Gemma 3n E2B IT Litert Lm — Hardware Requirements & GPU Compatibility
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Gemma 3n E2B IT Litert Lm is a 2B-parameter open language model from Google in the Gemma 3 family. At Q4_K_M it needs about 1.32 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~496 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~54 tok/s)
Gemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gemma 3n E2B IT Litert Lm
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve google/gemma-3n-E2B-it-litert-lm --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (8 GB headroom) · Q4_K_M
- Generation speed
- ~177 tok/s
- generation speed
- Cost per 1M output tokens
- $0.13
- per 1M output tokens
How Much VRAM Does Gemma 3n E2B IT Litert Lm Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.9 GB | 4.7 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~697 tok/s | |
| 1.1 GB | 4.8 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~612 tok/s | |
| 1.3 GB | 5.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~496 tok/s | |
| 1.6 GB | 5.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~417 tok/s | |
| 1.8 GB | 5.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~360 tok/s | |
| 2.2 GB | 5.9 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~298 tok/s | |
| 4.4 GB | 8.1 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~149 tok/s |
Selected Q4_K_M
4-bit medium quantization3.1GBat 16K context
- Weights
- 0.8 GB
- KV cache
- 2.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.9 GB free · ~496 tok/s
≥50 tok/s needs ≥101 GB/s · RTX 5090 ~882, RTX 3090 Ti ~496
Which Hardware Can Run Gemma 3n E2B IT Litert Lm?
Q4_K_M · 1.3 GBGemma 3n E2B IT Litert Lm (Q4_K_M) requires 1.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Gemma 3n E2B IT Litert Lm, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.9 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~374Simulate output on RTX 3080RTX 2080 Ti~303Simulate output on RTX 2080 TiRTX 3070 Ti~300Simulate output on RTX 3070 TiM3*8 GB~54Simulate output on iMac 24" M3 (8 GB)Orin Nano~50Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~36Simulate output on iPhone 1720 GB
fits · 16.9 GB free · up to 32K ctx
1 GPU
RX 7900 XT~364Simulate output on RX 7900 XT32–40 GB
fits · 28.9 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~882Simulate output on RTX 5090M5 Max36 GB~244Simulate output on Mac Studio M5 Max (36 GB)M532 GB~82Simulate output on MacBook Air 13" M5 (32 GB)Orin~101Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~67Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Gemma 3n E2B IT Litert Lm
Gemma 3 family · 20 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3 VL 2B InstructAlibaba | — | 3.5 GB | ~362 | same size |
| Moondream2vikhyatk | — | 3 GB | ~516 | same size |
| OneRec 1.7BOpenOneRec | — | 3.5 GB | ~362 | same size |
| Llm Jp 3.1 1.8B Instruct4llm-jp | — | 2.2 GB | ~360 | same size |
| Helium 1 2Bkyutai | — | 2 GB | ~374 | same size |
| H2ovl Mississippi 2Bh2oai | — | 6.5 GB | ~138 | same size |
Specifications
- Publisher
- Family
- Gemma 3
- Parameters
- 2B
- Release Date
- 2025-06-06
- License
- Gemma Terms
Frequently Asked Questions
How much VRAM does Gemma 3n E2B IT Litert Lm need?
Gemma 3n E2B IT Litert Lm requires 1.3 GB of VRAM at Q4_K_M, or 4.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 2B × 4.8 bits ÷ 8 = 1.2 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
What's the best quantization for Gemma 3n E2B IT Litert Lm?
For Gemma 3n E2B IT Litert Lm, Q4_K_M (1.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.9 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Gemma 3n E2B IT Litert Lm on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Gemma 3n E2B IT Litert Lm. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gemma 3n E2B IT Litert Lm locally?
Yes — Gemma 3n E2B IT Litert Lm can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 3n E2B IT Litert Lm?
At Q4_K_M, Gemma 3n E2B IT Litert Lm can reach ~3636 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~496 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 ÷ 1.32 × 0.65 = ~3939 tok/s
Estimated speed at Q4_K_M (1.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Gemma 3n E2B IT Litert Lm?
At Q4_K_M, the download is about 1.20 GB. The full-precision BF16 version is 4.00 GB. The smallest option (Q2_K) is 0.85 GB.
Which GPUs can run Gemma 3n E2B IT Litert Lm?
52 consumer GPUs can run Gemma 3n E2B IT Litert Lm at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Gemma 3n E2B IT Litert Lm?
163 devices with unified memory can run Gemma 3n E2B IT Litert Lm at Q4_K_M (1.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.