Unified memory has become one of the most important features for anyone running local LLMs in 2025. Instead of splitting memory between CPU RAM and GPU VRAM, unified architectures pool it into one high-bandwidth space that both the CPU and GPU can access. This matters because LLM inference is memory-bound long before it becomes compute-bound. The larger the model and the longer the context window, the more you rely on fast, shared memory rather than raw GPU TFLOPs. I cover the fundamentals of unified memory separately, so this page focuses on how it affects real LLM workloads and why unified-memory machines have become a practical alternative to traditional GPU-based setups.
In our tests, an RTX 5090 (December 2025 market price around $2800 just for the GPU) tops out around a 32B model with a 45K context in stable configurations, mainly due to VRAM limits. A compact unified-memory system with 128GB (around $2000) can load models up to roughly 120B in 4-bit with room for long contexts. This is why unified-memory machines have become the most cost-efficient way to experiment with truly large models without buying workstation-class GPUs or complex multi-GPU setups. There are caveats: architectures like AMD Strix Halo and Apple Silicon offer large unified memory pools but lower peak throughput when ingesting big prompts. Even so, they remain compelling for users who want low power draw, quiet systems, and the ability to run larger models than any single consumer GPU allows.
This page compares the leading unified-memory systems – AMD Ryzen AI MAX+ devices, Apple Silicon machines, and NVIDIA DGX Spark. You will see memory size, bandwidth, compute capabilities, real LLM prompt ingestion speed, tokens-per-second generation, and overall efficiency expressed in a way that speaks directly to local LLM users who care about performance per dollar, not theoretical peak numbers.
4. What Matters for Local LLM Performance
Unified-memory machines extend the advantages described above by giving you a straightforward way to scale model size without relying on multi-GPU setups or high-end workstation cards. Since local LLM performance is shaped by three main factors – how much memory you have for the model, how much bandwidth you can push during inference, and how much GPU compute you can apply during prompt processing, unified-memory systems sit in a useful middle ground. They deliver far more capacity than any single consumer GPU, enough bandwidth, and adequate compute for most practical workloads. For anyone focused on running large models with long context windows at a reasonable cost, these systems provide a clean and efficient path forward.
4.1 Memory Size
Model size scales directly with available unified memory. Medium-tier 7B–14B models like Qwen3, Llama 3.1, and Phi-4 usually require around 12–16GB in Q4 formats. Large 20B–36B models, such as Gemma 3 and gpt-oss variants, generally need about 24–32GB. Extra-large 70B–120B models, including Llama 3.3 and Mistral Large, typically demand 48–96GB depending on quantization and context length. Massive 235B+ models like GLM 4.5 or DeepSeek fall into server-grade territory and require multi-GPU setups or extremely high unified-memory configurations. Unified-memory machines make the 30B–120B range accessible in a compact single-system design that avoids the cost and complexity of multi-GPU VRAM machines.
4.2 Memory Bandwidth
LLM inference is bandwidth-bound. AMD’s Ryzen AI MAX+ 395 reaches roughly 256GB/s, while Apple’s top M-series chips reach about 500GB/s. Higher bandwidth improves per-token generation. Unified-memory systems remain below high-end GPUs in raw throughput but still handle 7B–70B 4-bit workloads reliably.
4.3 GPU / NPU / Compute Units
Inference is still GPU-driven. NPUs do not meaningfully accelerate large transformer models. Integrated GPUs in AMD and Apple systems are slower than dedicated GPUs but process quantized models effectively when paired with adequate bandwidth. DGX Spark is the first unified-memory platform that combines a large memory pool with truly high-end GPU compute.
4.4 Power Efficiency
Unified-memory machines are power-efficient compared to multi-GPU rigs. AMD mini PCs often operate between 100–200W, Apple Silicon handles large models at laptop-level power draw, and DGX Spark trades efficiency for workstation-class performance. For 24/7 local RAG or multi-model operation, the lower power draw of AMD and Apple systems becomes a practical advantage.
4.5 Price-to-Performance Considerations
Dedicated GPUs offer higher compute and bandwidth but become expensive once VRAM capacity becomes the bottleneck. Unified-memory systems provide much higher usable capacity per dollar, allowing users to run 30B–120B models without buying multiple GPUs. For workloads where model size and context length matter more than raw speed, unified-memory machines typically deliver the best overall value.