Gemma 2 2B IT — Hardware Requirements & GPU Compatibility
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Google Gemma 2 2B IT is a 2-billion parameter instruction-tuned model from Google's Gemma 2 family, the smallest variant in the Gemma 2 series. It is designed for efficient local inference on resource-constrained hardware, handling basic conversational tasks and simple instruction following at minimal compute cost. The model can run on GPUs with as little as 4GB of VRAM when quantized, and even on CPU-only setups. Released under the Gemma license.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 8K)Best on 24 GB (Q4_K_M · ~379 tok/s · 8K)Macs from 8 GB (Q4_K_M · ~41 tok/s)
Gemma Terms
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Gemma 2 2B IT
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run gemma2:2b-instruct-q4_K_MQ4_K_M fits fully on the RTX 4090 · 21.5 GB free · 8K context
Download: gemma-2-2b-it-Q4_K_M.gguf1.71 GBbartowski
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3060 12GB (9 GB headroom) · Q4_K_M
- Generation speed
- ~135 tok/s
- generation speed
- Cost per 1M output tokens
- $0.12
- per 1M output tokens
How Much VRAM Does Gemma 2 2B IT Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.2 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 8K context · ~537 tok/s | |
| 1.3 GB | — | 3-bit small — 3-bit small quantization | Fits: 8K context · ~520 tok/s | |
| 1.4 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 8K context · ~468 tok/s | |
| 1.4 GB | — | 4-bit · legacy — 4-bit legacy quantization | Fits: 8K context · ~455 tok/s | |
| 1.7 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 8K context · ~379 tok/s | |
| 2.0 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 8K context · ~320 tok/s | |
| 2.4 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 8K context · ~277 tok/s | |
| 2.9 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 8K context · ~228 tok/s |
Selected Q4_K_M
4-bit medium quantization2.5GBat 8K context
- Weights
- 1.2 GB
- KV cache
- 1.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 21.5 GB free · ~379 tok/s
≥50 tok/s needs ≥133 GB/s · RTX 5090 ~673, RTX 3090 Ti ~379
Which Hardware Can Run Gemma 2 2B IT?
Q4_K_M · 1.7 GBGemma 2 2B IT (Q4_K_M) requires 1.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Gemma 2 2B IT, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 5.5 GB free · up to 8K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~286Simulate output on RTX 3080RTX 2080 Ti~231Simulate output on RTX 2080 TiRTX 3070 Ti~229Simulate output on RTX 3070 TiM3*8 GB~41Simulate output on iMac 24" M3 (8 GB)Orin Nano~38Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~28Simulate output on iPhone 1720 GB
fits · 17.5 GB free · up to 8K ctx
1 GPU
RX 7900 XT~278Simulate output on RX 7900 XT32–40 GB
fits · 29.5 GB free · up to 8K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~673Simulate output on RTX 5090M5 Max36 GB~186Simulate output on Mac Studio M5 Max (36 GB)M532 GB~62Simulate output on MacBook Air 13" M5 (32 GB)Orin~77Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~51Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Gemma 2 2B IT?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
Here are all 2 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- LMArena Text
LMArena · Oct 2026Arena
Arena1200.1
#146/188 open
- MMLU-Pro
TIGER-LabIndependent
Independent15.6%
#136/146 open
Alternatives to Gemma 2 2B IT
Gemma 2 family · 21 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| WeVisOne VL 2BTencent | — | 3.6 GB | ~328 | same size |
| MiniCPM5 2BOpenBMB | — | 2.5 GB | ~345 | same size |
| North Micro Vision InstructCohere | — | 3.7 GB | ~323 | 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 |
Fine-tunes & variants(3)
Specifications
- Publisher
- Family
- Gemma 2
- Parameters
- 2.6B
- Context Length
- 8,192 tokens
- Release Date
- 2024-07-16
- License
- Gemma Terms
Frequently Asked Questions
How much VRAM does Gemma 2 2B IT need?
Gemma 2 2B IT requires 1.7 GB of VRAM at Q4_K_M, or 5.8 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 2.6B × 4.8 bits ÷ 8 = 1.6 GB
KV Cache + Overhead ≈ 0.1 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
What's the best quantization for Gemma 2 2B IT?
For Gemma 2 2B IT, Q4_K_M (1.7 GB) offers the best balance of quality and VRAM usage. Q5_0 (1.8 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.8 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Gemma 2 2B IT on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Gemma 2 2B IT. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run Gemma 2 2B IT locally?
Yes — Gemma 2 2B IT can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Gemma 2 2B IT?
At Q4_K_M, Gemma 2 2B IT can reach ~2775 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~379 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 ÷ 1.73 × 0.65 = ~3006 tok/s
Estimated speed at Q4_K_M (1.7 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 IT?
At Q4_K_M, the download is about 1.57 GB. The full-precision BF16 version is 5.23 GB. The smallest option (IQ2_XXS) is 0.72 GB.
Which GPUs can run Gemma 2 2B IT?
52 consumer GPUs can run Gemma 2 2B IT at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Gemma 2 2B IT?
163 devices with unified memory can run Gemma 2 2B IT at Q4_K_M (1.7 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.