Update Blog “qdrant-supports-arm-architecture”

qdrant-landing/content/blog/qdrant-supports-arm-architecture.md

@@ -36,6 +36,6 @@ For the purposes of this test, we created some random vectors which were compare
 
 During our experiments, we performed 1000 search operations for both ARM64 and x86-based setups. We didn’t measure the network overhead, only the time measurements returned by the engine in the API response. The chart below shows the distribution of that time, separately for each architecture.
 
-![](/blog/from_cms/1_zvuef4ri6ztqjzbsocqj_w.webp "In order to make an application available for ARM users, it has to be compiled for that platform. Otherwise, it has to be emulated by the device, which gives an additional overhead and reduces its performance. We decided to provide the Docker images targeted especially at ARM users. Of course, using a limited set of processor instructions may impact the performance of your vector search, and that’s why we decided to test both architectures using a similar setup.  Test environments AWS offers ARM-based EC2 instances that are 20% cheaper than the x86 corresponding alternatives with a similar configuration. That estimate has been done for the eu-central-1 region (Frankfurt) and R6g/R6i instance families. For the purposes of this comparison, we used an r6i.large instance (Intel Xeon) and compared it to r6g.large one (AWS Graviton2). Both setups have 2 vCPUs and 16 GB of memory available and these were the smallest comparable instances available.  The results For the purposes of this test, we created some random vectors which were compared with cosine distance.  Vector search During our experiments, we performed 1000 search operations for both ARM64 and x86-based setups. We didn’t measure the network overhead, only the time measurements returned by the engine in the API response. The chart below shows the distribution of that time, separately for each architecture.")
+![](/blog/from_cms/1_zvuef4ri6ztqjzbsocqj_w.webp "The latency distribution of search requests: arm vs x86")
 
 It seems that ARM64 might be an interesting alternative if you are on a budget. It is 10% slower on average, and 20% slower on the median, but the performance is more consistent. It seems like it won’t be randomly 2 times slower than the average, unlike x86. That makes ARM64 a cost-effective way of setting up vector search with Qdrant, keeping in mind it’s 20% cheaper on AWS. You do get less for less, but surprisingly more than expected.