Gemma 4 E4B — Hardware Requirements & GPU Compatibility
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Gemma 4 E4B is Google DeepMind's second-smallest model in the Gemma 4 family, a dense architecture with roughly 8 billion total parameters, of which Google describes about 4.5 billion as its effective footprint at inference. This is the pretrained base checkpoint rather than an instruction-tuned model, meant for fine-tuning rather than direct chat use. Like the rest of the family it is multimodal — text, image, and natively audio at this size — targeting efficient on-device deployment on laptops and higher-end phones. It runs comfortably on a single consumer GPU, or on-device once quantized. It supports a 131,072 token context window. It carries Google's Gemma 4 license terms, published as Apache 2.0 on Hugging Face with additional Gemma-specific usage terms linked from the card. Published in March 2026, it sits between the E2B on-device model and the larger 12B, 26B-A4B, and 31B tiers.
642.8K downloads
432 likesSpecifications
- Publisher
- Family
- Gemma 4
- Parameters
- 8.0B
- Architecture
- Gemma4ForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-03-02
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Gemma 4 E4B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.9 GB | 17.8 GB | 3.40 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.0 GB | 17.9 GB | 3.50 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.4 GB | 18.3 GB | 3.90 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 5.3 GB | 19.2 GB | 4.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.2 GB | 20.1 GB | 5.70 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.1 GB | 21.0 GB | 6.60 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.5 GB | 22.4 GB | 8.00 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Gemma 4 E4B?
Q4_K_M · 5.3 GBGemma 4 E4B (Q4_K_M) requires 5.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 131K context window can add up to 13.9 GB, bringing total usage to 19.2 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Gemma 4 E4B?
Q4_K_M · 5.3 GB58 devices with unified memory can run Gemma 4 E4B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Apple iPhone 17 Pro.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Gemma 4 E4B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Gemma 4 E4B need?
Gemma 4 E4B requires 5.3 GB of VRAM at Q4_K_M, or 16.5 GB at BF16. Full 131K context adds up to 13.9 GB (19.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.0B × 4.8 bits ÷ 8 = 4.8 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 14.4 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M5.3 GBQ4_K_M + full context19.2 GB- What's the best quantization for Gemma 4 E4B?
For Gemma 4 E4B, Q4_K_M (5.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.9 GB.
VRAM requirement by quantization
Q2_K3.9 GBQ3_K_L4.6 GBQ4_K_M ★5.3 GBQ5_K_S6.0 GBQ5_K_M6.2 GBBF1616.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 4 E4B on a Mac?
Gemma 4 E4B requires at least 3.9 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Gemma 4 E4B locally?
Yes — Gemma 4 E4B can run locally on consumer hardware. At Q4_K_M quantization it needs 5.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 4 E4B?
At Q4_K_M, Gemma 4 E4B can reach ~902 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~123 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 5.3 × 0.65 = ~977 tok/s
Estimated speed at Q4_K_M (5.3 GB)
~977 tok/s~123 tok/s~977 tok/s~902 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Gemma 4 E4B?
At Q4_K_M, the download is about 4.80 GB. The full-precision BF16 version is 15.99 GB. The smallest option (Q2_K) is 3.40 GB.
- Which GPUs can run Gemma 4 E4B?
52 consumer GPUs can run Gemma 4 E4B at Q4_K_M (5.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gemma 4 E4B?
59 devices with unified memory can run Gemma 4 E4B at Q4_K_M (5.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.