Gemma 4 System Requirements: RAM, VRAM & Hardware Guide (2026)
Exact RAM, VRAM, and CPU requirements to run Gemma 4 locally. Covers all model sizes (E2B, E4B, 26B, 31B) for Mac, Windows, and Linux — quantized and full precision.
Before downloading Gemma 4, you need to know if your hardware can actually run it. The answer depends on which model variant you want, which quantization level you're using, and whether you're running on CPU, GPU, or Apple Silicon. This guide gives you the exact numbers.
Quick Reference: Minimum RAM to Run Gemma 4
| Model | Full Precision (BF16) | Q4_K_M Quantized | Min GPU VRAM (Q4) |
|---|---|---|---|
| Gemma 4 E2B | 9.6 GB | 3.46 GB | 4 GB |
| Gemma 4 E4B | 15 GB | 5.41 GB | 6 GB |
| Gemma 4 26B-A4B | 48 GB | ~14 GB | 16 GB |
| Gemma 4 31B | 58.3 GB | ~18 GB | 24 GB |
Important: These are inference memory requirements — the RAM or VRAM needed to load and run the model. You need additional headroom for your OS, apps, and KV cache (context buffer).
New to quantization? The Q4_K_M column is what most people use. It cuts the file size by ~75% with minimal quality loss. See the Gemma 4 setup guide for instructions.
Gemma 4 E2B — System Requirements
Best for: Most laptops, consumer GPUs, iPhone 14+, older Macs.
The E2B is a Mixture-of-Experts model with 2 billion active parameters per forward pass. Despite the name, the full model file is larger than 2B because of the MoE routing structure — but inference RAM usage is much lower than a comparable dense model.
RAM requirements (E2B):
- BF16 (full precision): 9.6 GB — needs a dedicated GPU with 10+ GB VRAM or a Mac with 16 GB unified memory
- Q8 (8-bit): ~5 GB
- Q4_K_M (4-bit, recommended): 3.46 GB — fits in 4 GB+ VRAM or 8 GB system RAM
Minimum specs to run E2B comfortably:
- CPU-only: 8 GB RAM, any modern x64 or ARM CPU (inference is slow but functional)
- Dedicated GPU: 4 GB VRAM — GTX 1650, RTX 3050, RX 6600 or newer
- Apple Silicon Mac: 8 GB unified memory (M1/M2/M3/M4 any variant)
Realistic performance benchmarks (E2B Q4_K_M):
| Hardware | Tokens/sec |
|---|---|
| Mac Mini M4 (16 GB) | 40–60 t/s |
| Mac Mini M2 (16 GB) | 25–35 t/s |
| RTX 4060 (8 GB VRAM) | 50–80 t/s |
| RTX 3060 (12 GB VRAM) | 45–65 t/s |
| CPU only (Ryzen 7, 32 GB RAM) | 8–15 t/s |
Gemma 4 E4B — System Requirements
Best for: 16 GB+ Macs, mid-range to high-end GPUs, iPhone 15 Pro / 16 Pro.
The E4B has 4 billion active parameters and measurably better reasoning than E2B. The tradeoff is a larger footprint — particularly the GGUF quantized file at 5.41 GB.
RAM requirements (E4B):
- BF16 (full precision): 15 GB — needs 16 GB+ VRAM or 24 GB+ Mac unified memory
- Q8: ~8 GB
- Q4_K_M (recommended): 5.41 GB — fits in 6 GB VRAM with care; more comfortably in 8 GB+
Minimum specs to run E4B comfortably:
- CPU-only: 16 GB RAM recommended (works in 8 GB but with heavy paging — very slow)
- Dedicated GPU: 8 GB VRAM — RTX 3060/4060 or RX 6700 XT minimum; 12 GB+ recommended
- Apple Silicon Mac: 16 GB unified memory strongly recommended; 8 GB works for Q4 but tight
Realistic performance benchmarks (E4B Q4_K_M):
| Hardware | Tokens/sec |
|---|---|
| Mac Mini M4 Pro (24 GB) | 35–50 t/s |
| Mac Mini M2 Pro (16 GB) | 20–30 t/s |
| RTX 4070 (12 GB VRAM) | 55–75 t/s |
| RTX 3060 12 GB | 40–55 t/s |
| RTX 3050 (8 GB VRAM) | 20–35 t/s |
Gemma 4 26B-A4B — System Requirements
Best for: High-end workstations, Mac Pros, dual-GPU setups, servers.
The 26B-A4B is a sparse model with 26 billion total parameters but only 4 billion active per pass — similar to the E4B in active compute, but with a larger parameter pool that improves quality significantly on reasoning tasks.
RAM requirements (26B-A4B):
- BF16: ~48 GB — requires 2× RTX 4090 or enterprise GPU / Mac Pro 96 GB
- Q4_K_M: ~14 GB — fits in a single RTX 4090 (24 GB) or Mac Studio with 32 GB
Minimum for comfortable use:
- GPU: 16 GB VRAM (RTX 4080, 3090, 4090, or RX 7900 XTX)
- Apple Silicon: Mac Studio or Mac Pro with 32 GB+ unified memory
- CPU: Not recommended — file sizes and inference are too slow for practical use
Gemma 4 31B — System Requirements
Best for: Servers, multi-GPU rigs, research use.
The 31B is Gemma 4's largest consumer-facing model — a dense architecture redesigned for 256K context. Benchmarks put it above GPT-4o on several coding and reasoning tasks.
RAM requirements (31B):
- BF16: 58.3 GB — multi-GPU or Apple Silicon Mac Pro 96 GB only
- Q4_K_M: ~18–19 GB — fits in RTX 4090 (24 GB) or Mac with 24 GB unified memory with careful context management
Minimum for practical use:
- GPU: 24 GB VRAM (RTX 4090 or A6000) — single GPU, Q4 only
- Apple Silicon: Mac Studio with 64 GB+ or Mac Pro 96 GB for full precision
- Multi-GPU: 2× RTX 3090/4090 for BF16; distributed inference via llama.cpp or vLLM
CPU vs. GPU vs. Apple Silicon: Which Is Best for Gemma 4?
Apple Silicon (M-series Mac): The best all-around option for most users. Unified memory means the GPU and CPU share the same RAM pool, so a 16 GB Mac can run E4B Q4 entirely on the GPU without splitting across CPU RAM. Apple's Metal backend in llama.cpp and LM Studio is well-optimized for Gemma 4.
NVIDIA GPU (CUDA): Fastest raw inference for a given VRAM size. If your VRAM is large enough to hold the model, you get the highest tokens/second. The downside: VRAM is expensive and fixed — you can't supplement with CPU RAM as efficiently.
CPU-only: Works for E2B Q4 if you have 8+ GB RAM, but expect 8–15 tokens/second. Fine for occasional use, testing, and edge deployments. Not suitable for E4B or larger models unless you have 32+ GB RAM and are willing to accept slow inference.
How to Check Your Hardware
macOS: Apple menu → About This Mac → Memory (unified)
Windows: Task Manager → Performance → Memory tab (RAM) + GPU tab (Dedicated GPU Memory = VRAM)
Linux: free -h for RAM; nvidia-smi for VRAM; rocm-smi for AMD
Not sure what VRAM you have? See our complete VRAM check guide.
Key Takeaways
- E2B Q4_K_M (3.46 GB): Runs on any modern device with 4 GB+ VRAM or 8 GB RAM — the accessible option
- E4B Q4_K_M (5.41 GB): Needs 6 GB+ VRAM or 16 GB Mac — noticeably better quality
- 26B-A4B Q4 (~14 GB): Needs 16 GB VRAM — for high-end GPUs and Mac Studio
- 31B Q4 (~18 GB): Single RTX 4090 or 24 GB+ Mac — best quality available locally
- Apple Silicon handles E2B and E4B extremely well on 8–16 GB — no VRAM limits, unified memory is the key advantage
- Always leave 2–4 GB headroom beyond the model file size for OS + KV cache
Ready to install? Full step-by-step setup: Gemma 4 Setup Guide | Gemma 4 on iPhone
Alex the Engineer
•Founder & AI ArchitectSenior software engineer turned AI Agency owner. I build massive, scalable AI workflows and share the exact blueprints, financial models, and code I use to generate automated revenue in 2026.
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