EdgeNVIDIA

NVIDIA Jetson Orin Nano 8GB

Curated Aggregate·2026-04-164 workloads · 4 records
MyAI RatingNot scoredInsufficient comparison evidence

VRAM

8 GB

TDP

15 W

MSRP

$499

Perf/W

0.53 tok/s/W

Cost/1K tok

$0.66/M

Tested

2026-04-16

Quick answer

How fast is NVIDIA Jetson Orin Nano 8GB for local AI workloads?

NVIDIA Jetson Orin Nano 8GB has a source-attributed result of 8.0 tok/s on Llama 3 8B Q4 (batch 1, 2048-token context, Q4_K_M; runtime not documented). This is a reference report, not an independently verified lab result. A 70B Q4_K_M artifact needs roughly 40GB for weights alone; smaller memory configurations require explicit offload or model splitting and do not establish full-GPU residency.

Source: MyAIHardware benchmark database (bench-orin-nano-l3-8b-q4)As of 2026-04-16

Overview

Edge Entry

NVIDIA Jetson Orin Nano 8GB — entry-level edge AI module. 1024 CUDA cores, 32 Tensor Cores, 8GB LPDDR5, 7-15W TDP. Ampere architecture in a developer kit form factor.

AI Usefulness

Runs 3B Q4 models at ~5-10 tok/s. Sufficient for simple chatbots, keyword spotting, and basic RAG. Not suitable for 7B+ models at usable speeds. Best for learning edge AI deployment and prototyping before scaling to AGX Orin.

Verdict

NVIDIA Jetson Orin Nano 8GB with 8GB VRAM at 15W TDP, scored across 4 workloads with 4 benchmark records.

Reference workload

Llama 3 8B Q4

8 tok/s

Quantization

Q4_K_M

2K context · batch 1

LLM Inference Performance

05101520Llama 3 8BQ4Phi-3 MiniMistral 7B

Benchmarks (4 workloads)

WorkloadScoreQuantContextσStatusTested
Llama 3 8B Q4

llm

8.0tok/sQ4_K_M2K, Curated Aggregate2026-04-16
Phi-3 Mini

llm

18.0tok/sQ4_K_M4K, Curated Aggregate2024-06-25
Whisper transcription

audio

2.5x RTFP16, , Curated Aggregate2025-07-10
Mistral 7B

llm

5.5tok/sQ4_K_M4K, Curated Aggregate2024-05-30

MyAI Score: not scored

There is insufficient comparable evidence to score NVIDIA Jetson Orin Nano 8GB. A score needs results for multiple devices with matching workload, runtime version, quantization, context and batch size.

Workload Fit

Planning estimates for 8GB: weights plus at least 2GB or 10% overhead. Actual KV cache depends on model, context and cache format; confirm the artifact before buying.

Q4
Q8
FP16
7-8B
Good
Won't fit
Won't fit
13-14B
Won't fit
Won't fit
Won't fit
32B
Won't fit
Won't fit
Won't fit
70B
Won't fit
Won't fit
Won't fit
Reported batch-one examples
Llama 3 8B Q48 tok/s
Whisper transcription3 x RT
Phi-3 Mini18 tok/s

Source

Curated from public sources (llama.cpp logs / vendor specs / vLLM community) — llama.cpp CUDA on Jetson.

View sourceHow we benchmark →

Public Trust Layer

Trust score

6/10

MyAI rating

Not scored

Runs

Not documented

Freshness

Aging

Source-linked row with explicit verification status.

Record date: 2026-04-16; 146 days old.

Open primary source

Best place to start

Where to buy

Most buyers

Retailer we'd check first

Amazon

For mainstream AI hardware, Amazon usually updates street pricing, seller availability, and shipping speed faster than most comparison sites.

View current Amazon listing
  • +Use $499 as your price anchor unless the part is clearly supply-constrained or newly launched.
  • +Check the exact cooler, board partner, or memory configuration before buying. The silicon may match, but noise and thermals do not.
  • +If the Amazon price looks inflated, wait or compare against recent street pricing rather than paying a panic premium.

There is insufficient comparable evidence for a purchase recommendation. Check model fit, software support and a current seller quote.

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