Two GigaGPU servers offer 96 GB for AI workloads by very different routes. The RTX 6000 Pro gives you 96 GB of dedicated GDDR on a single discrete GPU. The Ryzen AI Max+ 395 gives you 96 GB of unified LPDDR5X shared between CPU and an integrated RDNA 3.5 GPU. They solve the “I need a lot of memory for a large model” problem in opposite ways, and on our dedicated GPU servers they are priced for very different buyers.
Topics
- What unified memory actually means
- Why bandwidth decides most of it
- Workloads where each one wins
- Software stack notes
- Who should buy which
Unified vs Dedicated
The Max+ 395 ships with LPDDR5X accessible to both the CPU and integrated GPU with zero copy between them. You allocate up to 96 GB to the GPU address space. The 6000 Pro has its own 96 GB GDDR on a 512-bit bus. Unified memory eliminates host-to-device transfers entirely. Dedicated VRAM delivers raw bandwidth that LPDDR5X simply cannot match.
The Bandwidth Gap
| Spec | Ryzen AI Max+ 395 | RTX 6000 Pro |
|---|---|---|
| Memory | 96 GB unified LPDDR5X-8000 | 96 GB GDDR on 512-bit |
| Bandwidth | ~256 GB/s | ~1,800 GB/s |
| GPU compute | 40 CU RDNA 3.5 | Blackwell workstation class |
| Power envelope | ~120 W total SoC | ~300 W card |
The 7x bandwidth advantage on the 6000 Pro directly translates into tokens per second for LLMs because decode is bandwidth-bound. Our tokens per watt piece explains this in detail.
Where Each One Wins
Ryzen AI Max+ 395 wins when:
- You need to load a giant model that does not fit on a single discrete GPU, at any speed
- Your workload is CPU-heavy with occasional GPU passes (hybrid pipelines)
- Power efficiency and cooling simplicity matter
- You want one box that runs both conventional server workloads and small AI tasks
RTX 6000 Pro wins when:
- Throughput and latency matter (all production LLM serving)
- You batch many concurrent requests
- You train or fine-tune models
- Your workload is pure GPU with minimal CPU involvement
Pick the Memory Architecture That Fits Your Workload
Both platforms available on our UK dedicated hosting with fixed monthly pricing.
Browse GPU ServersSoftware Notes
The Max+ 395 works with ROCm and llama.cpp’s Vulkan backend. Do not expect the same experience as CUDA. Ollama supports it. vLLM does not officially. For LLM serving you will lean on llama.cpp or Ollama primarily. The 6000 Pro has full CUDA support, vLLM, TGI, SGLang, and every research repo ever published. If your workflow involves cloning experimental code weekly, the 6000 Pro avoids hours of porting. For stable production of well-supported models like Llama or Qwen via Ollama, the Max+ is fine.
Who Should Buy Which
Pick the Max+ 395 if you are a solo developer or small team exploring local AI with large models and you do not need production throughput. Pick the 6000 Pro if you are building a commercial product that serves tokens to users and latency or throughput affects revenue. The cost difference matters less than the workload fit – a fast Max+ is cheaper than a slow 6000 Pro but neither statement is true.
If you are sizing up to the 6000 Pro from smaller cards, also read 6000 Pro vs dual 5090 and single 6000 Pro vs four 4060 Ti.
