Gemma 2 2B Jpn IT — Hardware Requirements & GPU Compatibility
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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.
Runs great on a 24 GB GPU
Runs from 8 GB (BF16 · 16K)Best on 24 GB (BF16 · ~114 tok/s · 32K)Macs from 16 GB (BF16 · ~24 tok/s)
Gemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gemma 2 2B Jpn IT
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve google/gemma-2-2b-jpn-it --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (4 GB headroom) · BF16
- Generation speed
- ~41 tok/s
- generation speed
- Cost per 1M output tokens
- $0.48
- per 1M output tokens
How Much VRAM Does Gemma 2 2B Jpn IT Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 5.8 GB | 9.8 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~114 tok/s |
Selected BF16
Brain floating point 167.6GBat 16K context
- Weights
- 5.2 GB
- KV cache
- 2.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.4 GB free · ~114 tok/s
≥50 tok/s needs ≥442 GB/s · RTX 5090 ~203, RTX 3090 Ti ~114
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Gemma 2 2B Jpn IT, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 0.4 GB free · up to 16K ctx
16 GPUs · 1 device family
RTX 3080~86Simulate output on RTX 3080RTX 2080 Ti~70Simulate output on RTX 2080 TiRTX 3070 Ti~69Simulate output on RTX 3070 TiOrin Nano~12Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.4 GB free · up to 32K ctx
1 GPU
RX 7900 XT~84Simulate output on RX 7900 XT32–40 GB
fits · 24.4 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~203Simulate output on RTX 5090M5 Max36 GB~56Simulate output on Mac Studio M5 Max (36 GB)M532 GB~19Simulate output on MacBook Air 13" M5 (32 GB)Orin~23Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~15Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Alternatives to Gemma 2 2B Jpn IT
Gemma 2 family · 20 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MiniCPM5 2BOpenBMB | — | 2.5 GB | ~345 | same size |
| North Micro Vision InstructCohere | — | 7.1 GB | ~119 | same size |
| LFM2.5 2.6BLiquid AI | — | 2.3 GB | ~321 | same size |
| Granite 3.1 2B InstructIBM | — | 3.2 GB | ~329 | same size |
| Phi 2Microsoft | — | 2.6 GB | ~248 | same size |
| Ouro 2.6B ThinkingByteDance | — | 12.1 GB | ~102 | same size |
Specifications
- Publisher
- Family
- Gemma 2
- Parameters
- 2.6B
- Release Date
- 2024-09-25
- License
- Gemma Terms
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
Can I run Gemma 2 2B Jpn IT on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Gemma 2 2B Jpn IT. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 5.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gemma 2 2B Jpn IT locally?
Yes — Gemma 2 2B Jpn IT can run locally on consumer hardware. 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 ~835 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 5.75 × 0.65 = ~904 tok/s
Estimated speed at BF16 (5.8 GB)
Real-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?
52 consumer GPUs can run Gemma 2 2B Jpn IT at BF16. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Gemma 2 2B Jpn IT?
151 devices with unified memory can run Gemma 2 2B Jpn IT at BF16 (5.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.