Qualcomm Snapdragon 8 Elite
No editorial image yet — generic vendor mark shown. Credentials in spec table below.
Late-2024 Android flagship SoC. Oryon CPU + Hexagon NPU at ~80 TOPS INT8. 8B-class models become viable on-device with adequate quantization.
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Sub-scores sum to 291 / 1000. Headline = 291 × 0.70 (Estimated-confidence discount) = 204. This is an algorithmic performance-tier score — distinct from, and often lower than, the editorial “Our verdict” below, which weighs value and real-world fit (especially for hardware we haven’t measured yet). How scoring works →
Extrapolated from 90 GB/s bandwidth — 7.2 tok/s estimated. No measured benchmarks yet.
Plain-English: Doesn't fit modern chat models usefully.
Verdicts extrapolated from catalog VRAM + bandwidth + ecosystem flags. Hover any chip for the rationale. Want measured numbers? Submit your own run with runlocalai-bench --submit.
What it does well
The Qualcomm Snapdragon 8 Elite is the 2025 flagship Android phone SoC and the most credible Android-side AI chip — 8 Oryon CPU cores + Adreno 830 GPU + dedicated Hexagon NPU rated at 45 TOPS. Ships in flagship Android phones from Samsung Galaxy S25 Ultra, OnePlus 13, Xiaomi 15 Pro, ASUS ROG Phone 9 at $799-$1,499 retail. For on-device AI features (Google Gemini Nano, Samsung Galaxy AI, OEM-specific AI features), Snapdragon 8 Elite is the canonical Android-side AI accelerator. The chip runs sub-3B class Gemini Nano-tier models comfortably for tasks like summarization, translation, smart reply, on-device image generation. For LLM-curious developers, Qualcomm's AI Hub + ONNX Runtime + llama.cpp Vulkan paths run on the Adreno GPU with reasonable throughput.
Where it breaks
- Phone form factor limits all serious AI development. No Terminal-grade access, no proper Python development, sandboxed runtime. You consume AI features in apps, not develop against them on the device.
- Memory ceiling at 12-16 GB system RAM. Phone-tier memory limits LLMs to 1-3B class.
- Battery life under sustained AI is minutes, not hours. Sustained inference drains battery rapidly.
- No CUDA, no ROCm, no Metal. Adreno + Hexagon NPU + Vulkan only.
- Day-zero new model architecture support arrives last on phone SoCs. Mobile silicon is the slowest tier for LLM framework support.
- OEM software fragmentation. Different phones expose different AI APIs (Samsung Galaxy AI vs OEM custom vs Google AICore).
Ideal model range
- Sweet spot: Sub-3B class on-device inference (Gemini Nano, Apple Intelligence-equivalent on Android, smaller transformer-based features).
- Sweet spot: Real-time on-device features (live translation, voice transcription, smart reply, summarization).
- Sweet spot: Phone-form-factor creator AI (image generation, photo editing AI, voice synthesis).
- Bad fit: 7B+ FP16 anything, fine-tuning, AI development workflows, CUDA-required.
Verdict
Buy a phone with Snapdragon 8 Elite for the phone use cases (camera, gaming, productivity) — the AI is a feature, not the reason. For on-device AI features (summarization, translation, image generation), 8 Elite is genuinely capable. For most readers, this verdict is informational reference about the silicon powering 2025-2026 flagship Android AI features.
Skip this if you're shopping for AI development hardware — phones aren't the right tier. Pick a Mac mini M4, discrete-GPU laptop, or workstation for serious local AI.
How it compares
- vs Apple A18 Pro → A18 Pro powers iPhone 16 Pro with similar NPU TOPS. Apple Intelligence vs Google Gemini Nano + Samsung Galaxy AI — different ecosystem, similar capability tier.
- vs Snapdragon 8 Gen 3 → Prior-gen at lower NPU TOPS. 8 Elite is the strict generational upgrade.
- vs Google Tensor G4 → Google's custom SoC in Pixel phones with deep Gemini Nano integration. Tensor G4's NPU is lower-throughput but tightly integrated with Google's first-party AI stack.
- vs Snapdragon X Elite (laptop) → SDX Elite is the laptop variant with more cores + memory + thermal envelope. 8 Elite is phone-form factor — different scale entirely.
Overview
Late-2024 Android flagship SoC. Oryon CPU + Hexagon NPU at ~80 TOPS INT8. 8B-class models become viable on-device with adequate quantization.
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Featured in this stack
The L3 execution stacks that pick this hardware as a recommended component, with the one-line note explaining the role it plays in each.
- Stack · L3·Homelab tier·Role: Target SoC (flagship 2024-2025)Android on-device AI stack — Phi-3.5 Mini / Llama 3.2 3B via MLC LLM or Qualcomm AI Hub
Snapdragon 8 Elite Hexagon NPU at ~80 TOPS INT8 + Adreno GPU. The 16GB RAM tier enables comfortable 3-4B model headroom. Pair with Qualcomm AI Hub for production NPU-first deployment.
Specs
| VRAM | 0 GB |
| System RAM (typical) | 16 GB |
| Power draw (peak) | 5 W |
| Released | 2024 |
| Backends |
Frequently asked
Does Qualcomm Snapdragon 8 Elite support CUDA?
Where next?
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify hardware specifications.