The 30-Second Answer
Before diving into memory timings and bus widths, here is the distilled decision framework:
A strong starting point when CUDA compatibility is non-negotiable, you rely on PyTorch or ComfyUI, the workload fits within available VRAM (16GB–32GB), and raw GPU throughput matters more than massive capacity.
Compelling when the target model requires far more than 32GB (e.g., 70B+ LLMs), you want a massive single memory pool (up to 128GB on M5 Max or 512GB on M5 Ultra), your software supports Apple Silicon/MLX, and compact form factor matters.
A viable third path if you require Windows or Linux, need substantially more accelerator-accessible memory than a consumer GPU provides (up to 96GB allocated graphics memory on 128GB systems), and your runtime supports ROCm, Vulkan, or llama.cpp.
Useful as a fallback when a model exceeds GPU VRAM. It prevents out-of-memory errors and makes execution possible, but transferring layers over the PCIe bus to system RAM substantially reduces token generation speed.
What Dedicated VRAM Actually Does
A discrete graphics card such as an NVIDIA GeForce RTX GPU features its own dedicated high-speed video memory (GDDR6 or GDDR7). The GPU processor accesses this memory directly across a dedicated, wide memory bus (typically 256-bit to 512-bit).
For AI workloads, dedicated VRAM creates a major operational advantage: the entire active neural network weights, attention matrices, and KV cache remain physically adjacent to the tensor cores and compute units performing the matrix multiplications. This delivers immense memory bandwidth (frequently exceeding 1,000 to 1,792 GB/s on high-end GPUs).
The Physical Limit of Dedicated VRAM
Dedicated VRAM is physically fixed onto the PCB. An RTX 5070 Ti or RTX 5080 has 16GB. An RTX 5090 has 32GB. When the model weights, KV cache, and runtime overhead exceed that boundary, the system must change strategy: applying heavier quantization, restricting context length, or offloading layers to host system RAM. A faster GPU core cannot compensate for a lack of physical capacity once memory is exhausted.
What Unified Memory Changes
Apple Silicon utilizes a Unified Memory Architecture (UMA). Rather than maintaining separate, physically isolated pools of system RAM for the CPU and dedicated VRAM for the GPU, both processors share a single, unified high-bandwidth pool of LPDDR5X memory packaged directly alongside the SoC silicon.
This architectural shift is significant for local AI inference because very large memory configurations are commercially accessible in consumer-class workstations. A Mac Studio configured with an M5 Max can feature 128GB of unified memory, while M5 Ultra variants scale up to 512GB.
A high-memory Mac Studio can hold 70B, 120B, or larger parameter models completely inside its accelerator-accessible memory pool — models that simply cannot load onto a 16GB or 32GB discrete consumer graphics card without severe CPU/RAM offloading.
The Important Nuance: Capacity ≠ Speed
Having 128GB of Unified Memory does not make a machine equivalent to a hypothetical “128GB RTX GPU.” Raw tensor compute throughput, memory bus bandwidth (M5 Max offers 460 GB/s, up to 614 GB/s depending on configuration; M5 Ultra reaches 1.2 TB/s versus 1,792 GB/s on an RTX 5090), and software backend optimizations (Metal / MLX vs. CUDA) mean execution characteristics differ fundamentally.
Ryzen AI Max Creates a Third Path
AMD’s Ryzen AI Max (such as the Ryzen AI Max+ 395) introduces a third architectural paradigm. Rather than forcing a choice between a traditional discrete GPU desktop or a macOS-exclusive workstation, these APUs combine high-performance CPU cores with powerful integrated RDNA graphics and a wide 256-bit LPDDR5X memory subsystem offering memory bandwidth of up to approximately 256 GB/s.
Systems equipped with 128GB of unified system memory permit configuring up to 96GB as Variable Graphics Memory (VGM). This allows a Windows or Linux system to allocate a large pool of memory to graphics and AI runtimes without purchasing an enterprise workstation accelerator (such as an NVIDIA RTX 6000 Ada). However, 96GB of VGM does not make it equivalent to 96GB of discrete high-bandwidth VRAM, as memory throughput and compute architecture remain different.
Additionally, software runtime compatibility must be verified prior to purchase. While frameworks like llama.cpp, Vulkan, and ROCm run successfully, many community ComfyUI custom nodes and PyTorch extensions remain hardcoded for NVIDIA CUDA.
Capacity and Bandwidth Solve Different Problems
When evaluating hardware for local AI, separate your evaluation into two distinct questions:
1. Memory Capacity (Gigabytes)
Question: Does the workload fit?
Capacity is a binary threshold. If model weights, KV cache, and runtime overhead exceed available accelerator memory, the model either refuses to run or must offload layers to slow system memory. Capacity enables large 70B+ LLMs, long context windows (32k–128k tokens), and multi-model diffusion pipelines.
2. Memory Bandwidth (GB/s)
Question: How quickly can the model execute?
Memory bandwidth is a major factor in memory-bound autoregressive decoding, but actual token throughput also depends on compute capability, quantization, model architecture and runtime optimization. When a model fits within VRAM, higher bandwidth architectures like an RTX 5090 (~1.8 TB/s) can deliver very fast token throughput compared to systems with narrower memory buses.
A huge memory pool solves the capacity bottleneck without necessarily delivering peak execution speed. Conversely, a blazing-fast consumer GPU delivers exceptional throughput, but cannot run models that exceed its 16GB or 32GB boundary.
Why Local LLMs Make Capacity So Important
Traditional gaming benchmarks measure frame rates at set resolutions. Local LLMs introduce a different reality: memory consumption scales dynamically based on multiple variables beyond the raw parameter count.
When planning system memory, factor in the total operational footprint:
- Base Model Weights: Determined by parameter size and quantization level (e.g., Q4_K_M, Q8_0, or FP16).
- KV Cache (Context Memory): Storing the key-value states for active conversation history. A 32,000 or 64,000-token context can require several additional gigabytes of memory depending on model architecture.
- Runtime & Execution Buffers: Memory allocated by Ollama, vLLM, LM Studio, or llama.cpp for activation states and scratchpad computation.
- Multimodal & Vision Encoders: If running vision-language models (VLMs), image encoders consume additional memory.
- Concurrent Operating System Tasks: Display outputs, web browsers, and background applications sharing GPU memory on a daily workstation.
This explains why a 14B model might fit comfortably in 12GB during a brief prompt, but exhaust a 16GB GPU when fed a large codebase with 28,000 tokens of context.
Which Architecture Should You Choose?
Match your primary workflow against hardware strengths:
Choose NVIDIA RTX Dedicated VRAM If:
- CUDA ecosystem compatibility is your highest priority.
- ComfyUI, Stable Diffusion, or PyTorch fine-tuning form your primary workflow.
- Your target models fit within 16GB (RTX 5070 Ti / 5080) or 32GB (RTX 5090).
- You also use GPU-accelerated creator software such as Blender Cycles, Houdini Karma XPU, or DaVinci Resolve Studio.
Consider Apple Unified Memory If:
- Large local LLMs (32B to 70B+) with extensive context windows are your main objective.
- Model memory capacity matters more to your research than raw CUDA-specific throughput.
- You require a compact, power-efficient, whisper-quiet desktop workstation (Mac Studio).
- macOS and tools like Apple MLX or llama.cpp integrate seamlessly into your developer environment.
Consider AMD Ryzen AI Max If:
- You want high-memory local AI capacity (up to 96GB graphics allocation) on Windows or Linux.
- Your workloads execute via supported backends such as llama.cpp or Vulkan.
- You prefer an integrated, compact form factor without the power draw or cost of enterprise GPUs.
Consider Cloud Compute If:
- Your AI experimentation is intermittent rather than daily.
- You require frontier-scale models (405B+) that exceed even 128GB workstations.
- Upfront capital expenditure and hardware maintenance offer little operational benefit.
Architecture Comparison & Decision Table
Compare the four primary local memory architectures side-by-side:
| Architecture | Memory Type | Capacity Class | Peak Bandwidth | CUDA Support | Best Workload |
|---|---|---|---|---|---|
| NVIDIA RTX Discrete | Dedicated GDDR6 / GDDR7 | 16GB – 32GB | Up to 1,792 GB/s (~1.8 TB/s) | Native (Primary) | ComfyUI, diffusion, CUDA PyTorch, high-speed LLM inference within VRAM, 3DCG crossover |
| Apple Silicon (UMA) | Unified LPDDR5X (SoC) | 36GB – 512GB | 460 – 614 GB/s (M5 Max) / 1.2 TB/s (M5 Ultra) | Metal / MLX (No CUDA) | Large 70B+ LLMs, massive context windows, silent workstation, macOS creator crossover |
| AMD Ryzen AI Max | Configurable Shared LPDDR5X | Up to 128GB (up to 96GB VGM) | Up to ~256 GB/s (Ryzen AI Max+ 395) | ROCm / Vulkan / GGUF | Large-memory LLM inference on Windows/Linux without enterprise workstation GPU prices |
| RAM Offload (DDR5) | Host System RAM (PCIe Bus) | 32GB – 128GB+ | Substantially lower accelerator bandwidth (Bus limited) | Partial fallback | Substantially lower effective accelerator access than dedicated or integrated high-bandwidth memory; actual performance depends on the PCIe path, system memory and runtime. |
CORE SPEC Editorial Principle
Do Not Buy Memory by the Headline Number Alone
Always evaluate in this sequence:
- What workload needs to fit? (Model parameters, quantization precision, maximum context length, and KV cache requirements).
- Which software ecosystem runs that workload? (CUDA-dependent tools vs. framework-agnostic runtimes like llama.cpp).
- What architecture delivers acceptable execution speed? (High memory bandwidth for rapid token output vs. large capacity for model residency).
Memory capacity determines what can fit. Memory architecture determines how that capacity can actually be utilized.
Frequently Asked Questions
Is Unified Memory the same as VRAM?
No. Unified Memory is a single shared memory pool accessed collaboratively by CPU and GPU cores on an integrated system architecture. Dedicated VRAM is high-speed memory physically mounted onto an add-in discrete graphics board.
Is 128GB Unified Memory better than 32GB VRAM?
Not universally. The 128GB unified memory pool allows running substantially larger neural models that cannot load into 32GB. However, a discrete RTX GPU delivers higher memory bandwidth and native CUDA acceleration, making it faster for models that comfortably fit in 32GB.
Can a Mac run a model that does not fit on an RTX 5090?
Potentially, yes. If configured with 64GB, 128GB, or more unified memory, a Mac Studio can keep a 70B quantized LLM entirely in memory, whereas an RTX 5090 (32GB) would exceed its dedicated VRAM ceiling and require offloading.
Does Ryzen AI Max have 96GB of physical VRAM?
No. High-memory Ryzen AI Max configurations allocate up to 96GB from their 128GB unified LPDDR5X system memory to graphics workloads via Variable Graphics Memory (VGM). It is not dedicated discrete GDDR memory.
Official Technical References
- NVIDIA GeForce RTX 50 Series: Blackwell Architecture & Memory Specifications
- Apple Mac Studio: Apple Silicon Unified Memory Specifications
- AMD Ryzen AI: AMD Ryzen AI Max Architecture Specifications
Next Steps
Explore adjacent local AI hardware guides and creator crossover analyses: