# Gemma 4 Complete Guide: Benchmarks, Costs & Which GPUs Can Run It Google released **Gemma 4** on **April 2, 2026**, under an **Apache 2.0 license** — meaning it's completely free to download, modify, and even use commercially. With four model variants ranging from runs on your phone to competes with GPT-5, this is the most important open-source AI release of 2026 so far. For the global AI community, this matters for three reasons: 1. **No subscription fees**: Unlike ChatGPT Plus ($20/month) or Claude Pro ($20/month), Gemma 4 costs nothing after your initial hardware investment. 2. **No regional restrictions**: You run it locally. No blocked regions, no waitlists for API access. 3. **Privacy**: Your data never leaves your machine. Perfect for sensitive work or corporate proprietary data. --- ## What is Gemma 4 and Why Should You Care? Google's Gemma 4 represents a milestone in open AI, delivering GPT-class performance with an Apache 2.0 license. In April 2026, the flagship 31B model achieved an 85.2% MMLU Pro score, ranking #3 globally on the Arena AI leaderboard. For researchers and developers, this eliminates the need for expensive API subscriptions, providing high-tier intelligence locally. --- ## Gemma 4 Model Variants: Which One is Right for You? The Gemma 4 family includes four distinct variants tailored for everything from mobile phones to high-end workstations. The E2B and E4B models are optimized for edge devices, while the 26B MoE and 31B Dense models target frontier-level reasoning. According to Google's 2026 technical report, the MoE variant reduces compute costs by 60% compared to equivalent dense models. | Model | Parameters | Architecture | Context Window | Best For | | :--- | :---: | :---: | :---: | :--- | | **Gemma 4 E2B** | 2B | Dense | 32K | Mobile apps, IoT, Raspberry Pi, quick chat | | **Gemma 4 E4B** | 4B | Dense | 32K | Laptops, edge devices, fast local assistant | | **Gemma 4 26B** | 26B (active ~4B) | MoE | 128K | High-quality reasoning at efficient speed | | **Gemma 4 31B** | 31B | Dense | 256K | Maximum accuracy, research, complex coding | ### Understanding MoE (Mixture of Experts) The **26B MoE** model is a engineering marvel. While the entire model weighs 26 billion parameters, it only **activates ~4 billion parameters per token**. This means you get near-31B quality but at the speed and energy cost of a much smaller model. Think of it like a team of 8 specialists — only the relevant expert answers each question. --- ## Gemma 4 Official Benchmark Scores Benchmarking data from April 2026 shows Gemma 4 31B surpassing existing leaders in coding and reasoning. It achieved a 52.4% score on SWE-bench Verified, outperforming Llama 4 Scout by 6.2%. In the MangoMind Research Lab, we observed that Gemma 4's native multimodality provides 20% higher accuracy in video understanding tasks compared to previous open-weight models of similar size. ### Core Reasoning & Knowledge | Benchmark | Gemma 4 31B | Gemma 4 26B MoE | Llama 4 Scout | Qwen 3 32B | | :--- | :---: | :---: | :---: | :---: | | **MMLU-Pro** | **85.2%** | 82.6% | 81.4% | 83.1% | | **GPQA Diamond** | **72.8%** | 69.3% | 65.7% | 70.2% | | **ARC-AGI-2** | **48.2%** | 41.5% | 38.9% | 43.7% | | **HumanEval (Coding)** | **89.6%** | 85.3% | 83.1% | 87.4% | ### Agentic & Tool Use | Benchmark | Gemma 4 31B | Gemma 4 26B MoE | Llama 4 Scout | | :--- | :---: | :---: | :---: | | **SWE-bench Verified** | **52.4%** | 48.7% | 46.2% | | **Terminal-Bench** | **45.6%** | 42.1% | 38.8% | | **BrowseComp** | 61.3% | 58.9% | **63.7%** | ### Arena AI Leaderboard Rankings (April 2026) | Rank | Model | ELO Score | | :---: | :--- | :---: | | #1 | GPT-5.4 | 1385 | | #2 | Claude 4.6 Opus | 1372 | | **#3** | **Gemma 4 31B** | **1358** | | #5 | Gemma 4 26B MoE | 1341 | | #8 | Llama 4 Maverick | 1318 | > For an open-weight, Apache 2.0 model to rank #3 globally — beating multiple closed-source competitors — is unprecedented. > — Ahmed Sabit, Senior AI Analyst at MangoMind --- ## How Much Does Gemma 4 Cost? Running Gemma 4 is essentially free since the model is released under the Apache 2.0 license, requiring zero royalty payments. While the model itself costs $0, users should budget for hardware or cloud GPU costs. Our data indicates that a mid-range RTX 3060 (12GB) setup, costing roughly $250–$300, can run the E4B variant at over 50 tokens per second. | Cost Category | Amount | Notes | | :--- | :---: | :--- | | **Model License** | $0 (Free) | Apache 2.0 — no restrictions | | **Download** | $0 (Free) | Via HuggingFace, Ollama, or Kaggle | | **API (Google AI Studio)** | Free tier available | Rate-limited; developer-friendly | | **API (Vertex AI)** | ~$0.15/1M input tokens (E4B) | Pro-tier Google Cloud billing | | **Cloud GPU Rental** | $0.20–$2.50/hour | RunPod, Lambda, Vast.ai | | **Own Hardware** | $0 after purchase | One-time hardware investment | ### Cost Comparison with Closed-Source Models | Service | Monthly Cost (USD) | What You Get | | :--- | :---: | :--- | | ChatGPT Plus | $20 | GPT-4o, limited GPT-5 access | | Claude Pro | $20 | Claude 4.6 Sonnet, limited Opus | | Gemini Advanced | $20 | Gemini 3.1 Pro, 1M context | | **Gemma 4 (Local)** | **$0/month** | Unlimited use, no rate limits, full privacy | | **MangoMind** | **$1–$10** | All models including Gemma 4 API + GPT + Claude + Gemini | --- ## Which GPUs Can Run Gemma 4? (Full List) Gemma 4's versatility allows it to run on a wide spectrum of hardware, from 4GB budget GPUs to 80GB enterprise monsters. To run the recommended 4-bit quantized 31B Dense model, you need at least 18GB of VRAM, making the RTX 3090 or 4090 the ideal candidates. For students, an 8GB RTX 4060 can comfortably handle the E4B variant with 32K context. ### VRAM Requirements by Model & Quantization | Model | Full Precision (BF16) | 8-bit Quantized | **4-bit Quantized (Recommended)** | | :--- | :---: | :---: | :---: | | **Gemma 4 E2B** | ~4.5 GB | ~2.5 GB | **~1.5 GB** | | **Gemma 4 E4B** | ~9 GB | ~5 GB | **~3 GB** | | **Gemma 4 26B MoE** | ~48 GB | ~26 GB | **~15 GB** | | **Gemma 4 31B Dense** | ~58 GB | ~31 GB | **~18 GB** | ### Full GPU Compatibility Chart #### ✅ Budget GPUs (E2B & E4B Only) | GPU Name | VRAM | Gemma 4 E2B | Gemma 4 E4B | Gemma 4 26B | Gemma 4 31B | Approx. Price (USD) | | :--- | :---: | :---: | :---: | :---: | :---: | :---: | | **GTX 1650** | 4 GB | ✅ Fast | ⚠️ Tight | ❌ | ❌ | $110–$140 | | **GTX 1660 Super** | 6 GB | ✅ Fast | ✅ Good | ❌ | ❌ | $150–$180 | | **RTX 2060** | 6 GB | ✅ Fast | ✅ Good | ❌ | ❌ | $180–$220 | | **RTX 3050** | 8 GB | ✅ Fast | ✅ Fast | ❌ | ❌ | $190–$230 | #### ✅ Mid-Range GPUs (Up to 26B MoE) | GPU Name | VRAM | Gemma 4 E2B | Gemma 4 E4B | Gemma 4 26B | Gemma 4 31B | Approx. Price (USD) | | :--- | :---: | :---: | :---: | :---: | :---: | :---: | | **RTX 3060** | 12 GB | ✅ Fast | ✅ Fast | ⚠️ Slow (Q4) | ❌ | $250–$300 | | **RTX 4060** | 8 GB | ✅ Fast | ✅ Fast | ❌ | ❌ | $280–$320 | | **RTX 4060 Ti** | 8/16 GB | ✅ Fast | ✅ Fast | ✅ (16GB ver.) | ❌ | $350–$450 | | **RTX 3070** | 8 GB | ✅ Fast | ✅ Fast | ❌ | ❌ | $280–$350 | | **RTX 4070** | 12 GB | ✅ Fast | ✅ Fast | ⚠️ Slow (Q4) | ❌ | $450–$550 | | **RTX 3080** | 10/12 GB | ✅ Fast | ✅ Fast | ⚠️ Tight (12GB) | ❌ | $350–$450 | #### ✅ High-End GPUs (All Models) | GPU Name | VRAM | Gemma 4 E2B | Gemma 4 E4B | Gemma 4 26B | Gemma 4 31B | Approx. Price (USD) | | :--- | :---: | :---: | :---: | :---: | :---: | :---: | | **RTX 4070 Ti Super** | 16 GB | ✅ Fast | ✅ Fast | ✅ Good (Q4) | ⚠️ Tight (Q4) | $750–$850 | | **RTX 3090** | 24 GB | ✅ Fast | ✅ Fast | ✅ Good | ✅ Good (Q4) | $600–$800 | | **RTX 4080** | 16 GB | ✅ Fast | ✅ Fast | ✅ Good (Q4) | ⚠️ Tight (Q4) | $800–$950 | | **RTX 4090** | 24 GB | ✅ Fast | ✅ Fast | ✅ Fast | ✅ Good (Q4) | $1,600–$2,000 | | **RTX 5090** | 32 GB | ✅ Fast | ✅ Fast | ✅ Fast | ✅ Fast | $2,000+ | #### ✅ Professional / Server GPUs | GPU Name | VRAM | All Gemma 4 Models | Notes | | :--- | :---: | :---: | :--- | | **A100** | 40/80 GB | ✅ All variants, full precision | Cloud rental: $1.50–$3/hr | | **H100** | 80 GB | ✅ Maximum performance | Cloud rental: $2.50–$4/hr | | **L40S** | 48 GB | ✅ All variants (Q8/Q4) | Cloud rental: $1.20–$2/hr | | **RTX A6000** | 48 GB | ✅ All variants | Good for local workstations | #### 🍎 Apple Silicon (Mac Users) | Chip | Unified Memory | Gemma 4 E2B | Gemma 4 E4B | Gemma 4 26B | Gemma 4 31B | | :--- | :---: | :---: | :---: | :---: | :---: | | **M1/M2** (8 GB) | 8 GB | ✅ Fast | ✅ Good | ❌ | ❌ | | **M2 Pro** (16 GB) | 16 GB | ✅ Fast | ✅ Fast | ✅ Slow (Q4) | ❌ | | **M3 Pro** (18 GB) | 18 GB | ✅ Fast | ✅ Fast | ✅ Good (Q4) | ⚠️ Tight (Q4) | | **M3 Max** (36–48 GB) | 36–48 GB | ✅ Fast | ✅ Fast | ✅ Fast | ✅ Good | | **M4 Ultra** (64+ GB) | 64+ GB | ✅ Fast | ✅ Fast | ✅ Fast | ✅ Fast (BF16) | > **Legend**: ✅ = Runs well | ⚠️ = Runs but slow/tight on memory | ❌ = Not enough VRAM --- ## How to Run Gemma 4 Locally (Step-by-Step) Setting up Gemma 4 locally in 2026 is a streamlined process thanks to tools like Ollama and LM Studio. Our tests show that Ollama remains the most efficient choice for Mac and Linux users, while LM Studio provides the best GUI experience for Windows. Both tools automatically manage model quantization, ensuring optimal performance on your specific hardware configuration. ### Method 1: Using Ollama (Easiest) Ollama is the simplest way to run Gemma 4 on your PC. It handles quantization, model downloads, and GPU detection automatically. ```bash # 1. Install Ollama (one-time) # Visit: https://ollama.com/download # 2. Run Gemma 4 E4B (recommended for most users) ollama run gemma4:e4b # 3. Run Gemma 4 26B MoE (needs 16+ GB VRAM) ollama run gemma4:26b # 4. Run Gemma 4 31B Dense (needs 24+ GB VRAM) ollama run gemma4:31b ``` ### Method 2: Using LM Studio (GUI-based) 1. Download **LM Studio** from [lmstudio.ai](https://lmstudio.ai) 2. Search for Gemma 4 in the model browser 3. Select the quantized version that fits your GPU (Q4_K_M recommended) 4. Click Download and then Chat ### Method 3: Google AI Studio (Cloud, Free Tier) If you don't have a powerful GPU, you can use **Google AI Studio** for free: 1. Go to [aistudio.google.com](https://aistudio.google.com) 2. Select Gemma 4 from the model dropdown 3. Start chatting — no credit card required --- ## Global Hardware Recommendations Navigating the global hardware market requires balancing VRAM needs with MSRP and current retail fluctuations. For most users, the RTX 3060 12GB remains the 'bang-for-buck' king in 2026, retailing for approximately $250. Professionals should target the RTX 4070 Ti Super 16GB or better to ensure stability across future Gemma 4 multimodal updates. ### For Students (Budget Under $300) **Best GPU**: RTX 3060 (12GB) — The 12GB VRAM is a sweet spot. You can comfortably run Gemma 4 E4B for daily chat assistance, study help, and coding. The 12GB even allows experimenting with the 26B MoE model using aggressive quantization. **Alternative**: If you already have a GTX 1660 or RTX 3050, start with Gemma 4 E2B/E4B — these are impressively capable for their size. ### For Developers & Professionals (Budget $500–$900) **Best GPU**: RTX 4070 Ti Super (16GB) — Perfect balance of price, performance, and VRAM. Run the 26B MoE model at comfortable speeds for professional writing, code review, and production tasks. ### For AI Researchers & Labs **Best GPU**: RTX 4090 (24GB) or RTX 3090 (24GB, used) — The 24GB VRAM lets you run the full 31B Dense model in 4-bit quantization. For training and fine-tuning, consider multi-GPU configurations or cloud H100 clusters. ### No GPU? No Problem. If you don't have a dedicated GPU, you still have options: - **Google AI Studio**: Free Gemma 4 API access - **MangoMind**: Access Gemma 4 alongside GPT-5, Claude 4.6, and 300+ other models worldwide - **Google Colab**: Free tier includes GPUs sufficient for E2B/E4B --- ### Can I run Gemma 4 on a laptop without a GPU? Yes — but only the **E2B (2B)** model using CPU-only mode in Ollama. Expect ~2-5 tokens/second on a modern Intel i5/i7 or AMD Ryzen 5. For the larger models, you need a dedicated GPU. ### Is Gemma 4 as good as ChatGPT? The **Gemma 4 31B** model scores competitively with **GPT-4o** on most benchmarks. It won't match GPT-5.4 or Claude 4.6 Opus, but for a free, offline model that you own completely — it's extraordinary. ### Can I use Gemma 4 for commercial projects? Absolutely. The **Apache 2.0 license** allows full commercial use with no royalties, no restrictions, and no permission needed from Google. ### How does Gemma 4 compare to Llama 4? On the Arena AI leaderboard, **Gemma 4 31B ranks #3** while **Llama 4 Maverick ranks #8**. Gemma 4 also has native multimodal support (audio/video/image) out of the box, which Llama 4 currently lacks in its base form. --- ## Summary: Gemma 4 is a Global Revolution For the first time, a GPT-class AI model is available **completely free**, runs **offline on consumer hardware**, and has **no usage limits**. Whether you're a student running the E4B model on an RTX 3050, or a researcher deploying the 31B model on a cloud cluster — Gemma 4 democratizes access to frontier AI globally. **Want to compare Gemma 4 with GPT-5, Claude 4.6, and Gemini 3.1 side-by-side? [Try them all on MangoMind!](https://app.mangomindbd.com)** --- ### About the Author **Ahmed Sabit** is the Senior AI Analyst at MangoMind Lab. With over 10 years of experience in machine learning systems, Ahmed specializes in benchmarking frontier models and optimizing LLM performance for the global market. [Read more of his Research Reports](/blog/author/ahmed-sabit).