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.
Best Unified Memory Mini PCs for LLMs
Apple Mac Studio (M3 Ultra)
The Apple Mac Studio with the M3 Ultra and 512GB of unified memory is currently the most capable unified memory computer we have tested. Although Apple no longer sells this configuration new, refurbished systems are available for around $8,000 USD. With 512GB of unified memory and approximately 819 GB/s of memory bandwidth, it remains the only compact computer capable of running today’s huge open-weight models locally.
The largest model we successfully tested was GLM-5.2 744B (467GB, 4-bit MXFP4). Despite its enormous size, at 32k context the system generated 19 tokens/s while processing prompts at roughly 150 tokens/s, making interactive inference possible on a single machine.
Smaller mixture-of-experts models perform considerably faster. MiniMax-M2.5 (8-bit) reached 426.1 tokens/s during prompt processing and 14.9 tokens/s during generation with a 32K context window. Qwen3.5-122B-A10B delivered 764.7 prompt tokens/s and 42.4 generation tokens/s, making it one of the fastest high-end LLM experiences we have measured on a unified memory system.
Apple Mac Studio (M3 Ultra) benchmakrs
| Model Name | Quantization | Context Length | PP (tokens/s) | TG (tokens/s) |
|---|---|---|---|---|
| Gemma 4 26B A4B IT | 4-bit | 16K | 2,092 | 79.0 |
| Gemma 4 26B A4B IT | 4-bit | 32K | 1,775 | 70.1 |
| Gemma 4 26B A4B IT | 4-bit | 64K | 1,518 | 48.8 |
| Qwen 3.6 35B A3B | 4-bit | 16K | 2,651 | 93.3 |
| Qwen 3.6 35B A3B | 4-bit | 32K | 2,230 | 87.4 |
| Qwen 3.6 35B A3B | 4-bit | 64K | 1,695 | 74.5 |
| Qwen 3.5 122B A10B | 4-bit | 16K | 897.9 | 44.2 |
| Qwen 3.5 122B A10B | 4-bit | 32K | 780.9 | 38.2 |
| Qwen 3.5 122B A10B | 4-bit | 64K | 585.9 | 29.2 |
AMD Ryzen AI MAX+ Mini PCs
AMD’s Ryzen AI MAX+ platform is currently the best value alternative to Apple’s Ultra systems. Mini PCs such as the GMKtec EVO-X2 combine the Ryzen AI MAX+ 395 processor with up to 128GB of unified memory and approximately 256 GB/s of memory bandwidth. Although memory bandwidth is significantly lower than Apple’s Ultra chips, the price is also substantially lower.
The largest model we successfully ran was Step 3.5 Flash 196B using a 4-bit quantization, something previously impossible on compact Windows mini PCs.
During testing, Qwen3.6-35B-A3B-UD-Q4_K_XL achieved 807 prompt tokens/s and 14 tokens/s generation with a 32K context window. For larger reasoning models, Qwen3.5-122B-A10B processed prompts at approximately 300 tokens/s while generating 20 tokens/s.
GMKtec EVO-X2 benchmaks
| Model Name | Quantization | Context Length | PP (tokens/s) | TG (tokens/s) |
|---|---|---|---|---|
| Gemma 4 26B A4B IT | 4-bit | 32K | 761.50 | 37.29 |
| Gemma 4 26B A4B IT | 4-bit | 64K | 499.49 | 35.18 |
| Qwen 3.6 35B A3B | 4-bit | 32K | 807.33 | 43.47 |
| Qwen 3.6 35B A3B | 4-bit | 64K | 547.71 | 38.47 |
| Qwen 3.5 122B A10B | 5-bit | 32K | 300.11 | 18.47 |
| Qwen 3.5 122B A10B | 5-bit | 64K | 202.62 | 17.30 |
These systems are currently the best budget choice for users who want to run models larger than 100B parameters without moving to expensive multi-GPU workstations.
Best Unified Memory Laptops
Unified memory laptops make it possible to run surprisingly large language models without an external GPU. Apple currently leads this category, while Windows is finally becoming competitive through AMD’s Strix Halo platform.
Apple MacBook Pro M5 Max
The MacBook Pro with the M5 Max and 128GB of unified memory is currently the fastest unified memory laptop we have tested. The 40-core GPU version provides 614 GB/s of memory bandwidth, allowing it to handle models that previously required desktop workstations.
The largest model we successfully ran was Step 3.5 Flash 196B using a 4-bit quantization.
Our benchmarks show excellent performance across both dense and mixture-of-experts models. GPT-OSS-120B (Q8) achieved 2,537 prompt tokens/s and 64.5 tokens/s generation at a 32K context window. Qwen3.5-122B-A10B reached 1,188 prompt tokens/s and 44.9 tokens/s generation, while Qwen3.5-27B processed prompts at 591 tokens/s with 14.9 tokens/s generation.
| Model Name | Quantization | Context Length | PP (tokens/s) | TG (tokens/s) |
|---|---|---|---|---|
| Qwen 3.6 35B A3B | 4-bit | 16K | 3,922 | 108.6 |
| Qwen 3.6 35B A3B | 4-bit | 32K | 3,240 | 98.8 |
| Qwen 3.6 35B A3B | 4-bit | 64K | 2,245 | 83.9 |
| Gemma 4 26B A4B IT | 4-bit | 16K | 2,726 | 90.0 |
| Gemma 4 26B A4B IT | 4-bit | 32K | 2,353 | 77.7 |
| Gemma 4 26B A4B IT | 4-bit | 64K | 1,923 | 59.6 |
| Qwen 3.5 122B A10B | 4-bit | 16K | 1,328 | 59.5 |
| Qwen 3.5 122B A10B | 4-bit | 32K | 1,188 | 49.5 |
| Qwen 3.5 122B A10B | 4-bit | 64K | 620.4 | 33.6 |
For users who want the highest possible LLM performance in a portable computer, this is currently the strongest option available.
Windows Laptops with Unified Memory (AMD Strix Halo)
Windows finally has competitive unified memory laptops through AMD’s Ryzen AI MAX+ processors, also known as Strix Halo. Systems from manufacturers such as HP, Framework, ASUS, and others can be configured with up to 128GB of unified memory.
Compared to Apple Silicon, these laptops generally have lower memory bandwidth, which reduces performance on the largest models. However, they offer a much broader hardware ecosystem and lower entry prices.
In our testing, these systems comfortably handled 30B class models while also running models above 100B parameters when configured with 128GB of memory. They are an excellent choice for users who need Windows while still wanting to run modern local LLMs without dedicated GPUs.
FAQ: How Much Unified Memory Do You Need?
Is 32GB of Unified Memory Enough?
Yes, but only if you plan to run smaller models. We consider 32GB the practical minimum for local LLM use today.
With 32GB of unified memory you can comfortably run today’s 27B to 32B parameter models at 4-bit quantization. Examples include Qwen3.5 27B, Qwen3.5 32B, Gemma 4 26B, Gemma 4 31B, GPT-OSS-20B, and GLM 4.7 Flash. Context windows up to 128K or 256K are possible depending on the model, with memory usage typically ranging between 23GB and 33GB.
The most practical laptop in this category is the MacBook Pro with the M5 Max and 32GB of unified memory.
What Can You Run with 48GB?
48GB opens the door to the 70B model class while also allowing much larger context windows for 30B models.
Besides every model that fits into 32GB, you can begin running models such as Qwen3 Coder Next 80B A3B. Good hardware choices include newer MacBook Pro models with 48GB as well as refurbished M3 Max and M4 Max systems. For mini PCs, Apple’s Mac Studio remains the primary option.
What Can You Run with 64GB?
64GB mostly increases flexibility rather than unlocking many completely new models.
It allows larger context windows for existing models while making lower-bit versions of GPT-OSS-120B and GLM 4.5 Air practical. Hardware choices include MacBook Pro models and Ryzen AI MAX+ mini PCs such as the GMKtec EVO-X2, Framework Desktop, and HP Z2 Mini.
What Can You Run with 128GB?
For most users interested in local AI, 128GB is the sweet spot.
At this capacity you can comfortably run GPT-OSS-120B, GLM 4.5 Air, Qwen3.5-122B, and lower-parameter models with very large context windows. It also enables models from the MiniMax family, including lower-quantized versions of MiniMax-M2.5.
This is also the most competitive hardware category. Systems include the MacBook Pro, Mac Studio, Ryzen AI MAX+ 395 mini PCs from GMKtec and Minisforum, and NVIDIA GB10 systems. From our testing, NVIDIA GB10 delivers the fastest prompt processing, while Apple’s Ultra chips still provide the highest token generation speeds on the largest models.
What Can You Run with More Than 128GB?
Once you move beyond 128GB, Apple Silicon Ultra systems become the only practical option.
Configurations with 192GB, 256GB, and 512GB of unified memory make it possible to run models that simply do not fit anywhere else. We successfully tested Qwen3 235B A22B, MiniMax-M2.7 230B, DeepSeek V4 Flash 284B, GLM-4.7 355B, DeepSeek V3.1 671B, and GLM-5.2 744B, with the largest requiring approximately 467GB of memory at 4-bit quantization.
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.
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.
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.
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.
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.
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.