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 GBIQ4_XS1.2 GBQ4_K_M ★1.3 GBQ5_K_S1.5 GBQ5_K_M1.6 GBQ8_02.2 GB★ 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)
~2208 tok/s~496 tok/s~1651 tok/s~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.
- Which GPUs can run Gemma 2 2B?
35 consumer GPUs can run Gemma 2 2B at Q4_K_M (1.3 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 2 2B?
33 devices with unified memory can run Gemma 2 2B at Q4_K_M (1.3 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.