Gemma 2 2B — Hardware Requirements & GPU Compatibility
Chat
Google Gemma 2 2B is a 2-billion parameter base (pretrained) model from Google's Gemma 2 family. As a base model, it is not instruction-tuned and is intended for fine-tuning, research, and custom downstream applications. Its compact size makes it suitable for experimentation, rapid prototyping, and domain-specific fine-tuning on consumer hardware with minimal VRAM. Released under the Gemma license.
276.5K downloads
629 likesSpecifications
- Publisher
- Family
- Gemma 2
- Parameters
- 2B
- Release Date
- 2024-08-07
- License
- Gemma Terms
Get Started
HuggingFace
How Much VRAM Does Gemma 2 2B 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?
Q4_K_M · 1.3 GBGemma 2 2B (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?
Q4_K_M · 1.3 GB33 devices with unified memory can run Gemma 2 2B, 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 need?
Gemma 2 2B 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?
For Gemma 2 2B, 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 on a Mac?
Gemma 2 2B 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 locally?
Yes — Gemma 2 2B 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?
At Q4_K_M, Gemma 2 2B 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?
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.