Gemma 4 GPU & VRAM Requirements 2026 — 26B MoE Fits 15 GB
Gemma 4 VRAM: E2B ~3GB, E4B ~5GB, 26B MoE ~15GB, 31B dense ~18GB at Q4. RTX 4090/M4 Pro fit the 26B. Gemma 4 31B vs Qwen 3.6 27B — GPU picks included.
Google released Gemma 4 on April 2, 2026 — the most capable open model family from Google to date. Built on the same technology as Gemini 3, Gemma 4 introduces four variants covering everything from edge devices to frontier-class performance, all under the Apache 2.0 license.
The headline numbers are remarkable: the 26B MoE variant scores 89% on AIME 2026 and 82% on GPQA Diamond while fitting in just 15GB at Q4 quantization. The 31B dense model hits a Codeforces ELO of 2150. And even the smallest E2B model handles text, images, audio, and video natively.
Gemma 4 Model Lineup
| Model | Type | Total Params | Active/Effective | Context | Modalities |
|---|---|---|---|---|---|
| Gemma 4 E2B | Dense (PLE) | 5.1B | 2.3B effective | 128K | Text, Image, Audio, Video |
| Gemma 4 E4B | Dense (PLE) | 8B | 4.5B effective | 128K | Text, Image, Audio, Video |
| Gemma 4 26B-A4B | MoE | 25.2B | 3.8B active | 256K | Text, Image |
| Gemma 4 31B | Dense | 30.7B | 30.7B | 256K | Text, Image |
PLE = Per-Layer Embeddings: each decoder layer gets its own embedding per token, making the model more parameter-efficient. "Effective" parameters reflect the compute actually used per token.
MoE = Mixture of Experts: the 26B model has 128 expert sub-networks and activates only 8 per token, giving high quality at low compute cost.
VRAM Requirements
| Variant | Q4_K_M | Q5_K_M | Q6_K | Q8_0 | BF16 |
|---|---|---|---|---|---|
| Gemma 4 E2B | ~3.0 GB | ~3.6 GB | ~4.2 GB | ~5.4 GB | ~10.2 GB |
| Gemma 4 E4B | ~4.6 GB | ~5.6 GB | ~6.6 GB | ~8.5 GB | ~16.0 GB |
| Gemma 4 26B-A4B (MoE) | ~14.8 GB | ~18.2 GB | ~21.4 GB | ~28.0 GB | ~50.4 GB |
| Gemma 4 31B | ~18.3 GB | ~22.5 GB | ~26.4 GB | ~34.5 GB | ~61.4 GB |
Add ~1-2 GB for KV cache and runtime overhead at default context lengths.
Hardware Recommendations
Gemma 4 E2B — Runs Everywhere
The smallest Gemma 4 fits on literally any modern GPU or Mac. At 3GB Q4, it leaves room for long contexts even on 8GB devices.
Recommended hardware:
- Any GPU with 4GB+ VRAM
- Mac M-series with 8GB+ unified memory
- CPU-only inference is viable (llama.cpp)
ollama run gemma4:e2b
Gemma 4 E4B — Best On-Device Model
The default Gemma 4 on Ollama. At ~4.6GB Q4, it fits perfectly on 8GB GPUs with room for context. Strong multimodal capabilities including audio and video understanding.
Recommended hardware:
- RTX 4060 8GB — runs at Q4 with 3.4GB headroom
- RTX 4070 12GB — runs at Q8 comfortably
- Any Mac with 16GB+ unified memory
ollama run gemma4:e4b
Check compatibility: Gemma 4 E4B on RTX 4060 | Gemma 4 E4B on M4 Pro
Gemma 4 26B-A4B — The MoE Sweet Spot
This is the model that changes the game. 25.2B total parameters but only 3.8B active per token means frontier-class quality at the speed and VRAM cost of a small model. At ~15GB Q4, it fits on a 24GB GPU.
Why this is special:
- 89% AIME 2026 (math competition benchmark)
- 82% GPQA Diamond (graduate-level science)
- 77% LiveCodeBench (code generation)
- Inference speed comparable to a 4B dense model
- All in ~15GB VRAM at Q4
Recommended hardware:
- RTX 4090 24GB — perfect fit at Q4, fast inference
- RTX 5090 32GB — comfortable at Q5+
- Mac M4 Pro 24GB — fits at Q4
- Mac M4 Max 36GB — runs at Q5 with headroom
ollama run gemma4:26b
Check compatibility: Gemma 4 26B on RTX 4090 | Gemma 4 26B on M4 Max
Gemma 4 31B — Maximum Dense Quality
The largest Gemma 4 delivers the highest absolute quality in the family. At ~18GB Q4, it fits on a 24GB GPU but with limited context headroom.
Recommended hardware:
- RTX 4090 24GB — fits at Q4, tight
- RTX 5090 32GB — comfortable at Q4, room for context
- Mac M4 Max 64GB — runs at Q6+ with full context
ollama run gemma4:31b
Gemma 4 vs Other Top Open Models (April 2026)
| Model | Params | VRAM (Q4) | MMLU Pro | AIME 2026 | LiveCodeBench | License |
|---|---|---|---|---|---|---|
| Gemma 4 26B-A4B | 25.2B (3.8B active) | ~15 GB | 82.6% | 89% | 77% | Apache 2.0 |
| Gemma 4 31B | 30.7B | ~18 GB | 85.2% | 89% | 80% | Apache 2.0 |
| Qwen 3.5 35B-A3B | 35B (3B active) | ~20 GB | — | — | — | Qwen |
| Qwen 3 32B | 32B | ~19 GB | — | — | — | Apache 2.0 |
| DeepSeek R1 32B | 32B | ~18 GB | — | — | — | MIT |
| Llama 4 Scout | 109B (17B active) | ~61 GB | — | — | — | Llama 4 |
The Gemma 4 26B MoE is the clear winner for consumer hardware: it matches or beats larger models while needing only 15GB VRAM. The Apache 2.0 license removes all usage restrictions.
Key Features
Multimodal Native
All Gemma 4 models understand images. The E2B and E4B also understand audio and video — a first for open models at this size. No separate vision adapter needed.
256K Context Window (26B and 31B)
The larger models support 256K context tokens with hybrid attention (sliding window + full attention). This is enough for processing entire codebases or long documents.
Agentic Capabilities
All variants support native function calling and tool use, making them suitable for agent workflows where the model needs to interact with external tools and APIs.
Apache 2.0 License
Unlike Gemma 3 (which had a restrictive license), Gemma 4 is fully open under Apache 2.0. Use it commercially, modify it, distribute it — no restrictions.
Getting Started
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# Run the default model (E4B)
ollama run gemma4
# Run the MoE model (best quality/size ratio)
ollama run gemma4:26b
# Run the flagship dense model
ollama run gemma4:31b
Gemma 4 vs Qwen 3.6 27B
Gemma 4 31B and Qwen 3.6 27B are the two most-compared dense models in the 15–18 GB VRAM tier in 2026. At Q4, Gemma 4 31B needs ~18.3 GB versus Qwen 3.6 27B's ~16.8 GB — both fit on an RTX 4090 or Mac M4 Pro 24GB. Qwen 3.6 27B wins on coding (SWE-bench 77.2% vs 43.2%) and math (AIME 94.1% vs 52.1%); Gemma 4 27B/31B wins on EU-language quality and safety alignment.
For the full benchmark head-to-head, see Qwen 3.6 27B vs Gemma 4 27B — Dense Head-to-Head.