Gemma 4 E2B IT — Hardware Requirements & GPU Compatibility
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Gemma 4 E2B IT is a 5.1B-parameter open language model from Google in the Gemma family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 3.43 GB of VRAM — see which GPUs and Macs can run it below.
2.8M downloads
711 likesSpecifications
- Publisher
- Family
- Gemma
- Parameters
- 5.1B
- Architecture
- Gemma4ForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,144
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Gemma 4 E2B IT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.5 GB | 6 GB | 2.18 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.6 GB | 6.1 GB | 2.24 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 2.9 GB | 6.3 GB | 2.50 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.9 GB | 6.4 GB | 2.56 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 3.4 GB | 6.9 GB | 3.07 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 4.0 GB | 7.5 GB | 3.65 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 4.6 GB | 8.1 GB | 4.23 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 5.5 GB | 8.9 GB | 5.12 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 4 E2B IT?
Q4_K_M · 3.4 GBGemma 4 E2B IT (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?
Q4_K_M · 3.4 GB33 devices with unified memory can run Gemma 4 E2B IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (3)
Frequently Asked Questions
- How much VRAM does Gemma 4 E2B IT need?
Gemma 4 E2B IT requires 3.4 GB of VRAM at Q4_K_M, or 5.5 GB at Q8_0. 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?
For Gemma 4 E2B IT, Q4_K_M (3.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (3.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 2.1 GB.
VRAM requirement by quantization
IQ2_M2.1 GBQ3_K_M2.9 GBQ4_13.2 GBQ4_K_M ★3.4 GBQ5_K_S3.9 GBQ8_05.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 4 E2B IT on a Mac?
Gemma 4 E2B IT requires at least 2.1 GB at IQ2_M, 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 locally?
Yes — Gemma 4 E2B IT 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?
At Q4_K_M, Gemma 4 E2B IT can reach ~850 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~191 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 = ~850 tok/s
Estimated speed at Q4_K_M (3.4 GB)
~850 tok/s~191 tok/s~635 tok/s~525 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?
At Q4_K_M, the download is about 3.07 GB. The full-precision Q8_0 version is 5.12 GB. The smallest option (IQ2_M) is 1.73 GB.
- Which GPUs can run Gemma 4 E2B IT?
35 consumer GPUs can run Gemma 4 E2B IT 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?
33 devices with unified memory can run Gemma 4 E2B IT 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.