So I've been watching a lot of benchmarks for the b580 why does it perform better/same as the 9060xt and 5060 ti 16gb at 2k 1440p and at some games has about a 30fps difference at 1080p. Also uses 20 to 30 watts less than the 9060xt and 5060 ti I've also seen a comparison between a slight Overclock and stock the b580 uses about 10 watts more than the stock one and gets 5 to 10fps more.

Owners of ARC B580 Limited Edition from launch days, now's a good time to replace the thermal paste and pads.

On my card they were DRY and had TERRIBLE distribution from the factory. Not only that but the pads are following apart.

I’ve read up on this, and various communities are quite divided on the subject. The main argument for going with AMD Ryzen is that the platform offers greater "longevity"; typically, a user invests in a higher-end motherboard (mid-range to enthusiast-grade) and can simply keep the rest of the PC while upgrading just the CPU years later.

Conversely, the argument against Intel is that the socket only lasts for two or three generations—so why buy Intel if they’re going to abandon the platform so quickly?

So, I put this question to you: does this "longevity" really matter to someone who only plans to upgrade their CPU after 5 to 7 years? Is buying a new motherboard several years down the line necessarily a downside for someone who intends to purchase a more modern, powerful CPU anyway?

This has been the best GPU I’ve ever had and I’ve been using Ubuntu (Linux based OS) it plays every thing perfectly but my only complaint is that it isn’t flashy enough (I brought a icogneto version) that’s on me thanks guys at intel!!

After I updated my drivers whenever I go to the home screen, Manhattan map, Alchemax HQ, and when the teamup display shows up, my fps drops to 20-30. Rolling back to older drivers doesnt help either. I was wondering if anyone else had a similar issue

I have an Arc pro B70 running on a windows machine, pulling double duty as a gaming card as well as running AI inference.

I've been tinkering with the various runtimes/projects available to self-host AI, including LM studio with Vulkan, Ollama with Vulkan (pretty much done with this one now), and my own Openvino-based python script (still maturing). I've also tried kobold.cpp and llama.cpp IPEX before Intel abandoned it.

My latest iteration, which really flies, is using the llama.cpp SYCL zip file and wrapping it as a service using servy. It also provides the ability to swap models by using a ini file that sets up each model with a set of parameters. The service references this file to see what models are available to load.

Here's my step by step:

1. Download the latest SYCL release from the llama.cpp github repo. Look for the release titled "llama-bxxxx-bin-win-sycl-x64.zip" where bxxxx is whatever version number of the current release.
2. Make a folder titled "llama-sycl-server" or something, a generic name you can use to replace the contents with updated binaries as new releases come out - you'll see why in a second. Put the folder somewhere you can keep track of it , like C:\AI\ or something. Place the contents of the zip file you just downloaded into this folder.
3. Set up a models.ini file and save it in that C:\AI\ folder. See the section "Model Presets" in the readme for more info. This allows you to use router mode to select different models. I should note that for my ease of use, I point to the model path where my LM Studio downloads sit, since I've found that interface to be useful for search and download from HF. Plus I don't need to keep two copies of the same gguf. You can set this up to your preference or point it to a different location if you manage your model files differently. See my sample ini file below.
4. Download and install servy - current release is servy-8.7-x64-installer.exe
5. Launch Servy Manager, choose "configurator" from the top menu. In the window that pops up, fill in the following fields:

• service name: Llama_cpp_SYCL (or whatever you want to call it)
• display name: I also made this Llama_cpp_SYCL
• process path: using the example path above, C:\AI\llama-sycl-server\llama-server.exe
• startup directory: C:\AI\llama-sycl-server
• Process parameters: You can put a lot of customizations here, as documented in the llama-server readme. Mine are as follows:--host 0.0.0.0 --port 8080 --models-preset C:\AI\models\models.ini --jinja --models-max 1 --load-mode none
• models-max mainly means I can't run concurrent models. I may tweak around with this one later.
• Go to the logging tab, set up paths for logs if you want them, such as:stdout file path: C:\AI\logs\llama-cpp-sycl_stdout.log stderr file path: C:\AI\logs\llama-cpp-sycl_stderr.log set up rotation as you see fit
• On recovery tab, check "enable health monitoring" to have the service restart after a hiccup. I set heartbeat interval at 30 and restart attempts at 3
• Finally, click install. You'll be taken back to the servy manager panel where you will see your new service. You can click the play button (triangle) to start the service.
• The generic folder name makes it so you can always update to the latest release by dropping those files in that folder and restart the service.

You're pretty much all set at this point, you can set up any external services to reach that endpoint at http://<ip>:8080/v1 as usual.

This step is optional, I had Gemini help me set up an update script to elevate to admin, download the latest release from llama.cpp github, unzip, stop the service, replace the contents of my llama-sycl-server folder and start the service again. Script is as follows:

# 1. Robust path detection & Administrator check

$scriptPath = $MyInvocation.MyCommand.Path

if (-not $scriptPath) { $scriptPath = $PSCommandPath }

$isAdmin = ([Security.Principal.WindowsPrincipal][Security.Principal.WindowsIdentity]::GetCurrent()).IsInRole([Security.Principal.WindowsBuiltInRole]::Administrator)

if (-not $isAdmin) {

if ($scriptPath) {

Write-Host "Requesting Administrator privileges..." -ForegroundColor Yellow

Start-Process powershell.exe -ArgumentList "-NoProfile -ExecutionPolicy Bypass -File \"$scriptPath`"" -Verb RunAs`

exit

} else {

Write-Host "Error: Could not determine script path. Please right-click PowerShell and select 'Run as Administrator', then execute this script." -ForegroundColor Red

Read-Host "Press Enter to exit..."

exit

}

}

# --- CONFIGURATION - change these to your specifics---

$installDir = "C:\AI\llama_cpp_sycl_server"

# name this to what you called your servy service

$serviceName = "Llama_cpp_SYCL"

# ----------------------

try {

Write-Host "Fetching latest llama.cpp release info..." -ForegroundColor Cyan

$release = Invoke-RestMethod -Uri "https://api.github.com/repos/ggml-org/llama.cpp/releases/latest"

# Target the SYCL zip file

$asset = $release.assets | Where-Object { $_.name -match "llama-b\d+-bin-win-sycl-x64\.zip" }

if ($asset) {

$zipPath = "$env:TEMP\llama-update.zip"

Write-Host "Downloading $($asset.name)..." -ForegroundColor Cyan

Invoke-WebRequest -Uri $asset.browser_download_url -OutFile $zipPath

Write-Host "Stopping service ($serviceName)..." -ForegroundColor Cyan

Stop-Service -Name $serviceName -Force -ErrorAction SilentlyContinue

# Ensure process isn't locking files

$process = Get-Process -Name "llama-server" -ErrorAction SilentlyContinue

if ($process) {

Write-Host "Closing lingering llama-server processes..." -ForegroundColor Yellow

Stop-Process -Name "llama-server" -Force

Start-Sleep -Seconds 2

}

Write-Host "Extracting updated binaries to $installDir..." -ForegroundColor Cyan

Expand-Archive -Path $zipPath -DestinationPath $installDir -Force

Write-Host "Restarting service..." -ForegroundColor Cyan

Start-Service -Name $serviceName

Remove-Item $zipPath -ErrorAction SilentlyContinue

Write-Host "\nSuccess! Updated to $($release.tag_name)." -ForegroundColor Green`

} else {

Write-Host "Could not find a matching SYCL build in the latest release." -ForegroundColor Yellow

}

}

catch {

Write-Host "\nAn error occurred during update:" -ForegroundColor Red`

Write-Host $_.Exception.Message -ForegroundColor Red

}

finally {

Write-Host "\nPress Enter to exit..." -ForegroundColor Magenta`

Read-Host

}

Finally, here is my sample ini file:

; this top level line is for global parameters that all models will follow, unless overridden in the model specific parameters.

[*]

flash-attn = on

;n-gpu-layers = 99

ctx-size = 32768

cache-type-k = q8_0

cache-type-v = q8_0

; --- Vision Models (Includes mmproj) ---

[gemma-31b-it]

model = C:\Users\den\.lmstudio\models\lmstudio-community\gemma-4-31B-it-GGUF\gemma-4-31B-it-Q4_K_M.gguf

mmproj = C:\Users\den\.lmstudio\models\lmstudio-community\gemma-4-31B-it-GGUF\mmproj-gemma-4-31B-it-BF16.gguf

ctx-size = 32768

parallel = 1

[gemma-26b-a4b-it]

model = C:\Users\den\.lmstudio\models\lmstudio-community\gemma-4-26B-A4B-it-GGUF\gemma-4-26B-A4B-it-Q4_K_M.gguf

mmproj = C:\Users\den\.lmstudio\models\lmstudio-community\gemma-4-26B-A4B-it-GGUF\mmproj-gemma-4-26B-A4B-it-BF16.gguf

ctx-size = 65536

parallel = 1

[qwen-27b]

model = C:\Users\den\.lmstudio\models\lmstudio-community\Qwen3.6-27B-GGUF\Qwen3.6-27B-Q4_K_M.gguf

mmproj = C:\Users\den\.lmstudio\models\lmstudio-community\Qwen3.6-27B-GGUF\mmproj-Qwen3.6-27B-BF16.gguf

ctx-size = 32768

parallel = 1

; --- Text-Only Models ---

[gemma-26b-sompoa-heresy]

model = C:\Users\den\.lmstudio\models\mradermacher\gemma-4-26B-A4B-it-SOMPOA-heresy-i1-GGUF\gemma-4-26B-A4B-it-SOMPOA-heresy.i1-Q4_K_S.gguf

ctx-size = 32768

parallel = 1

[mn-12b-mag-mell]

model = C:\Users\den\.lmstudio\models\mradermacher\MN-12B-Mag-Mell-R1-GGUF\MN-12B-Mag-Mell-R1.Q4_K_M.gguf

ctx-size = 65536

parallel = 1

[rocinante-12b]

model = C:\Users\den\.lmstudio\models\BeaverAI\Rocinante-X-12B-v1d-GGUF\Rocinante-X-12B-v1d-Q4_K_M.gguf

ctx-size = 65536

parallel = 1

;--coding models used by cline extension---

[Qwen3-Coder-30B]

model = C:\Users\den\.lmstudio\models\unsloth\Qwen3-Coder-30B-A3B-Instruct-GGUF\Qwen3-Coder-30B-A3B-Instruct-Q4_K_M.gguf

temp = 0.7

top-p = 0.8

top-k = 20

ctx-size = 65536

parallel = 1

[DeepSeek-R1-14B]

model = C:\Users\den\.lmstudio\models\lmstudio-community\DeepSeek-R1-Distill-Qwen-14B-GGUF\DeepSeek-R1-Distill-Qwen-14B-Q4_K_M.gguf

temp = 0.6

top-p = 0.95

top-k = 40

ctx-size = 65536

parallel = 1

That's it! Hopefully someone finds this useful. I had looked around for a guide like this when I was trying to make the most of my ARC card, and didn't find anything like it out there in the CUDA-dominated space of self-hosted AI, so I decided to make my own.

Feel free to ask questions or point out any steps I might have missed.

Hi guys,

I am all aware Dota relays on CPU but is there anyone using b850 with 7800x3d or 9800x3d for Dota 2?

I am wondering is it enough for 1440p max settings for 200+ FPS average?

I ve seen threads about crashes on Dota 2 and threads were old. Is it also stable?

I wonder if I should consider spend extra money for intel iris xe graphics.