Gemma 2 2B IT — Hardware Requirements & GPU Compatibility
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Google Gemma 2 2B IT is a 2-billion parameter instruction-tuned model from Google's Gemma 2 family, the smallest variant in the Gemma 2 series. It is designed for efficient local inference on resource-constrained hardware, handling basic conversational tasks and simple instruction following at minimal compute cost. The model can run on GPUs with as little as 4GB of VRAM when quantized, and even on CPU-only setups. Released under the Gemma license.
433.7K downloads
1.3K likesSpecifications
- Publisher
- Family
- Gemma 2
- Parameters
- 2B
- Context Length
- 8,192 tokens
- Release Date
- 2024-08-27
- License
- Gemma Terms
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HuggingFace
How Much VRAM Does Gemma 2 2B IT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ3_M | 3.60 | 1.0 GB | — | 0.90 GB | Importance-weighted 3-bit, medium |
| Q3_K_L | 4.10 | 1.1 GB | — | 1.02 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 1.2 GB | — | 1.07 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 1.2 GB | — | 1.13 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 1.3 GB | — | 1.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 1.5 GB | — | 1.38 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 1.6 GB | — | 1.43 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.8 GB | — | 1.65 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 2.2 GB | — | 2.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Gemma 2 2B IT?
Q4_K_M · 1.3 GBGemma 2 2B IT (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. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Gemma 2 2B IT?
Q4_K_M · 1.3 GB33 devices with unified memory can run Gemma 2 2B IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Gemma 2 2B IT need?
Gemma 2 2B IT requires 1.3 GB of VRAM at Q4_K_M, or 2.2 GB at Q8_0.
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)
VRAM usage by quantization
Q4_K_M1.3 GB- What's the best quantization for Gemma 2 2B IT?
For Gemma 2 2B IT, Q4_K_M (1.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.5 GB) provides better quality if you have the VRAM. The smallest option is IQ3_M at 1.0 GB.
VRAM requirement by quantization
IQ3_M1.0 GB~78%IQ4_XS1.2 GB~87%Q4_K_M ★1.3 GB~89%Q5_K_S1.5 GB~92%Q5_K_M1.6 GB~92%Q8_02.2 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 2 2B IT on a Mac?
Gemma 2 2B IT requires at least 1.0 GB at IQ3_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 2 2B IT locally?
Yes — Gemma 2 2B IT can run locally on consumer hardware. At Q4_K_M quantization it needs 1.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 2 2B IT?
At Q4_K_M, Gemma 2 2B IT can reach ~2208 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 1.3 × 0.55 = ~2208 tok/s
Estimated speed at Q4_K_M (1.3 GB)
AMD Instinct MI300X~2208 tok/sNVIDIA GeForce RTX 4090~496 tok/sNVIDIA H100 SXM~1651 tok/sAMD Instinct MI250X~1365 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Gemma 2 2B IT?
At Q4_K_M, the download is about 1.20 GB. The full-precision Q8_0 version is 2.00 GB. The smallest option (IQ3_M) is 0.90 GB.