diff --git a/docs/my-website/docs/benchmarks.md b/docs/my-website/docs/benchmarks.md
index 43ab82b8e6..be175a9212 100644
--- a/docs/my-website/docs/benchmarks.md
+++ b/docs/my-website/docs/benchmarks.md
@@ -16,19 +16,17 @@ model_list:
       api_key: "test"
 ```
 
-### 1 Instance LiteLLM Proxy
+### 2 Instance LiteLLM Proxy
 
 In these tests the baseline latency characteristics are measured against a fake-openai-endpoint.
 
 #### Performance Metrics
 
-| Metric | Value |
-|--------|-------|
-| **Requests per Second (RPS)** | 475 |
-| **End-to-End Latency P50 (ms)** | 100 |
-| **LiteLLM Overhead P50 (ms)** | 3 |
-| **LiteLLM Overhead P90 (ms)** | 17 |
-| **LiteLLM Overhead P99 (ms)** | 31 |
+| **Type** | **Name** | **Median (ms)** | **95%ile (ms)** | **99%ile (ms)** | **Average (ms)** | **Current RPS** |
+| --- | --- | --- | --- | --- | --- | --- |
+| POST | /chat/completions | 200 | 630 | 1200 | 262.46 | 1035.7 |
+| Custom | LiteLLM Overhead Duration (ms) | 12 | 29 | 43 | 14.74 | 1035.7 |
+|  | Aggregated | 100 | 430 | 930 | 138.6 | 2071.4 |
 
 <!-- <Image img={require('../img/1_instance_proxy.png')} /> -->
 
@@ -36,28 +34,32 @@ In these tests the baseline latency characteristics are measured against a fake-
 
 <Image img={require('../img/instances_vs_rps.png')} /> -->
 
+
+### 4 Instances
+
+| **Type** | **Name** | **Median (ms)** | **95%ile (ms)** | **99%ile (ms)** | **Average (ms)** | **Current RPS** |
+| --- | --- | --- | --- | --- | --- | --- |
+| POST | /chat/completions | 100 | 150 | 240 | 111.73 | 1170 |
+| Custom | LiteLLM Overhead Duration (ms) | 2 | 8 | 13 | 3.32 | 1170 |
+|  | Aggregated | 77 | 130 | 180 | 57.53 | 2340 |
+
 #### Key Findings
-- Single instance: 475 RPS @ 100ms median latency
-- LiteLLM adds 3ms P50 overhead, 17ms P90 overhead, 31ms P99 overhead
-- 2 LiteLLM instances: 950 RPS @ 100ms latency
-- 4 LiteLLM instances: 1900 RPS @ 100ms latency
-
-### 2 Instances
-
-**Adding 1 instance, will double the RPS and maintain the `100ms-110ms` median latency.**
-
-| Metric | Litellm Proxy (2 Instances) |
-|--------|------------------------|
-| Median Latency (ms) | 100 |
-| RPS | 950 |
-
+- Doubling from 2 to 4 LiteLLM instances cuts median latency from 200 ms to 100 ms.
+- High-percentile latencies drop significantly: P95 from 430 ms to 130 ms, P99 from 930 ms to 180 ms.
+- Setting workers equal to CPU count gives optimal performance.
 
 ## Machine Spec used for testing
 
 Each machine deploying LiteLLM had the following specs:
 
-- 2 CPU
-- 4GB RAM
+- 4 CPU
+- 8GB RAM
+
+
+## Locust Settings
+
+- 1000 Users
+- 500 user Ramp Up
 
 ## How to measure LiteLLM Overhead
 
@@ -137,10 +139,3 @@ Using LangSmith has **no impact on latency, RPS compared to Basic Litellm Proxy*
 |--------|------------------------|---------------------|
 | RPS | 1133.2 | 1135 |
 | Median Latency (ms) | 140 | 132 |
-
-
-
-## Locust Settings
-
-- 2500 Users
-- 100 user Ramp Up