Google·Gemma 4·Gemma4AssistantForCausalLM

Gemma 4 E4B IT Assistant — Hardware Requirements & GPU Compatibility

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Gemma 4 E4B IT Assistant is a 4B-parameter open language model from Google in the Gemma 4 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 2.70 GB of VRAM — see which GPUs and Macs can run it below.

52.2K downloads 108 likes 14.5K quant downloads131K context

Specifications

Publisher
Google
Family
Gemma 4
Parameters
4B
Architecture
Gemma4AssistantForCausalLM
Context Length
131,072 tokens
Vocabulary Size
262,144
Release Date
2026-04-23
License
Apache 2.0

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How Much VRAM Does Gemma 4 E4B IT Assistant Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.402 GB
Q3_K_Mest.3.902.3 GB
Q4_K_S4.502.5 GB
Q4_K_M4.802.7 GB
Q5_K_M5.703.1 GB
Q6_Kest.6.603.6 GB
Q8_08.004.3 GB
BF16est.16.008.3 GB

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 IT Assistant?

Q4_K_M · 2.7 GB

Gemma 4 E4B IT Assistant (Q4_K_M) requires 2.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 131K context window can add up to 0.3 GB, bringing total usage to 3.0 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Which Devices Can Run Gemma 4 E4B IT Assistant?

Q4_K_M · 2.7 GB

33 devices with unified memory can run Gemma 4 E4B IT Assistant, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.

Where to Download Gemma 4 E4B IT Assistant

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 IT Assistant need?

Gemma 4 E4B IT Assistant requires 2.7 GB of VRAM at Q4_K_M, or 8.3 GB at BF16.

VRAM = Weights + KV Cache + Overhead

Weights = 4B × 4.8 bits ÷ 8 = 2.4 GB

KV Cache + Overhead 0.3 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 0.6 GB (at full 131K context)

VRAM usage by quantization

2.7 GB
3.0 GB

Learn more about VRAM estimation →

What's the best quantization for Gemma 4 E4B IT Assistant?

For Gemma 4 E4B IT Assistant, Q4_K_M (2.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (3.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2 GB.

VRAM requirement by quantization

Q2_K
2.0 GB
Q4_K_S
2.5 GB
Q4_K_M
2.7 GB
Q5_K_M
3.1 GB
Q6_K
3.6 GB
BF16
8.3 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Gemma 4 E4B IT Assistant on a Mac?

Gemma 4 E4B IT Assistant requires at least 2 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 IT Assistant locally?

Yes — Gemma 4 E4B IT Assistant can run locally on consumer hardware. At Q4_K_M quantization it needs 2.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Gemma 4 E4B IT Assistant?

At Q4_K_M, Gemma 4 E4B IT Assistant can reach ~1080 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~243 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 MI300X5300 ÷ 2.7 × 0.55 = ~1080 tok/s

Estimated speed at Q4_K_M (2.7 GB)

~1080 tok/s
~243 tok/s
~807 tok/s
~668 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Gemma 4 E4B IT Assistant?

At Q4_K_M, the download is about 2.40 GB. The full-precision BF16 version is 8.00 GB. The smallest option (Q2_K) is 1.70 GB.

Which GPUs can run Gemma 4 E4B IT Assistant?

35 consumer GPUs can run Gemma 4 E4B IT Assistant at Q4_K_M (2.7 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 4 E4B IT Assistant?

33 devices with unified memory can run Gemma 4 E4B IT Assistant at Q4_K_M (2.7 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.