Refactor documentation for clarity and consistency: update phrasing, section titles, and formatting in setup and build guides for CPU and GPU versions of llama.cpp

Author: madeline-underwood

content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/1a_gb10_setup.md

@@ -86,7 +86,7 @@ Great! If you have seen this message your system is using Armv9 cores, which are
 
 ### Grace CPU specification 
 
-The following table gives you more information about the key specifications of the Grace CPU and explains their relevance to quantized LLM inference:
+The following table provides more information about the key specifications of the Grace CPU and explains their relevance to quantized LLM inference:
 
 | **Category** | **Specification** | **Description / Impact for LLM Inference** |
 |---------------|-------------------|---------------------------------------------|
@@ -99,7 +99,7 @@ The following table gives you more information about the key specifications of t
 | NUMA Topology | Single NUMA node (node0: 0–19) | Simplifies memory access pattern for unified memory workloads |
 | Security and Reliability | Not affected by Meltdown, Spectre, Retbleed, or similar vulnerabilities | Ensures stable and secure operation for long-running inference tasks |
 
-Its SVE2, BF16, and INT8 matrix multiplication (I8MM) capabilities are what make it ideal for quantized LLM workloads, as these provide a power-efficient foundation for both CPU-only inference and CPU–GPU hybrid processing.
+Its SVE2, BF16, and INT8 matrix multiplication (I8MM) capabilities are what make it ideal for quantized LLM workloads, as these provide a power-efficient foundation for both CPU-only inference and CPU-GPU hybrid processing.
 
 You can also verify the operating system running on your DGX Spark by using the following command:
 
@@ -120,7 +120,7 @@ This shows you that your DGX Spark runs on Ubuntu 24.04 LTS, a developer-friendl
 
 Nice work! You've confirmed your operating system is Ubuntu 24.04 LTS, so you can move on to the next step.
 
-## Step 2: Verify the Blackwell GPU and driver
+## Step 2: verify the Blackwell GPU and driver
 
 After confirming your CPU configuration, verify that the Blackwell GPU inside the GB10 Grace Blackwell Superchip is available and ready for CUDA workloads by using the following:
 
@@ -161,7 +161,7 @@ The `nvidia-smi` tool reports GPU hardware specifications and provides valuable
 The table below provides more explanation of the `nvidia-smi` output:
 | **Category** | **Specification (from nvidia-smi)** | **Description / impact for LLM inference** |
 |---------------|--------------------------------------|---------------------------------------------|
-| GPU name | NVIDIA GB10 | Confirms the system recognizes the Blackwell GPU integrated into the Grace–Blackwell Superchip |
+| GPU name | NVIDIA GB10 | Confirms the system recognizes the Blackwell GPU integrated into the Grace Blackwell Superchip |
 | Driver version | 580.95.05 | Indicates that the system is running the latest driver package required for CUDA 13 compatibility |
 | CUDA version | 13.0 | Confirms that the CUDA runtime supports GB10 (sm_121) and is ready for accelerated quantized LLM workloads |
 | Architecture / Compute capability | Blackwell (sm_121) | Supports FP4, FP8, and BF16 Tensor Core operations optimized for LLMs |
@@ -174,7 +174,7 @@ The table below provides more explanation of the `nvidia-smi` output:
 Excellent! Your Blackwell GPU is recognized and ready for CUDA workloads. This means your system is set up for GPU-accelerated LLM inference.
 
 
-## Step 3: Check the CUDA toolkit
+## Step 3: check the CUDA toolkit
 
 To build the CUDA version of llama.cpp, the system must have a CUDA toolkit installed.
 
@@ -210,7 +210,7 @@ At this point, you have verified that:
 
 Your DGX Spark environment is now fully prepared for the next section,  where you will build and configure both CPU and GPU versions of llama.cpp, laying the foundation for running quantized LLMs efficiently on the Grace Blackwell platform.
 
-## What have I achieved?
+## What you have accomplished
 
 In this entire setup section, you have achieved the following:
 

content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/2_gb10_llamacpp_gpu.md

@@ -125,7 +125,7 @@ Together, these options ensure that the build targets the Grace Blackwell GPU wi
 
 ### Step 4: Validate the CUDA-enabled build
 
-After the build completes successfully, verify that the GPU-enabled binary of *llama.cpp is correctly linked to the NVIDIA CUDA runtime.
+After the build completes successfully, verify that the GPU-enabled binary of llama.cpp is correctly linked to the NVIDIA CUDA runtime.
 
 To verify CUDA linkage, run the following command:
 

content/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/3_gb10_llamacpp_cpu.md

@@ -9,7 +9,7 @@ In this section, you'll build and test the CPU-only version of llama.cpp, optimi
 
 The Grace CPU features Arm Cortex-X925 and Cortex-A725 cores with advanced vector extensions including SVE2, BFloat16, and I8MM. These extensions make the CPU highly efficient for quantized inference workloads, even without GPU acceleration.
 
-### Configure and build the CPU-only version
+## Configure and build the CPU-only version
 
 
 In this session, you will configure and build the CPU-only version of llama.cpp, optimized for the Armv9-based Grace CPU.
@@ -83,8 +83,7 @@ The build output is shown below:
 ```
 
 After the build finishes, you'll find the CPU-optimized binaries at `~/llama.cpp/build-cpu/bin/`
-
-### Step 2: Validate the CPU-Enabled Build (CPU Mode)
+## Validate the CPU-enabled build (CPU mode)
 
 First, validate that the binary was compiled in CPU-only mode and runs correctly on the Grace CPU:
 
@@ -116,7 +115,7 @@ Here is an explanation of the key flags:
 - `-ngl 0` disables GPU offloading (CPU-only execution)
 - `-t 20` uses 20 threads (1 per Grace CPU core)
 
-If the build is successful, you will observe smooth model initialization and token generation, with CPU utilization increasing across all cores.
+If the build is successful, you will see smooth model initialization and token generation, with CPU utilization increasing across all cores.
 
 To monitor live CPU utilization and power metrics during inference, use `htop`:
 
@@ -125,7 +124,7 @@ htop
 ```
 
 The following screenshot shows CPU utilization and thread activity during TinyLlama inference on DGX Spark, confirming full multi-core engagement.
-![image2 htop screenshot](htop.png "TinyLlama CPU Utilization")
+![htop display showing 20 Grace CPU cores at 75-85% utilization during TinyLlama inference with OpenMP threading alt-text#center](htop.png)](htop.png "TinyLlama CPU Utilization")
 
 The `htop` interface shows:
 
@@ -138,7 +137,7 @@ The `htop` interface shows:
 In htop, press F6 to sort by CPU% and verify load distribution, or press `t` to toggle the tree view, which shows the `llama-cli` main process and its worker threads.
 {{% /notice %}}
 
-## What have I accomplished?
+## What you have accomplished
 
 In this section you have:
 - Built and validated the CPU-only version of llama.cpp.