LLMs ·

Gemma 2B IT

Parameters
2.5B
Architecture
Dense
Max context
8K tokens
License
gemma
Released
2024-02

How to run it

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run gemma:2b

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/google/gemma-2b-it (opens in a new tab)

ollama run hf.co/google/gemma-2b-it

Original weights · Hugging Face

Full-precision files from Google, for fine-tuning, vLLM, Transformers.

huggingface.co/google/gemma-2b-it (opens in a new tab)

Free Hugging Face account needed: request access on the model page, and Google approves it (usually within minutes to a few hours).

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

Apps that run Gemma 2B IT

Our pick for each kind of computer, assuming the model fits in its memory. Pick your own GPU or device in the Model Finder for an exact answer.

Mac (Apple silicon)

Ollama

One command with Ollama’s own build of this model.

Also good:

Windows PC, NVIDIA GPU

Ollama

One command with Ollama’s own build of this model.

Also good:

Windows PC, AMD Radeon

Lemonade

AMD’s own app: llama.cpp tuned for Radeon on Vulkan and ROCm.

Also good:

Linux, NVIDIA GPU

Ollama

One command with Ollama’s own build of this model.

Also good:

Linux server, 80 GB+ GPUs

vLLM

The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.

Also good:

App Type Opens it as Systems Engine Start
LM Studio Desktop app GGUF macOSWindowsLinux llama.cpp, MLX Open in LM Studio
Ollama CLI + API Ollama library macOSWindowsLinux llama.cpp, MLX 
ollama run gemma:2b
llama.cpp CLI + API GGUF macOSWindowsLinux llama.cpp 
llama-server -hf google/gemma-2b-it:Q4_K_M
Jan Desktop app GGUF macOSWindowsLinux llama.cpp, MLX Open in Jan
vLLM Server Original weights WSLWindowsLinux vLLM 
vllm serve google/gemma-2b-it
SGLang Server Original weights WSLWindowsLinux SGLang 
python3 -m sglang.launch_server --model-path google/gemma-2b-it
Docker Model Runner CLI + API GGUF macOSWindowsLinux llama.cpp, vLLM 
docker model run hf.co/google/gemma-2b-it:Q4_K_M
Lemonade Desktop app GGUF macOSWindowsLinux llama.cpp, ONNX Runtime GenAI 
lemonade pull google/gemma-2b-it:Q4_K_M
Unsloth Desktop Desktop app GGUF macOSWindowsLinux llama.cpp, MLX, stable-diffusion.cpp Open in Unsloth Desktop
Atomic Chat Desktop app GGUF macOSWindowsLinux llama.cpp, MLX-VLM Open in Atomic Chat
GPT4All Desktop app GGUF macOSWindowsLinux llama.cpp Get GPT4All
KoboldCpp Desktop app GGUF macOSWindowsLinux llama.cpp Get KoboldCpp
Text Generation WebUI Desktop app GGUF macOSWindowsLinux llama.cpp, Transformers, ExLlamaV3 Get Text Generation WebUI
llamafile CLI + API GGUF macOSWindowsLinux llama.cpp Get llamafile
Msty Studio Desktop app GGUF macOSWindowsLinux Ollama, llama.cpp, MLX Open in Msty Studio
Llama (macOS) Desktop app GGUF macOS llama.cpp Open in Llama (macOS)
RecurseChat Desktop app GGUF macOS llama.cpp Open in RecurseChat
node-llama-cpp Library GGUF macOSWindowsLinux llama.cpp Get node-llama-cpp
PocketPal AI Mobile app GGUF iOSAndroid llama.cpp App Store Google Play
ChatterUI Mobile app GGUF Android llama.cpp Download the APK
LLM Farm Mobile app GGUF iOS llama.cpp Not on the App Store or TestFlight for now (checked 2026-11-09).

Which apps can open a model depends on the files published for it: GGUF for llama.cpp-based apps, MLX for Apple’s engine, the original weights for vLLM and SGLang. Compare every app.

Memory needed

Weights plus KV cache and runtime overhead. Leave a little extra free for the operating system and other apps. Try another context length.

Quantization Quality Weights Total at 8K context Download
Q2_K Low 1.0 GB 1.8 GB
Q3_K_M Low 1.2 GB 2.0 GB
Q4_K_M Good 1.5 GB 2.3 GB
Q5_K_M High 1.8 GB 2.5 GB
Q6_K High 2.1 GB 2.8 GB
Q8_0 Very high 2.7 GB 3.4 GB
F16 Full 5.0 GB 5.8 GB Original weights (opens in a new tab)

Best hardware for Gemma 2B IT

The least expensive GPU in our catalog that runs it without offloading is the Intel Arc A380 (launch price $139) at Q6_K.

Quality Hardware Quant Memory Speed Runs
12Basic quality (D) · 12 of 100: how capable the model is at F16, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 3060 12GBNVIDIA GeForce RTX 3060 12GB F16 6.4 GB / 12.0 GB 41.3 tok/s Runs great
12Basic quality (D) · 12 of 100: how capable the model is at F16, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 SUPERNVIDIA GeForce RTX 4070 SUPER F16 6.4 GB / 12.0 GB 57.8 tok/s Runs great
12Basic quality (D) · 12 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4060NVIDIA GeForce RTX 4060 Q8_0 3.8 GB / 8.0 GB 67.7 tok/s Runs great
12Basic quality (D) · 12 of 100: how capable the model is at F16, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. Sapphire NITRO+ AMD Radeon RX 9070 XT OCAMD Radeon RX 9070 XT F16 6.6 GB / 16.0 GB 63.2 tok/s Runs great
12Basic quality (D) · 12 of 100: how capable the model is at F16, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPERNVIDIA GeForce RTX 4070 Ti SUPER F16 6.6 GB / 16.0 GB 77.1 tok/s Runs great
12Basic quality (D) · 12 of 100: how capable the model is at F16, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPERNVIDIA GeForce RTX 4080 SUPER F16 6.6 GB / 16.0 GB 84.4 tok/s Runs great
12Basic quality (D) · 12 of 100: how capable the model is at F16, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. ASUS Dual GeForce RTX 5060 Ti 16GB GDDR7 OC EditionNVIDIA GeForce RTX 5060 Ti 16GB F16 6.6 GB / 16.0 GB 51.4 tok/s Runs great
12Basic quality (D) · 12 of 100: how capable the model is at F16, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. ASUS TUF Gaming GeForce RTX 5070 Ti 16GB GDDR7 OC EditionNVIDIA GeForce RTX 5070 Ti F16 6.6 GB / 16.0 GB 103 tok/s Runs great
12Basic quality (D) · 12 of 100: how capable the model is at F16, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 Founders EditionNVIDIA GeForce RTX 5080 F16 6.6 GB / 16.0 GB 110 tok/s Runs great
12Basic quality (D) · 12 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. Intel Arc B580 Limited EditionIntel Arc B580 Q8_0 4.0 GB / 12.0 GB 86.7 tok/s Runs great

Check it on your own hardware

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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