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Qwen/Qwen3-1.7B

Qwen/Qwen3-1.7B architecture graph
Qwen/Qwen3-1.7B architecture graph
  • huggingface
  • AGX Thor
Labels
  • Text
  • LLM
  • Generation
Build
Embedl-optimized
Precision
W4A16
Runtime
TensorRT Edge-LLM 0.10.1 with Embedl optimizations
Context length
4,096 tokens
Run Qwen3 1.7B on AGX Thor

Alibaba's 1.7B Qwen3 model for chat, math and code, with optional step-by-step thinking.

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.

Files

Image Size

Model

docker.io/embedl/model-artifacts:qwen3-1-7b.huggingface_int4_embedl_tensorrt-edgellm.60d07f7b71b606f3

 1.88 GiB

Runtime

docker.io/embedl/models:runtimes.tensorrt-edgellm.arm64.2026101815014ee0

 1.01 GiB

Variants

Every build of Qwen3 1.7B on AGX Thor.

Variant Precision Speed Peak memory Energy / token Runtime
Baseline TensorRT Edge-LLM
FP16 63.8 tok/s 5.63 GiB 0.94 J
TensorRT Edge-LLM 0.10.1
Embedl optimized · TensorRT Edge-LLM This page
 W4A16 117.8 tok/s 3.43 GiB 0.53 J
TensorRT Edge-LLM 0.10.1 with Embedl optimizations
runtimes.tensorrt-edgellm.arm64.2026101815014ee0