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google/gemma-4-12B-it

Embedl optimized · TensorRT Edge-LLM
google/gemma-4-12B-it architecture graph
google/gemma-4-12B-it architecture graph
  • huggingface
  • AGX Orin
Labels
  • Image
  • Text
  • VLM
  • Generation
Build
Embedl-optimized
Precision
W4A16
Runtime
TensorRT Edge-LLM 0.10.1 with Embedl optimizations
Context length
4,096 tokens
Run Gemma 4 12B on AGX Orin

Google's mid-size Gemma 4 model, for chat, reasoning and coding, with images.

Methodology

  • Throughput: one user, real conversations from the public OpenAssistant set, 2,048 tokens in, 128 out, greedy, no prompt reuse. Average of 20 requests after 3 warm-up requests. End-to-end is the whole request. Prompt processing is the 2,048 prompt tokens over the time to first token.
  • Power and memory: sampled on the device during those runs. Idle is the device at rest with the model loaded; energy per token is the run's energy over its generated tokens; peak memory is the device's.
  • Accuracy: full test sets, greedy, the same model files and settings as served. GSM8K 1319, IFEval 541, MMLU-Pro 1008 questions.
  • Baseline TensorRT Edge-LLM: the original FP16 model on upstream TensorRT Edge-LLM, same settings, same device.
  • Vision-language model: measured on text only.