Gemma 2 2B Pytorch — Hardware Requirements & GPU Compatibility
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Gemma 2 2B Pytorch is a 2B-parameter open language model from Google in the Gemma 2 family. At BF16 it needs about 4.40 GB of VRAM — see which GPUs and Macs can run it below.
16 likesSpecifications
- Publisher
- Family
- Gemma 2
- Parameters
- 2B
- Release Date
- 2024-07-30
- License
- Gemma Terms
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HuggingFace
How Much VRAM Does Gemma 2 2B Pytorch Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 4.4 GB | — | 4.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Gemma 2 2B Pytorch?
BF16 · 4.4 GBGemma 2 2B Pytorch (BF16) requires 4.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 6+ 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 Pytorch?
BF16 · 4.4 GB33 devices with unified memory can run Gemma 2 2B Pytorch, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Gemma 2 2B Pytorch need?
Gemma 2 2B Pytorch requires 4.4 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 2B × 16 bits ÷ 8 = 4 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF164.4 GB- Can I run Gemma 2 2B Pytorch on a Mac?
Gemma 2 2B Pytorch requires at least 4.4 GB at BF16, 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 Pytorch locally?
Yes — Gemma 2 2B Pytorch can run locally on consumer hardware. At BF16 quantization it needs 4.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 2 2B Pytorch?
At BF16, Gemma 2 2B Pytorch can reach ~663 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~149 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 ÷ 4.4 × 0.55 = ~663 tok/s
Estimated speed at BF16 (4.4 GB)
~663 tok/s~149 tok/s~495 tok/s~410 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 Pytorch?
At BF16, the download is about 4.00 GB.
- Which GPUs can run Gemma 2 2B Pytorch?
35 consumer GPUs can run Gemma 2 2B Pytorch at BF16 (4.4 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 Pytorch?
33 devices with unified memory can run Gemma 2 2B Pytorch at BF16 (4.4 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.