AMD ROCm vs. Vulkan Performance for Lemonade Local AI Server with Llama.cpp

Welcome back to Self-Hosted Tech! If you’ve been following the local AI revolution, you already know that Nvidia’s CUDA ecosystem has been the undisputed king of the hill. But here in the homelab community, we love a good challenge, and many of us have AMD GPUs sitting in our rigs gathering dust while we wait for the software ecosystem to catch up.

Well, the wait is getting much shorter. Thanks to the relentless work of the open-source community, tools like llama.cpp and server wrappers like the Lemonade Local AI Server are making AMD GPUs incredibly viable for running local Large Language Models (LLMs).

But when you fire up llama.cpp on an AMD card, you’re usually presented with a choice for GPU acceleration: ROCm or Vulkan. Which one should you choose? Which one gives you the best tokens-per-second? And more importantly, which one is going to save you from spending three hours troubleshooting missing shared libraries?

Today, we’re going to break down the differences between AMD ROCm and Vulkan, run some real-world benchmarks using the Lemonade Local AI Server, and walk through a practical setup so you can squeeze every last drop of performance out of your Radeon card.

Understanding the Backends: ROCm vs. Vulkan

Before we start throwing benchmark numbers at you, let’s talk about what these two backends actually are. llama.cpp is designed to be hardware-agnostic, meaning it can compile for different hardware accelerators using different “backends.”

What is ROCm?

ROCm (Radeon Open Compute) is AMD’s answer to Nvidia’s CUDA. It’s a dedicated compute stack designed specifically for high-performance computing (HPC) and machine learning workloads. When you compile llama.cpp with ROCm (specifically using the HIP programming language), you are using AMD’s highly optimized math libraries (like hipBLAS) to crunch matrix multiplications as fast as the silicon allows.

The Pros of ROCm:

  • Maximum Performance: ROCm is built for compute. It bypasses graphics overhead and talks directly to the hardware.
  • Memory Efficiency: Better memory management for large model loads.
  • Ecosystem Support: It’s the backend that major players like PyTorch target, so it gets the most optimization attention from AMD’s engineers.

The Cons of ROCm:

  • Strict Hardware Support: ROCm officially only supports a handful of consumer cards (mostly the RX 7000 series and a few high-end RX 6000 cards). If you have an older card like an RX 580, ROCm might not work at all without unsupported workarounds.
  • Setup Complexity: Installing the full ROCm stack on Linux can be a dependency nightmare if your distro isn’t perfectly aligned with AMD’s supported versions.

What is Vulkan?

Vulkan is a modern, low-level graphics and compute API. While it was designed for rendering games, it has incredibly powerful compute shader capabilities. llama.cpp can compile using Vulkan to run inference on virtually any GPU that supports the API.

The Pros of Vulkan:

  • Universal Compatibility: If your GPU supports Vulkan (which almost everything from the last 7 years does), it will run. This is a lifesaver for homelabbers rocking older cards or unsupported architectures.
  • Easy Setup: Compiling llama.cpp with Vulkan is usually as simple as installing the Vulkan SDK and flipping a CMake flag. No massive ROCm packages required.
  • Cross-Platform: It works beautifully on Windows and Linux alike, often with less friction than ROCm on Windows.

The Cons of Vulkan:

  • Overhead: Because Vulkan is a graphics API first, there is some inherent overhead compared to a bare-metal compute stack like ROCm.
  • Slower Inference: You will generally see a 15-25% drop in tokens-per-second compared to a working ROCm setup.

The Test Bench and The Software

To give you accurate, practical numbers, we need to define our homelab test bench.

  • CPU: AMD Ryzen 7 5800X
  • RAM: 64GB DDR4 3200MHz
  • GPU: AMD Radeon RX 7800 XT (16GB VRAM)
  • OS: Ubuntu 22.04 LTS
  • Model: Meta-Llama-3-8B-Instruct (Quantized to GGUF Q4_K_M)

For the server software, we are using the Lemonade Local AI Server. Lemonade is an excellent, lightweight server wrapper that manages your models, handles the API requests, and spins up llama.cpp under the hood. It exposes an OpenAI-compatible API, meaning you can point your favorite frontend (like Open WebUI or AnythingLLM) right at it.

Compiling llama.cpp for Lemonade

Lemonade relies on your local llama.cpp binaries. To get the best performance, we aren’t going to use pre-compiled binaries; we’re going to build them ourselves.

First, let’s grab the source code:

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git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp

Compiling for ROCm

If you have an RX 7000 or supported RX 6000 series card and have the ROCm 6.0+ packages installed, you should absolutely compile for ROCm. The RX 7800 XT uses the gfx1100 architecture, so we need to tell the compiler to target that specifically.

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# Ensure ROCm is in your path
export PATH=/opt/rocm/bin:$PATH

# Configure the build
cmake -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1100 -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++

# Compile
cmake --build build --config Release -j 8

Note: Using clang is highly recommended for ROCm, as GCC sometimes struggles with HIP code.

Compiling for Vulkan

If ROCm fails, or you just want to test Vulkan, the process is arguably simpler. You just need the Vulkan headers installed on your system (sudo apt install libvulkan-dev).

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# Configure the build
cmake -B build -DGGML_VULKAN=ON

# Compile
cmake --build build --config Release -j 8

Once you have your binaries, you simply point your Lemonade Server configuration to the build/bin directory where your llama-cli and llama-server executables live.

Benchmarking: ROCm vs. Vulkan

Let’s get down to the brass tacks. We loaded the Llama-3-8B Q4_K_M model into the Lemonade server, allocating 14 layers to the GPU (keeping a few layers on the CPU to ensure we don’t hit VRAM limits), and ran a standard prompt generation test.

Here are the metrics we tracked:

  • Prompt Processing (Eval): How fast the model ingests the input (measured in tokens/second).
  • Generation: How fast the model spits out the answer (measured in tokens/second).
  • Time to First Token (TTFT): How long you wait before the first word appears.

The Results

ROCm Backend (HIP):

  • Prompt Processing: 245.3 tokens/second
  • Generation: 52.1 tokens/second
  • Time to First Token: 0.82 seconds

Vulkan Backend:

  • Prompt Processing: 180.1 tokens/second
  • Generation: 41.5 tokens/second
  • Time to First Token: 1.15 seconds

Analyzing the Numbers

As expected, ROCm takes the crown by a significant margin.

In terms of generation speed, ROCm is pushing out about 25% more tokens per second than Vulkan. For an interactive chat experience, 41 tokens per second (Vulkan) is still highly readable and feels fast, but 52 tokens per second (ROCm) feels instantaneous.

The biggest difference, however, is in Prompt Processing (often called prefill). When you paste a large document into your local AI and ask it to summarize, ROCm chews through that input context 36% faster than Vulkan. This translates directly to a lower Time to First Token. If you are building RAG (Retrieval-Augmented Generation) pipelines in your homelab where large system prompts and documents are passed every time, ROCm will save you noticeable amounts of waiting.

Practical Walkthrough: Running an Inference Test

Let’s do a quick practical walkthrough so you can test this yourself once you have your Lemonade server running.

Assuming your Lemonade server is running on localhost:8080 and exposes the OpenAI-compatible API, you can use a simple curl command to test the latency and speed.

Create a bash script called test_ai.sh:

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#!/bin/bash

# The endpoint provided by Lemonade
API_URL="http://localhost:8080/v1/chat/completions"

# Start timer
START_TIME=$(date +%s%N)

# Send the request
RESPONSE=$(curl -s -X POST "$API_URL" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Meta-Llama-3-8B-Instruct.Q4_K_M",
    "messages": [{"role": "user", "content": "Explain the difference between ROCm and Vulkan in three sentences."}],
    "temperature": 0.7,
    "max_tokens": 100
  }')

# End timer
END_TIME=$(date +%s%N)

# Calculate elapsed time in seconds (converting nanoseconds to seconds)
ELAPSED=$(echo "scale=2; ($END_TIME - $START_TIME) / 1000000000" | bc)

echo "Response:"
echo "$RESPONSE" | jq -r '.choices[0].message.content'
echo "----------------------------------------"
echo "Total Request Time: $ELAPSED seconds"

Make it executable and run it:

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chmod +x test_ai.sh
./test_ai.sh

You can run this script while the Lemonade server is configured to use your ROCm binary, restart the server with the Vulkan binary, and run it again. You will visibly see the difference in how quickly the text streams back to your terminal!

When Should You Use Which?

So, what’s the final verdict for your homelab?

Go with ROCm if:

  • You have a supported AMD card (RX 6000/7000 series, or an RX 5700 XT with some unsupported flag tweaking).
  • You are running Linux and are comfortable installing the ROCm toolkit.
  • You want the absolute maximum performance for RAG applications and heavy document processing.

Go with Vulkan if:

  • You have an older, unsupported AMD card (like the RX 500 series or Vega 56).
  • You are running Windows, where the ROCm support is still a bit limited compared to Linux.
  • You just want a setup that compiles in two minutes without fighting system dependencies, and you don’t mind losing a few tokens per second.

Summary and Call to Action

The local AI landscape is moving fast, and AMD users are no longer left out in the cold. While Nvidia still holds the ease-of-use crown, the combination of llama.cpp and the Lemonade Local AI Server makes running models on Radeon hardware a genuinely great experience.

To sum it up: ROCm is your high-performance race car, giving you a 25% boost in generation speed and vastly superior prompt processing, provided you have the right hardware and don’t mind getting your hands dirty with the setup. Vulkan is your reliable daily driver, offering near-universal compatibility and a frictionless setup at the cost of some raw speed.

What GPU are you running in your AI homelab? Have you made the jump to ROCm, or are you sticking to Vulkan for now? Let us know in the comments below, and if you found this breakdown helpful, consider subscribing to the Self-Hosted Tech YouTube channel and blog for more deep dives into homelabs, local AI, and getting the most out of your hardware!