Gemma 4 Complete Requirements Reference — VRAM, RAM & GPU Quick-Lookup Tables [E2B/E4B/26B/31B All Variants]

Gemma 4 minimum: 5GB RAM (E2B Q4). Recommended: 24GB VRAM (31B Dense Q4). All variants at a glance:

Note: The 26B MoE activates only ~4B parameters per token, making its VRAM requirement far lower than its total parameter count suggests.

◎=Excellent ○=Works △=Limited ✗=Not supported

Apple Silicon unified memory acts as both system RAM and VRAM, making M2/M3 Max (32GB) or higher ideal for running 31B Dense Q4.

Q4_K_M is the best balance: only 7% quality loss vs FP16, with 68% VRAM reduction.

CPU-only inference is 5-10x slower than GPU. E2B or E4B Q4 are the only practical CPU-only options.

The RTX 4090 (24GB) is the minimum consumer GPU that comfortably runs 31B Dense at Q4_K_M. Extending context beyond 128K adds ~4GB VRAM overhead.

Gemma 4 E2B is designed for edge and embedded use cases. It runs on a Raspberry Pi 5 (8GB model) via llama.cpp, making it the most accessible model in the Gemma 4 family.

E4B runs comfortably on any M2/M3 MacBook Air (16GB unified memory) and RTX 3060 cards. Ideal for users wanting a balance of quality and low power consumption.

The MoE architecture means 26B delivers near-31B Dense quality at a fraction of the VRAM cost. The RTX 4080 (16GB) is the perfect match for this model.

For 256K context on 31B Dense Q4, you need approximately 28GB VRAM, which exceeds the RTX 4090's 24GB. Consider a 32GB+ GPU or use 128K context or less on consumer hardware.

Both llama.cpp and vLLM support tensor parallelism. PCIe multi-GPU configurations are 15-30% slower than NVLink due to bandwidth constraints.

Estimated at $0.18/kWh. 24/7 inference workload assumed.

Ollama is the easiest entry point, automatically selecting the right quantization for your VRAM. For production, vLLM with its OpenAI-compatible API server is the standard choice.

Quantization selection guide: If VRAM allows, use Q5_K_M or Q6_K. If tight, use Q4_K_M. As a last resort, Q3_K_M. Avoid Q2_K due to significant quality degradation.

Q1. What are the minimum requirements to run Gemma 4? A. 5GB RAM for E2B Q4 CPU-only inference. This is the absolute minimum. Q2. Can I run Gemma 4 31B on an RTX 4090? A. Yes. Q4_K_M quantization uses 20-24GB VRAM, which fits within the RTX 4090's 24GB. Keep context under 128K for best results. Q3. What models work on an RTX 3060 (12GB)? A. E2B and E4B work excellently. 26B MoE is possible at Q4. 31B Dense is not supported on 12GB. Q4. Can I run Gemma 4 with only 8GB RAM? A. Only E2B Q4 is recommended. E4B may technically load but will be very slow on CPU-only. Q5. Does Gemma 4 run on a MacBook? A. Yes. M1/M2/M3 Pro (16GB) handles up to 26B MoE Q4. M2/M3 Max (32GB+) runs 31B Dense Q4 comfortably. Q6. Can Gemma 4 run without a GPU (CPU only)? A. Yes, but 5-10x slower than GPU. E2B Q4 or E4B Q4 are the only practical CPU-only options. Q7. Which quantization level gives the best quality-to-VRAM ratio? A. Q4_K_M. It reduces VRAM by 68% vs FP16 with only a 7% quality drop. Q8. Does Gemma 4 support 1M context? A. No. Maximum supported context is 256K tokens. Using 256K requires approximately 8GB additional VRAM on top of the base model requirement.

From hardware selection and environment setup to API integration and cost optimization, Oflight's engineers provide end-to-end support for on-premise Gemma 4 deployments. We also help teams choose between local GPU, cloud GPU, and hybrid setups based on your workload and budget. Learn more at AI Consulting Services.