Gemma 3n E4B IT Litert Lm — Hardware Requirements & GPU Compatibility
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31.8K downloads
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- Publisher
- Family
- Gemma
- Parameters
- 4B
- Release Date
- 2025-11-25
- License
- Gemma Terms
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HuggingFace
How Much VRAM Does Gemma 3n E4B IT Litert Lm Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.9 GB | — | 1.70 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.9 GB | — | 1.75 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 2.1 GB | — | 1.95 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 2.2 GB | — | 2.00 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 2.6 GB | — | 2.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 3.1 GB | — | 2.85 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 3.6 GB | — | 3.30 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 4.4 GB | — | 4.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 3n E4B IT Litert Lm?
Q4_K_M · 2.6 GBGemma 3n E4B IT Litert Lm (Q4_K_M) requires 2.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ 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 Litert Lm?
Q4_K_M · 2.6 GB33 devices with unified memory can run Gemma 3n E4B IT Litert Lm, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Gemma 3n E4B IT Litert Lm need?
Gemma 3n E4B IT Litert Lm requires 2.6 GB of VRAM at Q4_K_M, or 4.4 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 4B × 4.8 bits ÷ 8 = 2.4 GB
KV Cache + Overhead ≈ 0.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M2.6 GB- What's the best quantization for Gemma 3n E4B IT Litert Lm?
For Gemma 3n E4B IT Litert Lm, Q4_K_M (2.6 GB) offers the best balance of quality and VRAM usage. Q4_K_L (2.7 GB) provides better quality if you have the VRAM. The smallest option is IQ3_XS at 1.8 GB.
VRAM requirement by quantization
IQ3_XS1.8 GB~73%Q3_K_M2.1 GB~83%Q4_K_S2.5 GB~88%Q4_K_M ★2.6 GB~89%Q5_K_S3.0 GB~92%Q8_04.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 3n E4B IT Litert Lm on a Mac?
Gemma 3n E4B IT Litert Lm requires at least 1.8 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 Litert Lm locally?
Yes — Gemma 3n E4B IT Litert Lm can run locally on consumer hardware. At Q4_K_M quantization it needs 2.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 3n E4B IT Litert Lm?
At Q4_K_M, Gemma 3n E4B IT Litert Lm can reach ~1104 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~248 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 ÷ 2.6 × 0.55 = ~1104 tok/s
Estimated speed at Q4_K_M (2.6 GB)
AMD Instinct MI300X~1104 tok/sNVIDIA GeForce RTX 4090~248 tok/sNVIDIA H100 SXM~825 tok/sAMD Instinct MI250X~683 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 Litert Lm?
At Q4_K_M, the download is about 2.40 GB. The full-precision Q8_0 version is 4.00 GB. The smallest option (IQ3_XS) is 1.65 GB.
- Which GPUs can run Gemma 3n E4B IT Litert Lm?
35 consumer GPUs can run Gemma 3n E4B IT Litert Lm at Q4_K_M (2.6 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 Litert Lm?
33 devices with unified memory can run Gemma 3n E4B IT Litert Lm at Q4_K_M (2.6 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.