Ryzen AI Max 395 vs RTX 4090 for Local LLMs

For local LLM inference, an RTX 4090 is usually much faster when the model fits in its 24 GB of VRAM and the software uses NVIDIA acceleration well. The Ryzen AI Max+ 395 is often the more practical choice when you need to load a larger model into shared memory on a compact system, but its memory capacity does not make it as fast as the 4090.

What These Chips Actually Are

The Ryzen AI Max+ 395 is a laptop-class processor combining CPU cores with an integrated Radeon 8060S GPU. Its system memory is shared: the operating system, CPU, and integrated GPU draw from the same physical pool. Certain systems using this processor can be configured with up to 128 GB of LPDDR5X memory, though capacity, memory speed, and firmware options depend on the specific device. Not all of that memory is available to a model; the OS and other applications need room too.

The RTX 4090 in this comparison means the desktop graphics card, not the similarly named laptop GPU. It has 24 GB of dedicated GDDR6X VRAM and a much larger, more capable GPU than an integrated graphics processor. A laptop RTX 4090 is a different, lower-power product with different performance. Check which one a benchmark or system specification actually refers to.

That distinction explains the central trade-off: the 4090 offers substantially more GPU compute and dedicated high-bandwidth memory, while the Max+ 395 can access a larger shared memory pool in suitably configured systems. Model fit and inference speed are separate questions.

How Memory Determines Which Models Fit

A model’s parameter count alone does not tell you its exact memory requirement. Weight quantization reduces the storage needed per parameter, but inference also uses memory for the context or KV cache, runtime buffers, and the application. Longer prompts and larger context windows can raise memory use considerably.

As a rough illustration, a 70-billion-parameter model stored at four bits has a theoretical weight size around 35 GB, before metadata and runtime overhead. It cannot fit in a 24 GB 4090’s VRAM as a complete GPU-resident model. A system with 128 GB of shared memory may have enough total capacity to load it, depending on the quantization, context length, software, and memory reserved by the system. That does not guarantee fast generation, or even that a particular application will support the required allocation.

Smaller quantized models are more likely to fit entirely in the 4090’s VRAM, where they can use its stronger GPU resources. If a model exceeds available VRAM, software may place some layers or data in system memory. This “offloading” can make a model usable, but transfers across the system connection usually reduce speed. Shared memory on the Max+ 395 avoids a separate discrete-GPU memory pool, but the integrated GPU still has far less compute capability than a desktop 4090.

Speed: Tokens per Second and Prompt Processing

For interactive use, two performance measures matter. Prompt processing, often called prompt ingestion or prompt evaluation, measures how quickly the system handles input tokens. Generation speed measures how quickly it produces output tokens. A machine can perform well in one and less well in the other, and a long context can change both.

When a quantized model fits in VRAM and a compatible inference engine uses the 4090 effectively, expect the desktop 4090 to be the faster option, often by a substantial margin. The exact gap varies with model architecture, quantization, batch size, context, drivers, and software. There is no single trustworthy tokens-per-second figure that applies to every model and setup.

The Max+ 395 can still deliver useful local inference, especially for smaller models or for experiments where capacity matters more than response speed. But loading a model successfully is not the same as getting responsive output. A large model may generate slowly enough that a smaller model on the 4090 is a better everyday tool.

Software Support and Setup

NVIDIA’s CUDA ecosystem is widely supported by local inference tools, which makes the 4090 a comparatively straightforward choice for many GPU-accelerated workflows. Confirm that the specific application supports your card, driver, model format, and chosen quantization. Features such as multi-GPU execution or particular attention optimizations should not be assumed without checking the software’s documentation.

AMD integrated graphics support is more dependent on the operating system, driver, and inference backend. Some applications support AMD GPUs through ROCm or other supported backends, while compatibility and performance can vary by platform. On a system that exposes the Radeon GPU only through a less mature or unsupported path, inference may fall back to CPU execution or fail to use the GPU as expected.

• Check the inference application’s current hardware support before choosing a machine.
• Verify the actual memory available to the GPU, not just the computer’s advertised RAM.
• Test the model format and quantization you intend to run; support differs between tools.
• Compare prompt processing and generation separately, at the context length you expect to use.

Power, Form Factor, and Practical Costs

A desktop RTX 4090 requires a suitable desktop, power supply, cooling, and physical space. Its high power consumption under GPU workloads also makes heat and fan noise relevant. The Max+ 395 is designed for compact systems and can be attractive when portability, lower system complexity, or a large unified memory configuration matters. Actual system power, cooling, and sustained performance depend on the particular laptop or mini-PC, not just the processor name.

Compare complete systems rather than processor and graphics-card names in isolation. Include memory capacity and configuration, storage, cooling, power limits, and whether the system can run your chosen inference software. Prices change by region and product configuration, so there is no stable price comparison that applies to every buyer.

Which One Should You Choose?

• Choose the desktop RTX 4090 if your priority is high generation speed, the models you use fit in 24 GB of VRAM, and your software has strong NVIDIA support.
• Consider the Ryzen AI Max+ 395 if you need a compact system and want the option to load models larger than 24 GB into a sufficiently large shared-memory pool, accepting slower inference and platform-dependent software support.
• Consider a different setup if you need both very large models and high speed. A larger-memory workstation GPU, a supported multi-GPU system, or a remote inference server may be more suitable, though each brings added cost and complexity.

Do not choose solely by the largest model that can be made to start. For a chatbot, coding assistant, or document workflow, a smaller model that fits comfortably and responds quickly can be more useful than a larger one that runs slowly. Conversely, if a particular model’s capabilities are essential and it does not fit in 24 GB, extra shared memory can be a meaningful advantage.

Frequently Asked Questions

Can the Ryzen AI Max+ 395 run a 70B model?
Potentially, on a system with enough usable shared memory and compatible inference software, especially with a compact quantization. The model’s weights are only part of the memory requirement; context and runtime overhead also matter, and performance may be slow.

Is the RTX 4090 better for local LLMs?
It is generally better for speed when the model fits in its 24 GB VRAM and the software supports NVIDIA acceleration. It is not automatically better when model capacity is the overriding requirement.

Does 128 GB of RAM mean a 128 GB model fits?
No. The operating system and applications use memory, and model weights need additional space for runtime buffers and context. The GPU may also have a lower usable allocation than the system’s total memory.

Will a model that exceeds 24 GB run on the 4090?
Sometimes, if the inference software supports offloading some model data to system RAM. This can reduce performance, and available system memory and transfer overhead affect the result.

Is the laptop RTX 4090 the same as the desktop RTX 4090?
No. The laptop version has different power and performance characteristics. Do not apply desktop-card benchmarks or specifications to a laptop without checking the exact GPU.

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