Gemma 2 2B Jpn IT — Hardware Requirements & GPU Compatibility
Chat
Gemma 2 2B Jpn IT is a 2.6B-parameter open language model from Google in the Gemma 2 family. At BF16 it needs about 5.75 GB of VRAM — see which GPUs and Macs can run it below.
8.0K downloads
217 likesSpecifications
- Publisher
- Family
- Gemma 2
- Parameters
- 2.6B
- Release Date
- 2024-10-02
- License
- Gemma Terms
Get Started
HuggingFace
How Much VRAM Does Gemma 2 2B Jpn IT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 5.8 GB | — | 5.23 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Gemma 2 2B Jpn IT?
BF16 · 5.8 GBGemma 2 2B Jpn IT (BF16) requires 5.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Gemma 2 2B Jpn IT?
BF16 · 5.8 GB33 devices with unified memory can run Gemma 2 2B Jpn IT, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Gemma 2 2B Jpn IT need?
Gemma 2 2B Jpn IT requires 5.8 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 2.6B × 16 bits ÷ 8 = 5.2 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF165.8 GB- Can I run Gemma 2 2B Jpn IT on a Mac?
Gemma 2 2B Jpn IT requires at least 5.8 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 Jpn IT locally?
Yes — Gemma 2 2B Jpn IT can run locally on consumer hardware. At BF16 quantization it needs 5.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Gemma 2 2B Jpn IT?
At BF16, Gemma 2 2B Jpn IT can reach ~507 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~114 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 ÷ 5.8 × 0.55 = ~507 tok/s
Estimated speed at BF16 (5.8 GB)
~507 tok/s~114 tok/s~379 tok/s~313 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 Jpn IT?
At BF16, the download is about 5.23 GB.
- Which GPUs can run Gemma 2 2B Jpn IT?
35 consumer GPUs can run Gemma 2 2B Jpn IT at BF16 (5.8 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 28 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Gemma 2 2B Jpn IT?
33 devices with unified memory can run Gemma 2 2B Jpn IT at BF16 (5.8 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.