這與**低記憶體設置**截然相反，如果你想進一步提高 ASF 性能（就CPU速度而言），可能會增加記憶體使用的潛在成本，請遵循這些提示。

當涉及到常規優化時，ASF 始終以性能為先，因此在進一步提高性能方面，您可做的事情並不多，儘管您並非別無選擇。 但是，請記住，預設情況下不會啟用這些選項，這意味著它們不足以在大多數用法中保證它們的平衡，因此您應該自己決定是否可以接受啟用它們帶來的記憶體增加。

Below tricks involve serious memory increase and should therefore be used with caution.

The recommended way of applying those settings is through DOTNET_ environment properties. Of course, you could also use other methods, e.g. runtimeconfig.json, but some settings are impossible to be set this way, and on top of that ASF will replace your custom runtimeconfig.json with its own on the next update, therefore we recommend environment properties that you can set easily prior to launching the process.

.NET runtime allows you to tweak garbage collector in a lot of ways, effectively fine-tuning the GC process according to your needs. We've documented below properties that are especially important in our opinion.

Configures whether the application uses workstation garbage collection or server garbage collection.

You can read the exact specific of the server GC at fundamentals of garbage collection.

ASF is using workstation garbage collection by default. This is mainly because of a good balance between memory usage and performance, which is more than enough for just a few bots, as usually a single concurrent background GC thread is fast enough to handle entire memory allocated by ASF.

However, today we have a lot of CPU cores that ASF can greatly benefit from, by having a dedicated GC thread per each CPU vCore that is available. This can greatly improve the performance during heavy ASF tasks such as parsing badge pages or the inventory, since every CPU vCore can help, as opposed to just 2 (main and GC). Server GC is recommended for machines with 3 CPU vCores and more, workstation GC is automatically forced if your machine has just 1 CPU vCore, and if you have exactly 2 then you can consider trying both (results may vary).

Server GC itself does not result in a very huge memory increase by just being active, but it has much bigger generation sizes, and therefore is far more lazy when it comes to giving memory back to OS. You may find yourself in a sweet spot where server GC increases performance significantly and you'd like to keep using it, but at the same time you can't afford that huge memory increase that comes out of using it. Luckily for you, there is a "best of both worlds" setting, by using server GC with GCLatencyLevel configuration property set to 0, which will still enable server GC, but limit generation sizes and focus more on memory. Alternatively, you might also experiment with another property, GCHeapHardLimitPercent, or even both of them at the same time.

However, if memory is not a problem for you (as GC still takes into account your available memory and tweaks itself), it's a much better idea to not change those properties at all, achieving superior performance in result.

You can enable selected properties by setting appropriate environment variables. For example, on Linux (shell):

export DOTNET_gcServer=1

./ArchiSteamFarm # For OS-specific build
./ArchiSteamFarm.sh # For generic build

Or on Windows (powershell):

$Env:DOTNET_gcServer=1

.\ArchiSteamFarm.exe # For OS-specific build
.\ArchiSteamFarm.cmd # For generic build

• Ensure that you're using default value of OptimizationMode which is MaxPerformance. This is by far the most important setting, as using MinMemoryUsage value has dramatic effects on performance.
• Enable server GC. Server GC can be immediately seen as being active by significant memory increase compared to workstation GC. This will spawn a GC thread for every CPU thread your machine has in order to perform GC operations in parallel with maximum speed.
• If you can't afford memory increase due to server GC, consider tweaking GCLatencyLevel and/or GCHeapHardLimitPercent to achieve "the best of both worlds". However, if your memory can afford it, then it's better to keep it at default - server GC already tweaks itself during runtime and is smart enough to use less memory when your OS will truly need it.

Applying recommendations above allows you to have superior ASF performance that should be blazing fast even with hundreds or thousands of enabled bots. CPU should not be a bottleneck anymore, as ASF is able to use your entire CPU power when needed, cutting required time to bare minimum. The next step would be CPU and RAM upgrades, or splitting workload across several servers and ASF instances.