Gemma 4 E2B IT Qat Q4 0 Unquantized — Hardware Requirements & GPU Compatibility
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Gemma 4 E2B IT Qat Q4 0 Unquantized is a 5.1B-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 3.42 GB of VRAM — see which GPUs and Macs can run it below.
4.9K downloads
17 likesSpecifications
- Publisher
- Family
- Gemma 4
- Parameters
- 5.1B
- Architecture
- Gemma4ForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-04-29
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Gemma 4 E2B IT Qat Q4 0 Unquantized Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.5 GB | 6.0 GB | 2.17 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 2.8 GB | 6.3 GB | 2.49 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.9 GB | 6.4 GB | 2.55 GB | 4-bit legacy quantization |
| Q4_K_Mest. | 4.80 | 3.4 GB | 6.9 GB | 3.06 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 4.0 GB | 7.5 GB | 3.64 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 4.6 GB | 8.0 GB | 4.21 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 5.5 GB | 8.9 GB | 5.10 GB | 8-bit quantization, near-lossless |
| BF16 | 16.00 | 10.6 GB | 14.0 GB | 10.21 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 E2B IT Qat Q4 0 Unquantized?
Q4_K_M · 3.4 GBGemma 4 E2B IT Qat Q4 0 Unquantized (Q4_K_M) requires 3.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 131K context window can add up to 3.5 GB, bringing total usage to 6.9 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Gemma 4 E2B IT Qat Q4 0 Unquantized?
Q4_K_M · 3.4 GB33 devices with unified memory can run Gemma 4 E2B IT Qat Q4 0 Unquantized, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Gemma 4 E2B IT Qat Q4 0 Unquantized
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does Gemma 4 E2B IT Qat Q4 0 Unquantized need?
Gemma 4 E2B IT Qat Q4 0 Unquantized requires 3.4 GB of VRAM at Q4_K_M, or 10.6 GB at BF16. Full 131K context adds up to 3.5 GB (6.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 5.1B × 4.8 bits ÷ 8 = 3.1 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 3.8 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M3.4 GBQ4_K_M + full context6.9 GB- What's the best quantization for Gemma 4 E2B IT Qat Q4 0 Unquantized?
For Gemma 4 E2B IT Qat Q4 0 Unquantized, Q4_K_M (3.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (4.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.5 GB.
VRAM requirement by quantization
Q2_K2.5 GBQ4_02.9 GBQ4_K_M ★3.4 GBQ5_K_M4.0 GBQ6_K4.6 GBBF1610.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 4 E2B IT Qat Q4 0 Unquantized on a Mac?
Gemma 4 E2B IT Qat Q4 0 Unquantized requires at least 2.5 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 E2B IT Qat Q4 0 Unquantized locally?
Yes — Gemma 4 E2B IT Qat Q4 0 Unquantized can run locally on consumer hardware. At Q4_K_M quantization it needs 3.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 4 E2B IT Qat Q4 0 Unquantized?
At Q4_K_M, Gemma 4 E2B IT Qat Q4 0 Unquantized can reach ~852 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~192 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 ÷ 3.4 × 0.55 = ~852 tok/s
Estimated speed at Q4_K_M (3.4 GB)
~852 tok/s~192 tok/s~637 tok/s~527 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 E2B IT Qat Q4 0 Unquantized?
At Q4_K_M, the download is about 3.06 GB. The full-precision BF16 version is 10.21 GB. The smallest option (Q2_K) is 2.17 GB.
- Which GPUs can run Gemma 4 E2B IT Qat Q4 0 Unquantized?
35 consumer GPUs can run Gemma 4 E2B IT Qat Q4 0 Unquantized at Q4_K_M (3.4 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 E2B IT Qat Q4 0 Unquantized?
33 devices with unified memory can run Gemma 4 E2B IT Qat Q4 0 Unquantized at Q4_K_M (3.4 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.