Google’s release of the Gemma 4 series has redefined what is possible for local AI execution on consumer and enterprise hardware. If you are looking for the latest gemma 4 speed benchmark results, you have come to the right place to understand how these models stack up against the competition. These models, ranging from a tiny 2B parameter version to a powerful 31B dense transformer, are specifically optimized for high-performance reasoning and agentic workflows.
Understanding the gemma 4 speed benchmark is crucial for developers, gamers, and AI enthusiasts who want to run frontier-level intelligence on their own machines. By shifting away from cloud-based APIs, users can leverage the "intelligence per parameter" focus of Gemma 4 to achieve results that previously required models 20 times their size. Whether you are running an RTX 5090 or a Mac Studio, the performance gains in 2026 are nothing short of revolutionary.
Gemma 4 Model Family Overview
The Gemma 4 family is divided into four distinct sizes, each tailored for specific hardware constraints and use cases. The primary innovation in 2026 is the introduction of the Mixture-of-Experts (MoE) architecture in the mid-range model, which allows for incredible speeds by only activating a fraction of its parameters during inference.
| Model | Type | Active Parameters | Target Device |
|---|---|---|---|
| Gemma 4 2B | Dense | 2.3 Billion | Mobile & Edge |
| Gemma 4 4B | Dense | 4.5 Billion | Strong Edge/Multimodal |
| Gemma 4 26B-A4B | MoE | 3.8 Billion | Desktop/Workstation |
| Gemma 4 31B | Dense | 31 Billion | High-End GPU/Server |
💡 Tip: For the best balance of speed and intelligence, the 26B-A4B MoE model is the "sweet spot" for most home users, offering speeds comparable to the 4B model with the reasoning capabilities of a much larger system.
Gemma 4 Speed Benchmark: GPU Performance Analysis
When evaluating a gemma 4 speed benchmark, the hardware choice is the most significant factor. With the arrival of the RTX 50-series GPUs in 2026, we see a massive leap in tokens per second (t/s). The following data compares the flagship 31B dense model across the top three tiers of NVIDIA consumer hardware.
RTX 3090 vs 4090 vs 5090 (31B Dense Model)
| GPU | VRAM | Speed (Tokens/Sec) | Performance Gain |
|---|---|---|---|
| RTX 3090 | 24 GB | 35.7 t/s | Baseline |
| RTX 4090 | 24 GB | 42.3 t/s | +18% |
| RTX 5090 | 32 GB | 64.88 t/s | +81% |
As shown, the RTX 5090 acts as a significant outlier, nearly doubling the performance of the aging 3090. This is largely due to the increased memory bandwidth and the 32GB VRAM buffer, which allows the 31B model to run with less aggressive quantization.
The Mixture-of-Experts (MoE) Speed Advantage
The most impressive gemma 4 speed benchmark results come from the 26B-A4B model. Because it uses a Mixture-of-Experts architecture, it only activates 3.8 billion parameters at any given time. This allows it to bypass the memory bandwidth bottlenecks that slow down dense models like the 31B.
26B-A4B MoE Inference Speeds
| Hardware | Speed (Tokens/Sec) | Efficiency |
|---|---|---|
| RTX 5090 | 182 t/s | Exceptional |
| RTX 4090 | 147 t/s | High |
| RTX 3090 | 120 t/s | Solid |
| Mac Studio M2 Ultra | 300 t/s | Unified Memory Peak |
For users running agentic workflows—where the AI must "think" through multiple steps and call various tools—the 182+ t/s speed on an RTX 5090 makes the interaction feel instantaneous. This specific gemma 4 speed benchmark highlights why MoE is becoming the standard for local AI deployment.
Enterprise Benchmarks: NVIDIA DGX Spark (Grace Blackwell)
For professional environments, the NVIDIA DGX Spark (utilizing the GB10 Grace Blackwell Superchip) provides a different perspective on performance. While consumer GPUs focus on raw generation speed, unified memory systems like the DGX Spark excel at "Prompt Processing" (prefill), which is vital for long-context tasks.
| Model Config | Prompt Processing (2048 tokens) | Decode Speed (Peak) |
|---|---|---|
| 31B (BF16) | 1066 t/s | 4.0 t/s |
| 31B (AWQ int4) | 810 t/s | 11.0 t/s |
| 26B-A4B (MoE) | 3105 t/s | 24.0 t/s |
⚠️ Warning: On unified memory systems like the DGX Spark or Mac, token generation is often limited by LPDDR5X bandwidth rather than compute power. If you require high-speed generation for long documents, prioritize HBM-based datacenter cards or high-quantization (int4) recipes.
Real-World Capabilities and Agentic Logic
Beyond the raw gemma 4 speed benchmark numbers, the quality of the output remains competitive with much larger models. Google has integrated "agent skills" that allow the model to run entirely on-device, even on mobile phones. This enables the AI to reason through structured data, use tools, and execute multi-step tasks without a cloud connection.
In testing, the 31B model has successfully completed the following complex tasks:
- Mac OS Clone: Created a functional web-based UI with a toolbar, terminal, and calculator.
- F1 Donut Simulator: Coded a 3D physics simulation in raw browser code.
- Game Logic: Handled state management and turn-based scoring for a complex cardboard car game.
- Visual Reasoning: Analyzed and compared multiple images to extract shared patterns.
The 31B model currently ranks #3 among open models on the LM Arena leaderboard, trailing only slightly behind Qwen 3.5 27B, but using significantly fewer tokens to achieve similar results. You can access these models for testing via Google AI Studio for free.
How to Optimize Your Gemma 4 Setup
To get the most out of your hardware and maximize your gemma 4 speed benchmark scores, follow these optimization steps:
- Use the Right Harness: For agentic tasks, use the Kilo CLI. It is specifically designed to leverage the function-calling capabilities of Gemma 4.
- Choose Quantization Wisely: If you have 24GB of VRAM, run the 31B model in AWQ int4. This delivers roughly 3x the speed of the standard BF16 precision with minimal loss in intelligence.
- Update Drivers: Ensure you are on CUDA 13.0 or higher (driver 580.142+) to take advantage of the latest vLLM kernel optimizations.
- Enable Flash Attention: Gemma 4 uses heterogeneous head dimensions (256/512). Ensure your inference engine (like llama.cpp or vLLM) is using the Triton or Flash Attention backends.
FAQ
Q: What is the best hardware for running a Gemma 4 speed benchmark at home?
A: The NVIDIA RTX 5090 is currently the top performer for consumer builds, reaching over 64 t/s on the 31B model. However, a Mac Studio with an M2 or M3 Ultra is superior for the 26B-A4B MoE model due to its massive unified memory bandwidth.
Q: Can Gemma 4 run on a mobile phone?
A: Yes. The 2B and 4B "Edge" models are designed specifically for mobile devices and Raspberry Pi boards. Google’s "Agent Skills" update allows these models to run locally on your phone to process your data privately.
Q: How does Gemma 4 compare to Llama 4 Scout?
A: While Llama 4 Scout offers a larger 10-million-token context window, Gemma 4 is often faster and more efficient for tasks under 256k tokens. Gemma 4 typically uses 2.5x fewer tokens for similar reasoning tasks, making it cheaper and faster for real-world applications.
Q: Which model should I use for coding?
A: The 31B Dense model is the strongest for coding, scoring over 80% on LiveCodeBench. If you are on a memory-constrained system, the 26B-A4B MoE is a viable alternative that maintains high-quality structured JSON output.