Gemma 4 E4B IT Qat Mobile Transformers — Hardware Requirements & GPU Compatibility
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Gemma 4 E4B IT Qat Mobile Transformers is a 3.4B-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 2.55 GB of VRAM — see which GPUs and Macs can run it below.
848 downloads
18 likesSpecifications
- Publisher
- Family
- Gemma 4
- Parameters
- 3.4B
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-06-02
- License
- Apache 2.0
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How Much VRAM Does Gemma 4 E4B IT Qat Mobile Transformers Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.0 GB | 15.8 GB | 1.44 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 2.2 GB | 16.0 GB | 1.65 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.2 GB | 16.1 GB | 1.69 GB | 4-bit legacy quantization |
| Q4_K_Mest. | 4.80 | 2.5 GB | 16.4 GB | 2.03 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 2.9 GB | 16.8 GB | 2.41 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 3.3 GB | 17.2 GB | 2.79 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 3.9 GB | 17.8 GB | 3.38 GB | 8-bit quantization, near-lossless |
| BF16 | 16.00 | 7.3 GB | 21.1 GB | 6.75 GB | Brain floating point 16 — preferred for training |
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 Qat Mobile Transformers?
Q4_K_M · 2.5 GBGemma 4 E4B IT Qat Mobile Transformers (Q4_K_M) requires 2.5 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 13.9 GB, bringing total usage to 16.4 GB. 50 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 IT Qat Mobile Transformers?
Q4_K_M · 2.5 GB59 devices with unified memory can run Gemma 4 E4B IT Qat Mobile Transformers, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Gemma 4 E4B IT Qat Mobile Transformers
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 Qat Mobile Transformers need?
Gemma 4 E4B IT Qat Mobile Transformers requires 2.5 GB of VRAM at Q4_K_M, or 7.3 GB at BF16. Full 131K context adds up to 13.9 GB (16.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.4B × 4.8 bits ÷ 8 = 2 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_M2.5 GBQ4_K_M + full context16.4 GB- What's the best quantization for Gemma 4 E4B IT Qat Mobile Transformers?
For Gemma 4 E4B IT Qat Mobile Transformers, Q4_K_M (2.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (2.9 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.0 GB.
VRAM requirement by quantization
Q2_K2.0 GBQ4_02.2 GBQ4_K_M ★2.5 GBQ5_K_M2.9 GBQ6_K3.3 GBBF167.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 4 E4B IT Qat Mobile Transformers on a Mac?
Gemma 4 E4B IT Qat Mobile Transformers requires at least 2.0 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 Qat Mobile Transformers locally?
Yes — Gemma 4 E4B IT Qat Mobile Transformers can run locally on consumer hardware. At Q4_K_M quantization it needs 2.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 4 E4B IT Qat Mobile Transformers?
At Q4_K_M, Gemma 4 E4B IT Qat Mobile Transformers can reach ~1726 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~257 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 ÷ 2.5 × 0.65 = ~2039 tok/s
Estimated speed at Q4_K_M (2.5 GB)
~2039 tok/s~257 tok/s~2039 tok/s~1726 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 IT Qat Mobile Transformers?
At Q4_K_M, the download is about 2.03 GB. The full-precision BF16 version is 6.75 GB. The smallest option (Q2_K) is 1.44 GB.
- Which GPUs can run Gemma 4 E4B IT Qat Mobile Transformers?
50 consumer GPUs can run Gemma 4 E4B IT Qat Mobile Transformers at Q4_K_M (2.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gemma 4 E4B IT Qat Mobile Transformers?
59 devices with unified memory can run Gemma 4 E4B IT Qat Mobile Transformers at Q4_K_M (2.5 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.