MultimodalFlow
Real hardware · No cloud estimates

LLM Edge Inference Benchmark

All numbers measured on physical devices. Compare throughput (tok/s), first-token latency, and memory usage across different models, frameworks, and quantization levels on NVIDIA edge hardware.

Jetson AGX ThorDGX SparkJetson AGX OrinJetson Orin NX

Methodology: Batch size 1 · 4096 token context · 512 output tokens · warm-up 3 runs, average of 10 runs.

Last updated: 2026-06 · Submit a correction or new result

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Showing 22 of 22 results

Throughput(tok/s)↑ higher is better

Jetson AGX Thor
DGX Spark
Jetson AGX Orin
Jetson Orin NX
ModelDeviceFrameworkQuantTok/s ↑TTFT (ms) ↓Mem (GB) ↓Ctx Len
Llama 3.1 8BDGX SparkTensorRT-LLMFP16312.69816.84,096
Qwen2.5 7BDGX SparkvLLMFP16301.310815.64,096
Llama 3.1 8BDGX SparkSGLangFP16298.110517.04,096
Llama 3.1 8BDGX SparkvLLMFP16287.411217.24,096
Gemma 3 9BDGX SparkvLLMFP16267.812419.44,096
Qwen2.5 14BDGX SparkTensorRT-LLMFP16178.418729.34,096
Phi-4 14BDGX SparkTensorRT-LLMFP16162.320130.14,096
Qwen2.5 0.5BJetson AGX OrinOllamaQ4_K_M145.52760.54,096
Qwen2.5 7BJetson AGX ThorTensorRT-LLMINT4131.22034.84,096
Llama 3.1 8BJetson AGX ThorTensorRT-LLMINT4124.72185.14,096
Qwen2.5 7BJetson AGX ThorTensorRT-LLMINT891.62988.44,096
Llama 3.1 8BJetson AGX ThorTensorRT-LLMINT887.33129.24,096
Gemma 3 9BJetson AGX ThorTensorRT-LLMINT878.934110.14,096
Llama 3.1 8BJetson AGX ThorOllamaQ4_K_M68.44015.84,096
Phi-4 14BJetson AGX ThorTensorRT-LLMINT463.74128.94,096
Llama 3.1 8BJetson AGX Thorllama.cppQ4_K_M61.94375.64,096
Gemma 3 9BJetson AGX ThorOllamaQ4_K_M59.24236.24,096
Qwen2.5 14BJetson AGX ThorTensorRT-LLMINT852.349816.14,096
Llama 3.1 8BJetson AGX ThorTensorRT-LLMFP1651.248916.44,096
Llama 3.1 8BJetson AGX OrinTensorRT-LLMINT843.16219.04,096
Llama 3.1 8BJetson Orin NXOllamaQ4_K_M19.712405.44,096
Qwen3.6-35B-A3BJetson AGX ThorSGLangFP814.016136.08,192