Functionality Benchmark — 3-Way Comparison

The Dell AI Innovation Hub provides organizations with the expertise and infrastructure needed to accelerate the development and deployment of enterprise AI solutions.

Our Dell AI Innovation Hub helps organizations accelerate their AI journey by providing access to expertise, technology, and resources to develop and deploy AI solutions.

The Dell AI Innovation Hub enables customers and partners to explore, test, and deploy AI solutions using Dell’s hardware, software, and expertise in a collaborative environment.

Welcome to the lab! Please take a seat and let me know if you have an appointment or need assistance.

Welcome to the lab! It's lovely to have you here, please let me know who you're here to see.

Welcome to the lab! How can I assist you today?

Kecerdasan buatan adalah simulasi proses kecerdasan manusia oleh sistem komputer untuk melaksanakan tugas yang memerlukan pemikiran manusia.

Kecerdasan buatan ialah simulasi proses kecerdasan manusia oleh komputer.

Kecerdasan buatan ialah kemampuan mesin atau sistem komputer untuk melakukan tugas yang biasanya memerlukan kecerdasan manusia seperti berfikir, belajar, dan membuat keputusan.

Based on the screenshot provided, this page is the **AI Innovation Hub** dashboard (specifically for the Cyberjaya, Malaysia location). It serves as a landing page to showcase various AI-powered projects and infrastructure initiatives.

The main project tiles visible are:

*   **Coolest Path** (Status: Deployed)
*   **Network EMU** (Status: Active)
*   **IntelliSalesWidget** (Status: Edge / NPU)
*   **More projects coming** (Placeholder tile)

Here's an analysis of the lab dashboard screenshot:

**What is this page?**

This appears to be a dashboard for the "AI Innovation Hub" at Cyberjaya, Malaysia. It's focused on building AI at the edge and beyond, with a focus on applications and platform infrastructure. The page seems to be designed to showcase ongoing projects and provide a central hub for managing and monitoring them.

**Main Project Tiles:**

Here are the main project tiles visible on the screenshot:

*   **Coolest Path:** A project focused on geospatial ML, network ops, and on-device AI, specifically for route optimization using satellite data.
*   **Network EMU:** A project dealing with end-to-end network simulation, LLM incident summary, and CMDB enrichment.
*   **IntelliSalesWidget:** A project related to Edge/NPU.
*   **More projects coming:** Indicates that there are additional projects in development or planned.



Let me know if you'd like me to elaborate on any specific aspect of the screenshot!

This page is the **homepage of the AI Innovation Hub in Cyberjaya, Malaysia**, specifically showcasing its **AI-powered projects and platform infrastructure** focused on edge computing and beyond.

It serves as a **lab dashboard or project showcase** for AI initiatives developed at the hub, highlighting real-world applications in areas like geospatial AI, network operations, and on-device AI.

---

### Main Project Tiles Visible:

1. **Coolest Path**
   - **Status**: Deployed
   - **Tags**: Geospatial, XGBoost, K3s
   - **Description**: An AI-powered walking route optimizer for George Town, Penang. It ranks routes using satellite LST (Land Surface Temperature), shade analysis, and XGBoost GPU inference.

2. **Network EMU**
   - **Status**: Active
   - **Tags**: NetOps, LLM, Docker
   - **Description**: End-to-end network failure simulation. Integrates Zabbix alerts → CMDB enrichment → LLM incident summary → live NOC dashboard.

3. **IntelliSalesWidget**
   - **Status**: EDGE / NPU (likely indicating edge deployment with NPU acceleration)
   - **Description**: Not fully visible, but the tag suggests it’s an AI-powered sales tool optimized for edge or on-device deployment.

4. **More projects coming**
   - Placeholder tile indicating future projects are in development.

---

This dashboard emphasizes **AI at the edge**, combining **cloud, edge, and on-device AI** with technologie

Based on the monitoring dashboard provided, here is the report:

*   **CPU Utilization:** 67%
*   **Memory Usage:** 82.4 GB / 119.6 GB
*   **GPU Memory:** 85.0 GB / 119.6 GB
*   **Active Pods:** 47
*   **Pod using the most GPU memory:** vllm-qwen3vl-3 (55 GB)

Okay, here's a breakdown of the monitoring dashboard data you provided:

**1. CPU Utilization:**

*   **Overall:** 67%
*   **By Node:** The graph shows CPU utilization across different nodes. The node "mailnet602-hub" has the highest CPU utilization at 12.3%.

**2. Memory Usage:**

*   **Overall:** 82.4 GB / 119.6 GB (approximately 69% utilized)
*   **By Pod:** The pod "vlm-gemm2-9b-32b-" uses the most memory at 55.2 GB.

**3. GPU Memory:**

*   **Overall:** 85.0 GB / 119.6 GB (approximately 71% utilized)
*   **By Pod:** The pod "vlm-gemm2-9b" uses the most GPU memory at 55 GB.

**4. Active Pods:**

*   **Total:** 47

**5. Pod Using the Most GPU Memory:**

*   **vlm-gemm2-9b** - Utilizes 55 GB of GPU memory.



Let me know if you would like me to analyze any other aspect of the dashboard!

**Monitoring Dashboard Report**

**Overview:**
The dashboard provides a snapshot of compute resource utilization across the Dell AI Lab Kubernetes cluster over the last 6 hours, with a refresh interval of 10 seconds.

---

**Key Metrics:**

- **CPU Utilization:** 67%  
  The cluster is operating at a high CPU load, indicating significant computational activity.

- **Memory Usage:** 82.4 GB / 119.6 GB (≈68.9%)  
  Memory usage is moderately high, with approximately 37.2 GB of RAM remaining available.

- **GPU Memory Usage:** 85.0 GB / 119.6 GB (≈71.0%)  
  GPU memory is heavily utilized, with 34.6 GB still available. This suggests GPU-intensive workloads are active.

- **Active Pods:** 47  
  There are currently 47 pods running in the cluster, indicating a moderately sized deployment.

---

**GPU Memory Breakdown by Pod (Bar Chart):**

The GPU memory usage is dominated by a few pods:

- **vlm-qwen3vl-3:** 55 GB (highest usage)
- **vlm-gemma2-9b:** 24 GB
- **vlm-bench:** 13 GB
- **vlm-llava-7b:** 8 GB

✅ **Pod using the most GPU memory:** **vlm-qwen3vl-3** (55 GB)

---

**CPU Usage by Node (Line Graph):**

- **mailg02 (9):** Higher CPU usage (blue line), indicating this node is handling more workloads.
- **mailt03vl (7):** Lower CPU usage (green line), suggesting it is less loaded.

---

**Pod Health Status (Table):**

| Namespace         | Pod                     | CPU   | Memor

Based on the dashboard provided, the most unusual and problematic metric is the **GPU Memory usage**.

### The Problematic Metric: GPU Memory
While the CPU and Memory usage metrics appear to be within normal operating ranges (91% and ~70% respectively), the **GPU Memory** metric shows a critical state.

### Why it is anomalous:
1.  **High Utilization/Saturation:** The top-level summary shows **85.0 GB / 119.6 GB** used. While this doesn't look like 100% at a glance, the bar chart below it reveals a massive imbalance.
2.  **Extreme Resource Imbalance:** The bar chart shows that a single process/model (**vllm-qwen3vl-3**) is consuming **55 GB** of GPU memory. This single entity is consuming nearly half of the entire cluster's available GPU memory.
3.  **Potential for "Out of Memory" (OOM) Errors:** In Kubernetes environments, having one massive consumer can lead to scheduling issues. If another pod attempts to scale or a new model is deployed, there is very little "headroom" left.
4.  **Discrepancy with Pod Health Status:** If you look at the "Pod Health Status" table at the bottom, the pod `vllm-qwen3vl-32b...` is listed as using **55.2 GB** of memory, but its CPU usage is only **8.1%**. This indicates a highly memory-intensive workload that is likely bottlenecked by VRAM rather than compute power, which is a common point of failure in LLM

Okay, let's analyze this dashboard and identify the unusual/anomalous metric.

**The Problematic Metric: GPU Memory Usage**

Here's why GPU memory usage is the most concerning:

*   **High Overall Usage:** The overall GPU Memory Usage is at 85.0 GB / 118.6 GB, which is approximately 72%. This is a very high utilization rate.  While not necessarily a *critical* problem immediately, it indicates that the cluster is heavily utilizing its GPU resources.
*   **Significant Spike:** The graph of GPU Memory Usage shows a very sharp spike in usage. This is a clear anomaly. The usage jumps dramatically, suggesting a sudden and substantial demand for GPU resources. This could be due to a specific workload suddenly becoming more intensive, a new process starting, or a memory leak.
*   **Pod-Level Breakdown:** Looking at the Pod Health Status, the `vlm-gemma2-9b` pod is consuming 240 GB of GPU memory. This is far more than any other pod and is significantly higher than the total available GPU memory. This is a clear indication of a problem. It's likely this pod is either misconfigured, experiencing a memory leak, or is attempting to allocate more memory than is available.

**Other Observations (Less Critical):**

*   **CPU Utilization:**  CPU utilization is at 81%, which is also high, but not as immediately alarming as the GPU memory situation. It could be related to the GPU 

The most **anomalous and problematic metric** in this dashboard is the **GPU Memory usage**.

---

### 🚨 Problematic Metric: **GPU Memory Usage (85.0 GB / 119.6 GB)**

---

### ❗ Why it’s unusual:

1. **Extreme Discrepancy with CPU Usage**:
   - CPU Utilization is only **9%**, which suggests the cluster is not under heavy computational load.
   - However, GPU Memory is **over 70% utilized (85.0 GB / 119.6 GB)** — this is **highly unusual** for a system with such low CPU usage.
   - GPUs are typically used for intensive parallel workloads (e.g., AI/ML training or inference), which usually correlate with high CPU usage as well. The fact that GPU memory is maxed out while CPU is idle suggests **a misconfiguration or inefficient workload**.

2. **GPU Memory Usage vs. Pod Memory Usage**:
   - The **Pod Health Status** table shows that the pod `vlm-qwen3vl-32b...` is using **55.2 GB of memory**, which is **much less than the 85 GB of GPU memory** being consumed.
   - This implies that **GPU memory is being used far beyond what the pod’s memory footprint suggests**, which is **not typical** — unless this is a large model inference job (e.g., a 32B parameter model), but even then, 85 GB GPU memory for a 55 GB RAM pod is **extremely high** and likely **inefficient or misallocated**.

3. **GPU Memory Bar Chart**:
   - The bar chart shows that the `vlm-qwen3vl-3` pod is consuming **65 GB 

Based on the image, this is a **data center** or a **server room**.

The visible equipment and infrastructure include:

*   **Server Racks/Cabinets:** The large, dark vertical structures that house the hardware.
*   **Servers:** The individual computing units mounted within the racks.
*   **Network Cabling:** The dense, colorful bundles of wires (likely Ethernet or fiber optic cables) connecting the different pieces of hardware.
*   **Status Indicator Lights (LEDs):** The numerous small green and amber lights visible on the front of the equipment, which indicate power status, network activity, and hardware health.
*   **Rack Mounting Hardware:** The metal frames and rails used to secure the equipment in place.

Based on the image, this is a **data center**. Here's a breakdown of the equipment and infrastructure visible:

**Key Identifiers:**

*   **Server Racks:** The most prominent feature is the rows of tall, black, metal enclosures. These are server racks, designed to house and organize servers and networking equipment.
*   **LED Indicators:** The numerous blinking lights (green, yellow/orange, and blue) are LED indicators on the servers and network devices within the racks. These lights signal the status of the equipment - whether it's powered on, operating normally, experiencing errors, etc.
*   **Cabling:** The dense network of cables (likely Ethernet, fiber optic, and power cables) running between the racks is a hallmark of a data center. This cabling connects the servers to each other and to the external network.

**Other Visible Infrastructure:**

*   **Doors with Handles:** The racks have doors with handles, providing physical security and access control.
*   **Rack Mounts:**  You can see the vertical rails inside the racks where servers and other equipment are mounted.

**What this suggests about the data center:**

*   **High Density:** The number of racks and the density of cabling suggest a high-density data center, meaning a lot of computing power is packed into a relatively small space.
*   **Active Operation:** The blinking lights indicate that the data cent

This image depicts a **data center** or **server room**, a facility designed to house and manage computing infrastructure for organizations or cloud services.

---

### **Identified Equipment and Infrastructure:**

1. **Server Racks (Enclosures):**
   - Tall, black, metal cabinets with perforated mesh doors.
   - These are standard 19-inch server racks used to mount and organize servers, switches, and other networking equipment.
   - The mesh doors allow for airflow and cooling while providing security.

2. **Servers:**
   - Multiple rack-mounted servers are visible inside the cabinets.
   - They are arranged vertically in rows, typical of data center deployments.
   - The green lights indicate that the servers are powered on and operational.

3. **Cabling:**
   - A dense network of cables is visible, including:
     - **Yellow/orange cables** – likely **fiber optic cables** (used for high-speed data transmission).
     - **Blue/teal cables** – likely **Ethernet (Cat 5e/6/6a) cables** for network connectivity.
   - The cables are routed through the front of the racks, connecting servers to switches or other network devices.
   - Some cables are bundled or loosely coiled, suggesting either active use or ongoing maintenance.

4. **Network Switches or Patch Panels:**
   - While not clearly visible, the dense cabling suggests the presence of switches or patch panels at the top or b

Based on the two screenshots provided, there is only one specific change:

*   **CPU Utilization:** The value increased from **67%** in the first screenshot to **91%** in the second screenshot.

All other values, including Memory Usage (82.4 GB / 119.6 GB), GPU Memory (85.0 GB / 119.6 GB), Active Pods (47), the bar chart values, and the Pod Health Status table, remain identical.

Here's a breakdown of the changes between the two screenshots of the dashboard:

**1. CPU Utilization:**

*   **Screenshot 1:** 67%
*   **Screenshot 2:** 81%
    *   A significant increase in CPU utilization.

**2. GPU Memory Usage:**

*   **Screenshot 1:** 55 GB / 118.6 GB
*   **Screenshot 2:** 55 GB / 118.6 GB
    *   No change in GPU memory usage.

**3. Pod Health Status - vlm-gemm2-9b:**

*   **Screenshot 1:** 240 GB
*   **Screenshot 2:** 240 GB
    *   No change in memory usage.

**4. Pod Health Status - vlm-qwen3v4-32b:**

*   **Screenshot 1:** 8.1%
*   **Screenshot 2:** 8.1%
    *   No change in CPU usage.

**5. Pod Health Status - vlm-have7b:**

*   **Screenshot 1:** 13 GB
*   **Screenshot 2:** 13 GB
    *   No change in memory usage.

**6. Pod Health Status - vlm-bench:**

*   **Screenshot 1:** 8 GB
*   **Screenshot 2:** 8 GB
    *   No change in memory usage.

**Overall:**

The most notable change is the increase in CPU utilization from 67% to 81%. All other values appear to be unchanged.

The only change between the two screenshots is in the **CPU Utilization** metric and the **color of the GPU Memory bar** for the `vlm-qwen3vl-3` pod.

Here are the specific changes:

1. **CPU Utilization**:
   - **First screenshot**: 67%
   - **Second screenshot**: 91%
   - **Change**: Increased by 24 percentage points.

2. **GPU Memory Bar Color**:
   - **First screenshot**: The bar for `vlm-qwen3vl-3` (55 GB) is **orange**.
   - **Second screenshot**: The same bar is now **red**.
   - **Interpretation**: This likely indicates a threshold crossing (e.g., high usage or warning state), although the actual GPU memory value (55 GB) remains unchanged.

All other values are identical:
- Memory Usage: 82.4 GB / 119.6 GB (unchanged)
- GPU Memory: 85.0 GB / 119.6 GB (unchanged)
- Active Pods: 47 (unchanged)
- CPU Usage by Node graph: Identical lines and patterns
- Pod Health Status table: All CPU, Memory, and Status values are unchanged

✅ **Conclusion**: The only changes are the **CPU Utilization increasing from 67% to 91%** and the **GPU Memory bar for `vlm-qwen3vl-3` changing from orange to red**, likely indicating a performance or resource alert.

```mermaid
graph TD
    User((User)) -->|HTTPS| LibreChat[LibreChat<br/>chat.lab.local]
    LibreChat -->|API Request| LiteLLM[LiteLLM Proxy]
    
    subgraph vLLM_Backends [vLLM Backends]
        LiteLLM -->|Text Routing| Gemma[Gemma-3-12B]
        LiteLLM -->|Vision Routing| Qwen[Qwen3-VL-32B]
    end
```

```mermaid
graph LR
    A[User] --> B(LibreChat at chat.lab.local);
    B --> C(LiteLLM Proxy);
    C --> D{Gemma-3-12B (Text)};
    C --> E{Qwen3-VL-32B (Vision)};
```

```mermaid
graph TD
    A[User] --> B[LibreChat<br>chat.lab.local]
    B --> C[LiteLLM Proxy]
    C --> D[vLLM Backend<br>Gemma-3-12B<br>(Text)]
    C --> E[vLLM Backend<br>Qwen3-VL-32B<br>(Vision)]
```

```python
import matplotlib.pyplot as plt

models = ['Qwen3-VL-32B', 'Gemma-3-12B', 'Qwen2.5-VL-7B']
speeds = [6.5, 16, 27]
colors = ['#5794F2', '#73BF69', '#FF9830']

plt.figure(figsize=(10, 6))
plt.barh(models, speeds, color=colors)

plt.title('Vision Model Speed on GB10')
plt.xlabel('Tokens per second')
plt.gca().invert_yaxis()  # To show the first model at the top

plt.show()
```

```python
import matplotlib.pyplot as plt

models = ['Qwen3-VL-32B', 'Gemma-3-12B', 'Qwen2.5-VL-7B']
speeds = [6.5, 16, 27]
colors = ['#5794F2', '#73BF69', '#FF9830']

plt.figure(figsize=(10, 6))
plt.barh(models, speeds, color=colors)
plt.title("Vision Model Speed on GB10")
plt.xlabel("Tokens per second")
plt.show()
```

import matplotlib.pyplot as plt

models = ["Qwen3-VL-32B", "Gemma-3-12B", "Qwen2.5-VL-7B"]
speeds = [6.5, 16, 27]
colors = ["#5794F2", "#73BF69", "#FF9830"]

plt.figure(figsize=(8, 5))
plt.barh(models, speeds, color=colors)
plt.title("Vision Model Speed on GB10")
plt.xlabel("Tokens per second")
plt.tight_layout()
plt.show()

<svg width="400" height="200" xmlns="http://www.w3.org/2000/svg">
  <defs>
    <marker id="arrowhead" markerWidth="10" markerHeight="7" refX="0" refY="3.5" orient="auto">
      <polygon points="0 0, 10 3.5, 0 7" fill="black" />
    </marker>
  </defs>
  
  <!-- STT Box -->
  <rect x="20" y="75" width="80" height="50" fill="white" stroke="#0076CE" stroke-width="2" />
  <text x="60" y="105" font-family="Arial" font-size="14" text-anchor="middle" fill="black">STT</text>
  
  <!-- Arrow 1 -->
  <line x1="100" y1="100" x2="140" y2="100" stroke="black" stroke-width="2" marker-end="url(#arrowhead)" />
  
  <!-- LLM Box -->
  <rect x="150" y="75" width="100" height="50" fill="white" stroke="#0076CE" stroke-width="2" />
  <text x="200" y="105" font-family="Arial" font-size="14" text-anchor="middle" fill="black">LLM</text>
  
  <!-- Arrow 2 -->
  <line 

```svg
<svg width="400" height="200" xmlns="http://www.w3.org/2000/svg">
  <rect x="20" y="40" width="120" height="80" fill="white" stroke="#0076CE" stroke-width="2"/>
  <text x="60" y="85" font-size="20" font-family="sans-serif" fill="black">STT</text>
  <rect x="160" y="40" width="120" height="80" fill="white" stroke="#0076CE" stroke-width="2"/>
  <text x="200" y="85" font-size="20" font-family="sans-serif" fill="black">LLM</text>
  <rect x="300" y="40" width="120" height="80" fill="white" stroke="#0076CE" stroke-width="2"/>
  <text x="340" y="85" font-size="20" font-family="sans-serif" fill="black">TTS</text>
  <path d="M130 80 L160 80" stroke="#0076CE" stroke-width="2"/>
  <path d="M230 80 L260 80" stroke="#0076CE" stroke-width="2"/>
</svg>
```

<svg width="400" height="200" xmlns="http://www.w3.org/2000/svg">
  <rect x="50" y="70" width="80" height="60" fill="white" stroke="#0076CE" stroke-width="2"/>
  <text x="90" y="100" text-anchor="middle" font-family="Arial" font-size="20" fill="black">STT</text>
  <rect x="150" y="70" width="80" height="60" fill="white" stroke="#0076CE" stroke-width="2"/>
  <text x="190" y="100" text-anchor="middle" font-family="Arial" font-size="20" fill="black">LLM</text>
  <rect x="250" y="70" width="80" height="60" fill="white" stroke="#0076CE" stroke-width="2"/>
  <text x="290" y="100" text-anchor="middle" font-family="Arial" font-size="20" fill="black">TTS</text>
  <line x1="130" y1="100" x2="150" y2="100" stroke="#0076CE" stroke-width="2"/>
  <polygon points="150,100 145,95 145,105" fill="#0076CE"/>
  <line x1="230&quo

```python
import matplotlib.pyplot as plt
import numpy as np

# Set dark theme style
plt.style.use('dark_background')

# Create figure and grid layout
fig = plt.figure(figsize=(16, 10), facecolor='#0b0e14')
gs = fig.add_gridspec(4, 4, hspace=0.3, wspace=0.2)

# Helper function for metric cards
def draw_metric_card(ax, title, value, color='#4f8ff7'):
    ax.set_facecolor('#161b22')
    ax.add_patch(plt.Rectangle((0, 0), 1, 1, transform=ax.transAxes, color='#161b22', zorder=-1))
    ax.text(0.05, 0.8, title, transform=ax.transAxes, color='white', fontsize=10, fontweight='bold')
    ax.text(0.05, 0.4, value, transform=ax.transAxes, color=color, fontsize=14, fontweight='bold')
    ax.set_xticks([])
    ax.set_yticks([])
    for spine in ax.spines.values():
        spine.set_edgecolor('#30363d')

# 1. Top Row: Metric Cards
metrics = [
    ("CPU Utilization", "67%", "#4f8ff7"),
    ("Memory Usage", "82.4 GB / 119.6 GB", "#4f8ff7"),
    ("GPU Memory", "85.0 GB / 119.6 GB", "#4f8ff7"),
    ("Active Pods", "47", "#4f8ff7")
]

for i, (title, val, col) in enumerate(metrics):
    ax = fig.add_subplot(gs[0, i])
    draw_metric_card(ax, title, val, col)

# 2. Middle Left: CPU Usage by 

```python
import matplotlib.pyplot as plt
import numpy as np

# --- CPU Utilization ---
plt.figure(figsize=(12, 8))

# Create a simple bar chart for CPU Utilization
cpu_utilization = 67
plt.subplot(2, 2, 1)
plt.bar(['CPU Utilization'], [cpu_utilization], color='skyblue')
plt.ylim(0, 100)
plt.ylabel('Percentage')
plt.title('CPU Utilization')
plt.xticks([])  # Remove x-axis ticks

# --- Memory Usage ---
memory_usage = 82.4
total_memory = 119.6
plt.subplot(2, 2, 2)
plt.bar(['Memory Usage'], [memory_usage], color='skyblue')
plt.ylim(0, total_memory)
plt.ylabel('GB')
plt.title('Memory Usage')
plt.xticks([])

# --- GPU Memory ---
gpu_memory = 85.0
total_gpu_memory = 119.6
plt.subplot(2, 2, 3)
plt.bar(['GPU Memory'], [gpu_memory], color='skyblue')
plt.ylim(0, total_gpu_memory)
plt.ylabel('GB')
plt.title('GPU Memory')
plt.xticks([])

# --- Active Pods ---
active_pods = 47
plt.subplot(2, 2, 4)
plt.bar(['Active Pods'], [active_pods], color='skyblue')
plt.ylim(0, 50)
plt.ylabel('Number of Pods')
plt.title('Active Pods')
plt.xticks([])

plt.tight_layout()
plt.show()

# --- CPU Usage by Node (Line Chart) ---
plt.figure(figsize=(12, 6))
x = np.arange(10)  # Example x-values (nodes)
y = np.random.rand(10) * 10  # Example y-values (CPU 

```python
import matplotlib.pyplot as plt
import numpy as np

# Set dark theme
plt.style.use('dark_background')

# Create figure and grid
fig, ax = plt.subplots(3, 2, figsize=(12, 8), gridspec_kw={'height_ratios': [1, 1, 1], 'width_ratios': [1, 1]})
fig.patch.set_facecolor('#1a1a1a')
fig.suptitle('Dell AI Lab: Kubernetes / Compute Resources / Cluster', fontsize=12, color='white', y=0.98)
fig.text(0.95, 0.98, 'Last 6h □ refresh 10s', fontsize=8, color='gray', ha='right', va='top')

# Top row - Metrics
metrics = [
    ('CPU Utilization', '67%', 'white'),
    ('Memory Usage', '82.4 GB / 119.6 GB', 'lime'),
    ('GPU Memory', '85.0 GB / 119.6 GB', 'orange'),
    ('Active Pods', '47', 'purple')
]

for i, (title, value, color) in enumerate(metrics):
    ax[0, i//2].text(0.5, 0.5, title, ha='center', va='center', fontsize=10, color='white', weight='bold')
    ax[0, i//2].text(0.5, 0.3, value, ha='center', va='center', fontsize=10, color=color, weight='bold')
    ax[0, i//2].axis('off')
    ax[0, i//2].set_xlim(0, 1)
    ax[0, i//2].set_ylim(0, 1)

# Second row - CPU Usage by Node
time_point

You should use a multi-series line chart where each server is represented by a distinct colored line. This type of visualization is ideal because it allows you to easily track temporal trends and compare the unique temperature patterns of all five servers simultaneously over the 24-hour period.

A line chart is ideal for visualizing this time-series data, as it effectively displays trends and fluctuations in GPU temperature over time for each of the five servers. Using separate lines for each server allows for easy comparison of their temperature patterns and identification of any anomalies.

Use a line chart with multiple lines, one for each server, to visualize the temperature trends over time. This allows easy comparison of patterns across servers while clearly showing how each server’s temperature changes throughout the 24-hour period.