Gemma 3n E4B IT — Hardware Requirements & GPU Compatibility
Vision
Gemma 3n E4B IT is a 7.8B-parameter open language model from Google in the Gemma family. At Q4_K_M it needs about 5.18 GB of VRAM — see which GPUs and Macs can run it below.
19.9K downloads
917 likesSpecifications
- Publisher
- Family
- Gemma
- Parameters
- 7.8B
- License
- Gemma Terms
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HuggingFace
How Much VRAM Does Gemma 3n E4B IT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.7 GB | — | 3.34 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.8 GB | — | 3.43 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.2 GB | — | 3.83 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.3 GB | — | 3.92 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.2 GB | — | 4.71 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.2 GB | — | 5.59 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.1 GB | — | 6.48 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.6 GB | — | 7.85 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 3n E4B IT?
Q4_K_M · 5.2 GBGemma 3n E4B IT (Q4_K_M) requires 5.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Gemma 3n E4B IT?
Q4_K_M · 5.2 GB33 devices with unified memory can run Gemma 3n E4B IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Gemma 3n E4B IT need?
Gemma 3n E4B IT requires 5.2 GB of VRAM at Q4_K_M, or 8.6 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 7.8B × 4.8 bits ÷ 8 = 4.7 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M5.2 GB- What's the best quantization for Gemma 3n E4B IT?
For Gemma 3n E4B IT, Q4_K_M (5.2 GB) offers the best balance of quality and VRAM usage. Q4_K_L (5.3 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 3.6 GB.
VRAM requirement by quantization
IQ3_XS3.6 GBQ3_K_M4.2 GBQ4_K_S4.9 GBQ4_K_M ★5.2 GBQ5_K_S5.9 GBQ8_08.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 3n E4B IT on a Mac?
Gemma 3n E4B IT requires at least 3.6 GB at IQ3_XS, 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 3n E4B IT locally?
Yes — Gemma 3n E4B IT can run locally on consumer hardware. At Q4_K_M quantization it needs 5.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 3n E4B IT?
At Q4_K_M, Gemma 3n E4B IT can reach ~563 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~127 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 5.2 × 0.55 = ~563 tok/s
Estimated speed at Q4_K_M (5.2 GB)
~563 tok/s~127 tok/s~421 tok/s~348 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Gemma 3n E4B IT?
At Q4_K_M, the download is about 4.71 GB. The full-precision Q8_0 version is 7.85 GB. The smallest option (IQ3_XS) is 3.24 GB.
- Which GPUs can run Gemma 3n E4B IT?
35 consumer GPUs can run Gemma 3n E4B IT at Q4_K_M (5.2 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gemma 3n E4B IT?
33 devices with unified memory can run Gemma 3n E4B IT at Q4_K_M (5.2 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.