Gemma 4 System Requirements — 5–62GB VRAM, RTX 3060 to H100 by Variant (E2B/E4B/26B/31B) [2026 Guide]

Bottom line first. The minimum hardware you need to run Gemma 4, in four rows. Detailed quantization tables, speed benchmarks, and OS-by-OS setup are covered below.

Key points: Ollama defaults to Q4_K_M (4-bit quantization), reducing memory usage by ~55–60%. A GPU is not required — CPU-only operation works but runs 5–10× slower. Apple Silicon's unified memory makes CPU+GPU coexistence smoother.

Running Gemma 4 in a local environment requires appropriate RAM or VRAM depending on the model's parameter count and quantization level. Requirements range from a minimum of 5GB (E2B/E4B quantized) to a maximum of 80GB (31B FP16). Quantization is a technique that reduces memory usage while maintaining model accuracy. Ollama uses Q4_K_M (4-bit quantization) by default, reducing memory usage by approximately 55-60%. Using a GPU significantly improves inference speed but is not mandatory. CPU-only execution is possible but 5-10 times slower. This guide comprehensively covers detailed requirements for each variant, performance by GPU, and budget-based recommended configurations.

E2B and E4B are efficiency-focused lightweight models that run on typical laptops. Gemma 4 E2B (2B parameters)

Gemma 4 E4B (4B parameters)

E2B/E4B run comfortably with 10GB+ VRAM GPU. Without GPU, CPU execution is possible but speed drops to around 5-8 tokens/sec.

26B MoE (Mixture of Experts) uses an efficient design where only 4 billion parameters are active during inference out of 26 billion total. Naming variants: this model appears in different sources as Gemma 4 26B, Gemma 4 26B MoE, Gemma 4 26B-A4B, 26B (4B active), or 26B/A4B — all refer to the same model. "A4B" stands for Active 4B parameters, a common MoE notation. We'll use "26B MoE" throughout this article. Gemma 4 26B MoE / 26B-A4B (26B parameters, 4B active)

26B MoE practically requires 16GB+ VRAM. RTX 4090 or RTX A5000 with 24GB VRAM is ideal. It also works on Apple Silicon M3 Max 64GB but uses unified memory, potentially affecting other applications. Thanks to MoE architecture, it's faster and more memory-efficient than 31B Dense.

31B Dense uses all parameters for maximum performance, targeting enterprise and research use cases. Gemma 4 31B Dense (31B parameters)

31B Dense requires 24GB+ VRAM. Q4 quantization barely fits in 24GB, but 32GB+ is recommended for practical use. For FP16 execution, NVIDIA H100 80GB or A100 80GB is necessary, making cloud environments (AWS p4d, Azure ND series) realistic options. It also works on Apple Silicon M3 Ultra 192GB, but NVIDIA offers better cost-performance.

Quantization is a technique that reduces memory usage by representing model weights with lower bit precision. Quantization Level Comparison

Ollama uses Q4_K_M by default. Here "K" means Kalman quantization (more accurate quantization method), and "M" means medium (moderate precision). Q4_K_M is sufficient for business use, but Q8_0 or higher is recommended for fields requiring high precision like medical or legal domains. Quantization is automatically handled within Ollama, requiring no manual user configuration.

Apple Silicon's design with CPU and GPU sharing unified memory makes it suitable for running Gemma 4. Recommended Models by Apple Silicon

The biggest advantage of Apple Silicon is power efficiency. While RTX 4090 consumes 450W, M3 Max uses around 90W maximum. The electricity cost difference becomes significant for long inference tasks. However, absolute speed is inferior compared to NVIDIA GPUs.

NVIDIA GPUs deliver the best performance for running Gemma 4 thanks to advanced CUDA optimization. NVIDIA GPU Performance Comparison

*Uses some system RAM when VRAM insufficient (speed degradation occurs) For cost-performance, RTX 4060 Ti 16GB or RTX 4090 are optimal. RTX 4070 or higher for comfortable E4B use, RTX 6000 Ada or higher for serious 31B use.

Gemma 4 can run on CPU-only without GPU, but speed significantly decreases. CPU Performance (E4B Q4)

For CPU execution, AVX-512 instruction set support significantly affects speed. AMD Ryzen 7000 series onwards and Intel Xeon (3rd gen onwards) support it. For practical speed, minimum 8 cores, 16 cores recommended. 26B+ models are impractical on CPU-only (1-3 tokens/sec).

Four recommended configurations based on budget and use case. Entry Configuration ($800-1,200) - CPU: AMD Ryzen 5 7600 / Intel Core i5-13400 - RAM: 16GB DDR5 - GPU: RTX 3060 12GB / Integrated GPU (M2 Mac mini) - Recommended Model: E2B, E4B (Q4) - Use Case: Personal learning, lightweight automation Mid-Range Configuration ($2,000-3,000) - CPU: AMD Ryzen 7 7700X / Intel Core i7-13700 - RAM: 32GB DDR5 - GPU: RTX 4070 Ti 12GB / RTX 4060 Ti 16GB - Recommended Model: E4B (Q8), 26B MoE (Q4) - Use Case: SMB AI adoption, development environment High-End Configuration ($4,000-6,000) - CPU: AMD Ryzen 9 7950X / Intel Core i9-13900K - RAM: 64GB DDR5 - GPU: RTX 4090 24GB / M3 Max 48GB - Recommended Model: 26B MoE (Q8), 31B (Q4) - Use Case: Enterprise AI, R&D Enterprise Configuration ($12,000+ / Cloud Recommended) - CPU: AMD EPYC 7643 / Intel Xeon Gold 6348 - RAM: 256GB ECC - GPU: RTX 6000 Ada 48GB × 2 / H100 80GB (Cloud) - Recommended Model: 31B (Q8, FP16) - Use Case: Large-scale AI deployment, multi-user environment Cloud usage (AWS EC2 p4d, Azure NDv5) is also a strong option. It reduces upfront investment with pay-as-you-go pricing, making it cost-efficient when monthly inference volume is low.

If all you want to know is "the absolute floor I can run this on," here are the per-model minimums — the configurations that just barely boot and reach a usable ~5 tok/s.

Laptop minimum: an M1/M2 MacBook Air 8GB or a gaming laptop (RTX 3060 6GB+) for E2B / E4B (Q4). CPU-only minimum: 8-core CPU with 16GB RAM runs E4B (Q4); for snappy daily use, 16-core + 32GB+ with AVX-512 support. Mac mini minimum: M2 Mac mini 16GB runs E4B (Q4) comfortably; M4 Pro 32GB+ extends to 26B MoE. "Minimum" and "comfortable" are different. The minimum is what just boots; for daily use, plan for 1.5–2× the listed VRAM/RAM.

If you're asking "which one should I actually pick for my workload?" — here's a use-case lookup.

Pick a use case, start with the row's recommended config, then escalate quantization (Q4→Q8) or model size (E4B→26B→31B) only as needed. That order minimizes wasted spend.

Three resource types are involved: VRAM, system RAM, and storage. - VRAM (GPU memory): Where the model weights live. Q4-quantized minimums: 5GB for E2B/E4B, 16GB for 26B MoE, 24GB for 31B Dense. With CPU-only inference, system RAM substitutes for VRAM. - System RAM: When using a GPU, plan for ~+4GB buffer. CPU-only inference needs RAM equal to the VRAM column above. - Storage: Model file size on disk. Q4 weights: ~3–4GB (E2B/E4B), ~16GB (26B MoE), ~22GB (31B Dense). FP16 weights for 31B exceed 60GB. SSD strongly recommended — first-load on HDD is painful. Rule of thumb: required memory (GB) = parameters (B) × bytes per weight (Q4=0.5, Q8=1, FP16=2) × 1.2 (overhead). Example: 31B × Q4 = 31 × 0.5 × 1.2 ≈ 18.6GB → 24GB VRAM with safety margin. When running multiple models concurrently, sum each model's memory requirement and add 4GB buffer. Example: E4B (5GB) + E2B (3GB) → 12GB+ VRAM target.

Practical guidance for picking your OS, assuming you'll use Ollama (the simplest official path). macOS (Apple Silicon recommended) - Unified memory means E2B/E4B work on 8GB Macs. 26B MoE fits comfortably on 32GB Pro+; 31B is a 64GB Max+ target. - Install: `brew install ollama`, then `ollama serve` and `ollama run gemma4:4b` in another terminal. - Wins: ~1/5 the power draw of an NVIDIA GPU, silent fans, runs anywhere. Windows (NVIDIA GPU recommended) - Use the official .exe installer; CUDA setup is automatic. RTX 3060 12GB+ handles E4B–26B MoE comfortably. - WSL2 + Linux Ollama works too, but the native Windows build is enough for most users. - Caveat: Laptops with only integrated graphics fall back to CPU and feel slow. An eGPU (external GPU) helps. Linux (max flexibility, max performance) - Ubuntu 22.04+ / Debian 12 are the safe picks. Install with `curl -fsSL https://ollama.com/install.sh | sh`. - Multi-GPU setups (e.g., RTX 4090×2) excel for shared internal services. Pairs naturally with Docker / Kubernetes for in-house servers. - Use NVLink-capable GPUs (A6000, H100) when running multiple cards. Quick decision: personal/mobile development → Mac. Cost-effective local development → Windows + RTX 4070 Ti class. Internal multi-user server → Linux + multi-GPU.

Solutions when memory shortage occurs during Gemma 4 execution. Solutions by Symptom 1. OOM Error (Out of Memory) Occurs - Solution: Try lighter quantization level (Q8→Q5→Q4) - Command Example: `ollama run gemma4:4b-q4` to explicitly specify Q4 2. Launches Successfully But Very Slow - Cause: Insufficient VRAM, swapping to system RAM - Solution: Downgrade to smaller model (31B→26B→E4B) or close other apps 3. "Memory Pressure" Warning on macOS - Solution: Don't allocate more than 70% of unified memory to Gemma. Example: Use models under 10GB on 16GB Mac 4. Page File Warning on Windows - Solution: Manually increase page file size (System Properties→Advanced→Performance→Virtual Memory) 5. Want to Run Multiple Models Simultaneously - Required Memory: Sum of each model's memory requirements + 4GB - Example: E4B (5GB) + E2B (5GB) = Minimum 14GB needed To avoid memory shortage, it's recommended to prepare hardware with 1.5x the model memory requirement.

Gemma 4 execution speed differs between batch processing (generating full text at once) and streaming (sequential generation). Performance by Execution Mode (E4B Q4, RTX 4090)

Ollama's default is streaming. For chatbots and real-time applications, users prioritize time-to-first-word (latency), making streaming suitable. For batch summarization of hundreds of documents, parallel batch processing provides higher throughput.

Power consumption is an important cost factor for local AI execution. Power Consumption by Hardware (E4B Q4 Continuous Execution)

*Calculated at $0.30/kWh electricity rate Comparison with Cloud (31B Q4, 1M tokens/month) - Local (RTX 4090): $1,600 upfront + ~$40/month electricity - AWS EC2 p4d.xlarge: $0 upfront + ~$500/month usage (on-demand) - OpenAI GPT-4: ~$150/month API fees ($30 per million tokens) Local execution has cost advantages for 1M+ tokens/month processing. However, maintenance and management costs must also be considered.

Using multiple GPUs enables running larger models and acceleration. Multi-GPU Configuration Examples

Ollama automatically detects multi-GPU and load balances. However, when GPUs have different VRAM (e.g., RTX 4090 24GB + RTX 3060 12GB), distribution matches the smaller one, reducing efficiency. In multi-GPU configurations, matching GPU models is crucial. Also, with NVLink-connected GPUs, VRAM communication is accelerated, providing an additional 10-15% performance boost.

Q1: Can Gemma 4 run on an 8GB RAM laptop? A: E2B (Q4) can run. However, using other applications (like browsers) simultaneously may cause instability, so 16GB+ is recommended. Q2: Is it practical without GPU? A: For E2B/E4B, practical level (5-8 tokens/sec) is achievable with 8+ core CPU. However, GPU provides 5-10x speed boost, so GPU is recommended for frequent use. Q3: How much accuracy loss from quantization? A: Q4_K_M typically causes 3-6% accuracy loss. For business document summarization or translation, the perceptual difference is small, but Q8+ is recommended for fields requiring high precision like mathematical reasoning or medical diagnosis. Q4: M1 Mac or RTX 4070, which is better? A: RTX 4070 for speed priority (1.5-2x faster), M1 Mac for power efficiency and quietness. For long-running operations, M1's power efficiency (1/5 consumption) is a major advantage. Q5: Which is faster, 26B MoE or 31B Dense? A: At the same quantization level, 26B MoE is 1.3-1.5x faster. MoE uses only 4B parameters during inference, resulting in less memory access. Performance-wise, 31B Dense slightly outperforms. Q6: What's the difference between VRAM and system RAM? A: VRAM is GPU-dedicated high-speed memory, system RAM is general-purpose memory. For LLM execution, VRAM is 5-10x faster, but system RAM has lower cost per GB. Apple Silicon uses unified memory serving both. Q7: Which is more cost-efficient, cloud or local? A: Cloud for under 1M tokens/month, local for over. However, local is recommended when prioritizing data privacy or communication stability.

Oflight Inc. provides comprehensive support from optimal hardware selection for Gemma 4 to implementation. Hardware Consulting Services 1. Requirements Interview: Propose optimal configuration based on data volume, response speed requirements, and budget 2. Performance Benchmarking: Test execution with your actual data to verify real performance in advance 3. Procurement Support: Introduce optimal GPU suppliers, compare quotes 4. Environment Setup: Optimal configuration of Ollama, CUDA, drivers 5. Performance Tuning: Optimize quantization level, batch size, etc. Implementation Results - Manufacturing Company A: Implemented 31B Dense with RTX 4090×2, built quality control AI system (ROI period 8 months) - Financial Institution B: Built multi-user AI analysis environment with RTX A5000×4 - Retail Company C: Local AI deployment per store with M3 Mac mini×10 (90% cloud cost reduction) Hardware selection is a critical factor determining AI implementation success. We propose designs that minimize upfront investment while securing future scalability. We offer free consultations, so please feel free to contact us. Learn more about AI Consulting Services