Gemma 2B Pytorch — Hardware Requirements & GPU Compatibility
Chat
Gemma 2B Pytorch is a 2B-parameter open language model from Google in the Gemma 2 family. At Q4_K_M it needs about 1.32 GB of VRAM — see which GPUs and Macs can run it below.
6 downloads
9 likesSpecifications
- Publisher
- Family
- Gemma 2
- Parameters
- 2B
- Release Date
- 2024-06-27
- License
- Gemma Terms
Get Started
HuggingFace
How Much VRAM Does Gemma 2B Pytorch Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.9 GB | — | 0.85 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.0 GB | — | 0.88 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.1 GB | — | 0.97 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.1 GB | — | 1.00 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 1.3 GB | — | 1.20 GB | 4-bit medium quantization — most popular sweet spot |
| 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 2B Pytorch?
Q4_K_M · 1.3 GBGemma 2B Pytorch (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 2B Pytorch?
Q4_K_M · 1.3 GB33 devices with unified memory can run Gemma 2B Pytorch, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Gemma 2B Pytorch need?
Gemma 2B Pytorch 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 2B Pytorch?
For Gemma 2B Pytorch, 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 Q2_K at 0.9 GB.
VRAM requirement by quantization
Q2_K0.9 GBQ4_01.1 GBQ4_K_S1.2 GBQ4_K_M ★1.3 GBQ5_K_S1.5 GBQ8_02.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Gemma 2B Pytorch on a Mac?
Gemma 2B Pytorch requires at least 0.9 GB at Q2_K, 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 2B Pytorch locally?
Yes — Gemma 2B Pytorch 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 2B Pytorch?
At Q4_K_M, Gemma 2B Pytorch 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 2B Pytorch?
At Q4_K_M, the download is about 1.20 GB. The full-precision Q8_0 version is 2.00 GB. The smallest option (Q2_K) is 0.85 GB.
- Which GPUs can run Gemma 2B Pytorch?
35 consumer GPUs can run Gemma 2B Pytorch 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 2B Pytorch?
33 devices with unified memory can run Gemma 2B Pytorch 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.