support native audio input (ASR and speech-to-translated-text), a feature currently missing from the larger 26B and 31B variants.

The 26B A4B MoE: The Speed King

The 'A4B' designation stands for 'Active 4 Billion.' While the full 26B weight file must be loaded into VRAM, the compute cost per token tracks with a much smaller model. This is why the 26B MoE outperforms the 31B Dense model in speed by nearly 5x on identical hardware. For developers building RAG (Retrieval-Augmented Generation) pipelines with LangChain, this speed is a game-changer.

VRAM Requirements and Quantization Strategies

Memory is the primary bottleneck for local AI. While the 26B MoE only 'uses' 4B parameters for calculation, the entire 26B model must reside in your GPU's memory to avoid the massive latency of system RAM offloading.

Using GGUF formats via Ollama or llama.cpp is the standard approach for consumer hardware. Here is the VRAM breakdown for the most common quantization levels:

Pro Tip: For the 26B MoE, Q4_K_M is the 'Goldilocks' zone. It fits within 24GB VRAM cards like the RTX 3090 or 4090 while leaving room for a significant KV cache. If you need even higher performance or access to models like Claude 3.5 Sonnet or OpenAI o3 without buying multiple GPUs, n1n.ai offers a unified API to access these top-tier models instantly.

Real-World Performance Benchmarks

Inference speed is determined by memory bandwidth. The RTX 4090 has a bandwidth of 1,008 GB/s.

1. 26B MoE (Q4_K_M): The active weight window is about 2 GB. Theoretical limit: 1,008 GB/s / 2 GB = 504 tok/s. Real-world bottlenecks (overhead) bring this down to ~149 tok/s.
2. 31B Dense (Q4_K_M): All 18 GB of weights must be read per token. Theoretical limit: 1,008 GB/s / 18 GB = 56 tok/s. Real-world performance lands around 28–35 tok/s.

GPU Comparison Table (Tokens/Second)

Note: Performance on 16GB cards is 'Context Limited.' Once the KV cache exceeds the remaining VRAM, speeds drop to single digits (5–10 tok/s) as the system offloads to DDR5 RAM.

The 16GB Dilemma: Is it Enough?

If you own an RTX 5060 Ti or 5070 Ti with 16GB of VRAM, running the 26B MoE is possible but tricky. The model at Q4_K_M requires ~17GB of total VRAM when accounting for the runtime buffer and KV cache.

When VRAM is exceeded, Ollama will offload the 'overflow' to your system RAM. If you are analyzing a short snippet of code, you won't notice. However, if you are performing a deep RAG analysis on a 50-page PDF, the context will quickly fill the 16GB VRAM, and your generation speed will fall off a cliff.

Solutions for 16GB Users:

• Limit Context: Set num_ctx to 2048 in your Modelfile. This keeps the KV cache small enough to stay on the GPU.
• Aggressive Quantization: Use Q3_K_M (~12 GB), though you will see a measurable drop in reasoning quality, especially in complex coding tasks.
• Hybrid Approach: Use local models for simple tasks and route complex queries to n1n.ai to handle large context windows and advanced reasoning.

Reasoning and Coding Benchmarks

As of April 2026, the Gemma 4 31B Dense model is the new king of the 'under 70B' open-weight category. It rivals models like Llama 3.3 70B in several key benchmarks:

• MMLU: 85.2% (31B Dense) vs 82.6% (26B MoE)
• AIME 2026: 89.2% (31B Dense)
• LiveCodeBench v6: 80.0% (31B Dense)

The 31B Dense model is superior for multi-hop legal reasoning and extreme competitive math. However, for 95% of developer workflows—such as boilerplate generation, unit testing, and documentation—the 26B MoE is the better choice due to its speed.

Conclusion

Gemma 4 represents a massive leap in efficiency. The combination of PLE for small models and MoE for large models allows Google to compete directly with the likes of DeepSeek-V3 and Meta's Llama series. For local users, the 24GB VRAM tier (RTX 3090/4090) remains the sweet spot for the 26B MoE, while 16GB users must carefully manage their context windows.

If you need the power of these models without the hardware headache, or if you want to compare Gemma 4 against other giants like Claude 3.5 or GPT-5, n1n.ai provides the most stable and high-speed API access in the industry.

Get a free API key at n1n.ai.