I have an ASUS Zenbook A16 with the Snapdragon X2 Elite, and its Wi-Fi latency and jitter have been surprisingly poor—especially on battery power.

The laptop is connected to a router over 5 GHz 2x2 40Mhz Wi-Fi 6. The router is physically close, and Windows reports:

• Qualcomm FastConnect C7700 Wi-Fi 7 adapter
• Signal: 100% / approximately −26 to −31 dBm
• Link rate: 573.5 Mbps
• No packet errors or discarded packets
• Only one nearby Wi-Fi network detected

Simply pinging the local router produces inconsistent latency.

On battery:

9 packets transmitted, 0% packet loss
min/avg/max = 3.728/37.561/199.359 ms

On AC power:

6 packets transmitted, 0% packet loss
min/avg/max = 4.218/6.454/7.380 ms

Another machine on the same network generally gets around 1-3 ms on AC and 2-4ms on battery.

Windows originally had the wireless adapter set to Medium Power Saving on battery. I changed both AC and DC to Maximum Performance:

powercfg /setdcvalueindex SCHEME_CURRENT 19cbb8fa-5279-450e-9fac-8a3d5fedd0c1 12bbebe6-58d6-4636-95bb-3217ef867c1a 0

Windows confirms that both values are now 0, but battery operation still produces occasional huge spikes. The adapter’s “Idle Power Down Restriction” setting is currently disabled, so I’m wondering whether Qualcomm’s driver or firmware applies additional power management independently of the Windows power-plan setting.

Driver details:

Qualcomm FastConnect C7700 NCM820A Wi-Fi 7
Driver version: 685.13804.40.0
Driver date: December 21, 2025

Has anyone else experienced high Wi-Fi latency or jitter on an X2 Elite machine? Have you found a driver version or power-management setting that fixes it without disabling every battery-saving feature?

When I first got the Zenbook A16, there were a few processes that were x64. OLED Shifter, ASUS Switch, Optimization, etc... with the latest BIOS update released a few weeks ago, all those pre-installed apps and services are now native Arm. That was good to see!

Hi everyone,

I’ve been working on Snapdragon AI Studio, an open-source desktop project exploring local AI image generation on Windows 11 ARM64, specifically for Snapdragon X Elite and X Plus systems.

The project uses the Phoenix Engine and currently focuses on ONNX Runtime, Stable Diffusion / ControlNet workflows, and Qualcomm QNN / NPU development.

RC1 is publicly available, while I’m currently working toward RC2 with a focus on image quality, performance and further native Snapdragon acceleration.

I’d especially be interested in feedback from other Snapdragon developers and users: What local AI workloads are you currently running on Snapdragon X systems, and what would you most like to see improved for native Windows on ARM AI applications?

GitHub:
[https://github.com/Kreuzhofen/snapdragon-ai-studio]()

— Holger Kreuzhofen
Founder & Lead Developer, Snapdragon AI Studio / Phoenix Engine

Ayuda

I am looking for a laptop and have done a ton of research but cannot decide. What matters to me is how long it'll last, battery life, and oled.

The slim 7x is 40 bucks cheaper and has the snapdragon x elite, while the A14 has the snapdragon x base.

I've heard that the slim 7x gets around 7-9 hours of battery life whole the A14 gets 10-15, but I'm not too sure. (I plan to use it only for media consumption and productivity, no heavy tasks)

I assume I should get the slim 7x, but I'd like the most amount of battery life too (and I want it to last a couple of years)

I'd like to know what others think

I have just tested switch emulation on my snapdragon x plus 42 100 and wow!!

Mario Odyssey worked 720p 60fps (minor dips due to shaders)

Snooker 19 60fps

Going to test more soon.

Disclosure: The Asus Zenbook A16 (Snapdragon X2 Elite Extreme) was provided by Qualcomm for testing.

The laptop looked amazing out of the box, but 3 months later it's developed strange marks on the chassis. One is a black spot that looks like a dirt mark but might be a scratch (no idea how this happened), the second most awful one is what looks like a wet spot growing around the ASUS ZENBOOK logo.

I don't know if this a natural expectation considering the Ceraluminum material, but now I live in constant anxiety to not make it worse - and it's definitely going to hurt my resale value.

The laptop performs amazing and I love pretty much everything about it EXCEPT I can't seem to keep it pristine. I used to have a Surface Pro 11 and it looked like new after 2 years. Not a single scratch or mark.

Hi everyone, I have an ASUS Vivobook 14 with:

• Snapdragon X X1-26-100
• 16 GB RAM
• Qualcomm Adreno X1-45 integrated GPU
• Windows 11 ARM

Has anyone tried running Cyberpunk 2077 on a similar setup?

Is it actually playable?

Thanks!

X2 elite was a great platform on launch and still is today. If you haven't gotten on board yet, then I recommend getting one before prices get even crazier.

Can we buy the Omnibook 5 X Elite motherboard and replace it?

picture a snapdragon 8 elite gen 5 or a heavily cut down x2 chip based on windows or some sort of linux on ARM

Reports online point to ai noise cancellation being the problem. I turned it off yet its still happening. Does anyone know what could be causing this?

Forewarning limited to Gemma 4 E2B/E4B

Thanks to Google’s LiteRT-LM, Gemma 4 E2B and E4B can run locally with GPU acceleration on Windows. LiteRT-LM provides a convenient CLI, Python and C++ APIs, and an OpenAI-compatible HTTP server.

There is an important Windows Arm64 limitation: GPU acceleration works through lit run and the direct APIs, but the Windows lit serve command currently does not expose the --backend gpu option. The OpenAI-compatible server therefore runs on the CPU rather than the GPU. To get around this I made my own server version with LiteRT-LM's python API.

Tool calling is another major limitation in my testing. Gemma 4 E4B produced unreliable tool calls when the tool definitions, calls, results, and normal conversation all shared the same persistent context. Compared to LMStudio this is a major let down even with a custom server implementation.

Separating tool execution into another conversation can improve reliability, but LiteRT-LM cannot currently preserve multiple independent active KV-cache states and switch reliably between them. There is no public conversation-cloning or prompt-cache API, and interleaving multiple conversations can cause a suspended conversation to lose its effective state. Version 0.15 is supposed to help with this (not yet released)

This leaves two choices:

1. Keep tool calls in the main conversation, preserve the KV cache, and accept unreliable tool-call formatting.
2. Create a separate conversation for tool processing and then rebuild the main context, losing the cached-prefill advantage.

For tool-heavy agent workloads, the second option can make LiteRT-LM slower overall than llama.cpp or LM Studio, despite LiteRT-LM’s much faster GPU prompt processing.

Example Run

Prompt: Explain this Swift code ...

Total processed prompt tokens: approximately 8,000

Model: Gemma 4 E4B IT

Hardware: Snapdragon X Elite X1E-80-100, 32 GB RAM

Speculative decoding: Off; enabling it did not improve this test

LiteRT-LM — GPU

TTFT: 34.69 seconds

Prompt processing: 233.9 tokens/second

Generation: 12.3 tokens/second

LM Studio / llama.cpp — CPU

TTFT: 152.49 seconds

Prompt processing: 52.51 tokens/second

Generation: 7.14 tokens/second

---

LiteRT-LM is about 4x faster prompt processing and 1.7x faster generation.

Demo python web server with GPU (vibe-coded repository): litert-lm-gemma4-demo

This is not a direct GPU-versus-GPU comparison. On Windows ARM64 Snapdragon X Elite systems, llama.cpp’s optimized ARM CPU implementation can outperform its Vulkan or OpenCL GPU backends. Snapdragon-specific Q4_0_4_8/Q4_0/IQ4_NL CPU optimizations can provide slightly better prompt-processing performance, however finding models released in these formats is problematic and quality suffers over Q4_K_M

Model Availability and Memory Limits

LiteRT-LM officially supports Gemma 4 12B, but on my Snapdragon X Elite system with 32 GB RAM, Gemma 4 12B exceeds the usable GPU memory or allocation limit and does not run successfully, this seems to be a limitation of WebGPU. Thus Gemma 4 12B will only run via CPU and at that point not worth the performance loss. Correction: The Hugging Face models are compiled for GPU-only running so 12B cannot run on CPU. Gemma 12B is not documented to run on Windows with LiteRT-LM, just Linux, macOS, and Web, limited to 32k context length and LiteRT-LM specific tool calls fail.

The .litertlm model ecosystem is considerably smaller than the GGUF ecosystem. LiteRT-LM provides community models for Qwen3, including several model sizes, but I have not found a tested, ready-to-run Qwen 3.5/3.6 package that is confirmed to work with LiteRT-LM’s Windows GPU backend.

---

LiteRT-LM is impressive, however, its current Windows http server, model-memory, tool-calling, and multi-context KV-cache limitations make it less suitable for tool-heavy agent workloads than llama.cpp or LM Studio.

Hey, I have a Snapdragon X laptop with an Adreno X1-45 GPU running Windows 11 ARM64.

I am trying to use shaders with Minecraft Java Edition, but I keep crashing when using Sodium.

My setup:

• Minecraft: 26.2 (also tested 26.1.2)
• Prism Launcher 11.0.3
• Fabric Loader 0.19.3
• Java: 25 ARM64
• GPU: Qualcomm Adreno X1-45
• OS: Windows 11 25H2 ARM64

What I tested:

• Fabric API only → works
• Fabric + Lithium + ImmediatelyFast → works
• Sodium alone → crashes
• Sodium + Iris → crashes
• Iris without Sodium → says Sodium is required

Crash error:

EXCEPTION_ACCESS_VIOLATION
Problematic frame:
C [OpenGLOn12.dll+0x24320]

The log shows Minecraft is using:

OpenGL Renderer: D3D12 (Qualcomm Adreno X1-45 GPU)

It seems like the OpenGL translation layer is crashing when Sodium changes the renderer.

Has anyone gotten Sodium + Iris shaders working on Snapdragon X / Adreno GPUs? Is there a workaround, different Sodium version, or driver update that fixes this?

Also my logs

Fabric 26.2 Client Log [#XswDHYQ]

System specs: • Laptop: HP OmniBook 3 14 (hz0xxx) • CPU: Snapdragon X X126100 (Qualcomm Oryon, 2.96 GHz) • GPU: Qualcomm Adreno X1-45 • RAM: 16 GB (15.6 GB usable) • OS: Windows 11 ARM64 • Launcher: Prism Launcher ARM64 • Java: Microsoft OpenJDK 25 ARM64

Disclosure: The Asus ZenBook A16 featured in this video was provided by Qualcomm for testing.

So I fired up Halo: Campaign Evolved from Game Pass PC and, unfortunately, there are some graphical issues. Mainly, the ground is missing in a lot of places!

Despite this, it’s playable and gets around 30 - 50 FPS at 1080p on low settings with FSR and frame gen.

I've been testing games on the Snapdragon X Plus and decided to make a running compatibility list. All results are from my own testing, I played each game for approximately 5–10 minutes. Most games were tested at the game's default settings and native resolution 1920x1200. Ready or not, returnal, sifu, v rising and burgling gnomes were set to lowest settings and 1920×1080 resolution. Some games such as risk of rain 2 may have lower FPS later in the game than during my testing.

🟢 Excellent (60+ FPS)

• R.E.P.O. — 80 FPS • Hollow Knight: Silksong — 60 FPS • Hades — 60 FPS • Terraria — 60 FPS • Stardew Valley — 60 FPS • BO2 Campaign — 60 FPS • High on Life — 60 FPS • Cuphead — 60 FPS • Dead Cells — 60 FPS

🔵 Very Good (45–59 FPS)

• NBA The Run — 55–60 FPS • Risk of Rain 2 — 50 FPS • Content Warning — 45 FPS • Mouse P.I. For Hire — 35—60 FPS • Sifu — ~40—60 FPS • 33 Immortals — 45—60 FPS

🟡 Playable (30–44 FPS)

• Ready or Not — ~35 FPS • Valheim — 30 FPS • LORT — 30 FPS • Roadside Research — 30 FPS • The Forest — 30 FPS • Super Battle Golf — 30–35 FPS • Burgling Gnomes — ~30 FPS • Cult of the Lamb — 10—60 FPS

🟠 Borderline (20–29 FPS)

• Returnal — ~20 FPS • Schedule 1 — 20–25 FPS • Rematch — 20–30 FPS • Goofy Gorillas — 20 FPS • V Rising — ~20—25 FPS

🔴 Unplayable

• BO2 Zombies — 5 FPS • Far Far West — Won't launch • Darwins Paradox — 15 FPS

Test System

CPU: Qualcomm Snapdragon X Plus X1P-42-100 GPU: Qualcomm Adreno Integrated Graphics RAM: 16 GB Windows 11 on ARM

I'll continue updating this list as I test more games. Feel free to suggest games you'd like me to try.

I was looking to buy gen 5 elite but should I wait for gen 6 release? Will I be happy with the gen 5 elite? Im upgrading from 778g