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The most reproducible starting point is Ubuntu 24.04 LTS, AMD ROCm 7.2.1, and Ollama 0.20.x on a Ryzen AI Max+ 395 system with Radeon 8060S graphics. Install ROCm for the exact operating system and device, install Ollama, pull a moderate model such as qwen3.5:35b, and use ollama ps to verify that the model is actually running on the GPU. A 128GB unified-memory configuration can load models far larger than a conventional integrated-GPU computer, but it does not provide 128GB of dedicated, always-fast VRAM: the operating system, CPU, GPU, model weights and context share the same physical pool.
AMD’s published figures are versioned measurements, not performance guarantees. They show 42.04 tokens per second for a 35B-A3B model with full GPU offload, 29.84 tokens per second for a 9B model, and 8.59 tokens per second for a 122B-A10B model split between GPU and CPU. Your result will change with memory allocation, quantization, context length, thermals, drivers and runtime versions.
What the Ryzen AI Max+ 395 platform changes for local LLMs
The Ryzen AI Max+ 395 combines a high-end mobile CPU with Radeon 8060S integrated graphics and a large unified-memory pool. In AMD’s documented example, the machine has 128GB of LPDDR5X memory. CPU and GPU access the same physical memory rather than using separate system RAM and a discrete graphics card, so a model can be larger than the GPU allocation alone would suggest.
That architecture is useful for local inference because model weights do not have to fit entirely inside a small conventional VRAM partition. It is not equivalent to having 128GB of dedicated VRAM, however. The operating system, applications, inference runtime, model weights, key-value cache and context all compete for the pool. AMD’s example starts with 64GB configured as GPU-accessible memory, and that allocation can be changed. Giving the GPU more of the pool leaves less headroom for the rest of the system.
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AMD describes the capability this way: “With 128 GB of unified memory, the entire memory pool is accessible for AI workloads — enabling models with 100 billion or more parameters to run on a single system, with no second card and no cloud bill.” That statement describes capacity, not a promise that every 100-billion-parameter model will be fully GPU-resident or responsive. Quantization, context length and the division between GPU and CPU determine the practical result.
Choose the operating system and runtime first
Recommended Linux baseline
Use Ubuntu 24.04 LTS with ROCm 7.2.1 and Ollama 0.20.x if you want to reproduce AMD’s documented path most closely. The corresponding hardware example is a Ryzen AI Max+ 395 with Radeon 8060S graphics, 128GB LPDDR5X unified memory and a configurable 64GB GPU-accessible allocation.
ROCm installation is device- and release-specific. Use AMD’s current installation instructions, select Ubuntu 24.04 and the exact Radeon/Ryzen device family, and install the host ROCm components and required system libraries before configuring an inference application. Do not assume that a package intended for a different Radeon architecture will provide working acceleration.
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Windows choices
Windows has a different support story. AMD documents LM Studio as a graphical interface over llama.cpp; it can download models, select a runtime, and expose an OpenAI-compatible local endpoint for other applications. AMD’s Windows guidance discusses Vulkan and ROCm runtime choices, driver updates and an AMD Variable Graphics Memory setting. Reboot after changing a memory setting, and confirm the current support matrix for the exact mini PC before relying on an older playbook.
AMD also provides prebuilt Windows llama.cpp packages for supported architecture targets, including gfx115x, the family associated with Strix Halo products. This route requires a separate GGUF model file and uses llama-server with explicit context, GPU-layer and Flash Attention options. AMD’s Ryzen limitations page for ROCm 7.2.1 stated that only PyTorch was then available on Windows in the ROCm stack, with the rest supported on Linux. That was a release-specific snapshot, so check current ROCm documentation rather than treating it as a permanent Windows limitation.
Prepare the mini PC
Hardware and software checklist
- Ryzen AI Max+ 395 processor with Radeon 8060S graphics or the exact compatible device listed by AMD.
- Enough unified memory for the model, context and normal desktop workload. AMD’s reference configuration uses 128GB; lower-capacity configurations have less room for large models.
- Ubuntu 24.04 LTS for the documented ROCm/Ollama baseline, or a currently supported Windows setup for LM Studio or llama.cpp.
- A current AMD graphics driver and the ROCm release required by the chosen runtime.
- Network access while downloading ROCm packages, Ollama and model files.
- Free storage for GGUF or runtime-managed model files. The model’s compressed file size is not the only memory requirement; runtime buffers and context consume additional memory.
Keep memory headroom
Before loading a large model, close browsers, virtual machines and other memory-heavy applications. AMD specifically recommends reducing competing memory use and adjusting context length for the model. A 64GB GPU-accessible setting does not mean 64GB is available to weights alone: the desktop, driver allocations and runtime overhead are still present.
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Install and verify Ollama on Ubuntu
The following is AMD’s documented smoke-test sequence. Run it after ROCm is installed for the correct device and the system has been rebooted if the driver instructions require it.
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- Install Ollama with the official installer:
curl -fsSL https://ollama.com/install.sh | sh. - Download the example model:
ollama pull qwen3.5:35b. - Start an interactive session:
ollama run qwen3.5:35b. - In another terminal, inspect the loaded process:
ollama ps.
When all layers are offloaded to the Radeon 8060S, AMD says the process listing should show 100% GPU. This check matters more than simply seeing a response: a model can run successfully while falling back partly or entirely to the CPU.
If ollama ps does not show 100% GPU
- Confirm that the active driver and ROCm installation match the Ubuntu release, device and runtime version.
- Check whether system memory pressure forced a smaller GPU allocation or CPU placement.
- Try a smaller model or shorter context to establish a known-good fully offloaded case.
- Verify that the model’s format and runtime support the selected backend.
- Only after those checks, compare token rates. A CPU fallback is a configuration problem before it is a hardware benchmark.
Understand memory allocation before choosing a model
Unified memory removes a hard divide between RAM and VRAM, but it does not remove capacity or bandwidth limits. Think of three separate quantities:
- Total installed memory: the physical LPDDR5X pool, such as 128GB in AMD’s reference system.
- GPU-accessible allocation: the portion initially made available to the graphics processor, 64GB in that example and configurable on the platform.
- Working memory: what remains after the operating system, applications, model runtime, weights, temporary buffers and context are accounted for.
A model smaller than the GPU-accessible allocation can often be fully offloaded, but the comparison must use the runtime’s actual footprint rather than a marketing parameter count. A quantized 35B model may occupy about 20.5GB in AMD’s Q4_K_M measurement, while a 122B model occupies 76GB and exceeds the 64GB GPU allocation. The latter therefore uses both GPU and CPU memory.
Longer context increases the key-value cache and can materially raise memory use even when the model weights do not change. If a large model loads but becomes slow or unstable after a long conversation, reduce context, close other applications or increase the GPU allocation only if the operating system still has adequate headroom.
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Model sizes and AMD’s published measurements
AMD reported the following Q4_K_M results in 2026 on its disclosed Ryzen AI Max+ 395 environment. They are publisher-reported measurements under that setup, not minimum expected speeds.
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| Model | Quantization | Reported model size | Placement | Speed | Interpretation |
|---|---|---|---|---|---|
| Qwen3.5 9B | Q4_K_M | 6.2GB | 100% GPU offload | 29.84 tokens/s | Smallest example and a useful baseline for responsive local chat. |
| Qwen3.5 35B-A3B | Q4_K_M | 20.5GB | 100% GPU offload | 42.04 tokens/s | Only about 3B parameters are active per token, so this mixture-of-experts model can outpace the dense 9B example. |
| Qwen3.5 122B-A10B | Q4_K_M | 76GB | 61% GPU / 39% CPU | 8.59 tokens/s | The model exceeds the 64GB GPU-accessible allocation and is split across processors. |
The 35B-A3B result illustrates why total parameter count alone is a poor speed predictor. Its active-parameter count per token is about 3B, while its full stored weight set is much larger. Conversely, the 122B example demonstrates the cost of crossing the GPU allocation boundary: a model can fit in 128GB of unified memory and still be substantially slower because some layers run on the CPU.
Pick a model by latency, quality and capacity
For interactive chat and coding
Start with a model that fits comfortably inside the GPU-accessible allocation, leaving room for context and the operating system. The documented 35B-A3B example is a practical starting point on the 128GB system because AMD measured full GPU offload and 42.04 tokens per second. A smaller 9B model uses less memory and may be preferable when you need maximum headroom for long context or several other applications.
For maximum model capacity
Models around or above 70GB can be loaded on a 128GB machine, but expect mixed CPU/GPU placement unless the GPU allocation and remaining system memory support full residency. The 122B-A10B example ran at 8.59 tokens per second with 61% of the load on the GPU and 39% on the CPU. That can be valuable for experimentation or tasks where model capability matters more than conversational speed.
For long documents
Choose the model first, then set a context length that leaves measurable memory headroom. A short prompt that works at the default context does not prove that a very long document will fit. Increase context gradually while watching system memory and response behavior, and close memory-intensive applications before testing the largest setting.
Using LM Studio on Windows
LM Studio provides a GUI around llama.cpp. In AMD’s description, it can download and deploy models, select Vulkan or ROCm runtimes where supported, and serve an OpenAI-compatible local endpoint. That endpoint lets compatible desktop tools or scripts send requests to the mini PC without moving inference to a cloud service.
- Update the AMD graphics driver using the current instructions for the mini PC.
- Install the current LM Studio release and verify which AMD runtime options it exposes for the machine.
- Download a GGUF model whose size and quantization leave room for the operating system and context.
- Select the available Vulkan or ROCm backend, then load the model.
- Use the application’s performance or system information view to confirm GPU use rather than assuming that a successful response is accelerated.
- If you change AMD Variable Graphics Memory, reboot before testing again.
Labels and runtime availability can change between LM Studio, driver and ROCm releases. Treat the GUI as a convenient front end, not as evidence that every backend is supported on every Windows build.
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Using llama.cpp directly
Direct llama.cpp is useful when you need explicit control over model format, context length, GPU layers, Flash Attention or a server endpoint. AMD’s Windows package includes architecture-specific prebuilt ROCm binaries for supported targets such as gfx115x. You must provide a compatible GGUF file separately.
The documented server approach is to start llama-server with a chosen context length, a GPU-layer offload setting and Flash Attention enabled where supported. The exact executable arguments depend on the package version, so use the command syntax shipped with the current AMD build rather than copying options from an older release. On Linux, AMD lists supported Radeon GPUs and Ryzen APUs and expects a host ROCm installation plus required system libraries.
Troubleshoot the common failure modes
The model loads but is much slower than expected
Check placement first. A model that spills into CPU memory, even partially, can be dramatically slower than a fully offloaded model. Reduce model size or context, close applications, and inspect the runtime’s GPU-layer or offload report. Compare only after confirming the same quantization and placement.
The model will not load
Estimate the complete working set rather than comparing only the advertised model file size. Leave room for the operating system, runtime buffers and context. Lower the context length, select a smaller quantization or model, and ensure that the GPU-accessible setting is not consuming memory needed by the desktop.
GPU acceleration is unavailable
Verify the OS-specific driver and ROCm requirements, then confirm that the runtime actually has an AMD backend selected. On Linux, the documented baseline is Ubuntu 24.04 LTS with ROCm 7.2.1 and Ollama 0.20.x. On Windows, support differs between Vulkan, ROCm, LM Studio and llama.cpp releases; a package that works on Linux is not automatically interchangeable with a Windows build.
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Undo the allocation change or reduce it until the operating system and applications have sufficient memory. Unified memory is shared: assigning more to the GPU does not create new physical memory and can starve the CPU side.
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A practical testing workflow
- Record the operating system, driver, ROCm version, runtime version, model file and quantization.
- Start with the 9B or 35B-class model and a moderate context.
- Confirm GPU placement using
ollama pson Linux or the equivalent runtime information in the GUI or server. - Run the same short prompts several times and record generation speed only after the model is warm.
- Increase context or model size one change at a time, watching memory and placement.
- When a model crosses the GPU allocation boundary, treat the result as a mixed CPU/GPU configuration and expect a different latency class.
This workflow separates software configuration problems from the normal performance trade-off of running a model that cannot fit wholly in the GPU-accessible portion of unified memory.
What one mini PC can and cannot do
A single Ryzen AI Max+ 395 mini PC is a strong local inference appliance for models that fit in its memory budget, especially when they can be fully offloaded. It is not a substitute for a multi-GPU server when the goal is high-throughput serving, very large batches or consistently low latency on models that require extensive CPU spillover.
AMD has also demonstrated distributed llama.cpp RPC inference across four Framework Desktop systems. That evidence describes a multi-node configuration, not the capability of one 395 mini PC, so it should not be used to claim that a single machine can run trillion-parameter models.
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TechRadar’s 2026 review of the GMKtec EVO-X3 identified a configuration with the Ryzen AI Max+ 395, Radeon 8060S graphics and 128GB of LPDDR5X unified memory. The review found local AI capability but also noted that unlocking the platform required knowledge and experimentation. Treat that as one reviewed configuration, not a specification shared by every mini PC using the processor.
Check the exact listing for memory capacity, firmware options, cooling, power limits and operating-system support before purchase. AMD’s software setup does not require a special accessory beyond the computer and ordinary network access for downloading software and models.
Bottom line for a reliable setup
Use AMD’s Ubuntu 24.04/ROCm 7.2.1/Ollama 0.20.x combination when reproducibility matters, verify actual offload with ollama ps, and begin with a model that fits inside the GPU-accessible allocation. The 128GB unified-memory design makes 100-billion-parameter-class experiments possible on one box, but the 122B example shows why capacity and speed are different questions. Keep context and background applications under control, record versions and quantization, and expect Windows runtimes and ROCm support to change over time.
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