For modest local writing with a smaller quantized model, 16 GB of system or unified memory is a sensible starting point—not a guarantee for every model. Eight gigabytes may work on an Apple Silicon Mac with a smaller model and modest context, while larger models, longer context, and other open apps call for more headroom. Storage depends on the model files you download and keep; there is no universal minimum capacity.
How much RAM do you need?
LM Studio recommends 16 GB or more for Apple Silicon Macs and at least 16 GB for Windows. Its guidance says an 8 GB Mac may still run smaller models with modest context sizes. These are starting recommendations, not guarantees: the model, runtime, context setting, and available memory all matter. See LM Studio’s system requirements.
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Do not size a computer by model parameter count alone. The exact model file and its quantization affect weight memory, and the program also needs memory for the context and runtime. Your operating system and other open applications compete for the same system memory. The official sources do not establish a universal RAM-per-parameter formula.
The Tool Desk
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- 8 GB: A constrained option. LM Studio identifies smaller models and modest context as the conditions under which an 8 GB Apple Silicon Mac may be usable. Memory available to the model will be less than the installed total once the operating system and other apps are running.
- 16 GB: A sensible entry point for modest local use, consistent with LM Studio’s recommendations for Apple Silicon and Windows. Check the actual model file and context rather than assuming every model will fit.
- More than 16 GB: Consider more memory if your chosen model or context does not fit, or you need to keep other demanding applications open. The exact amount depends on the model, runtime, context, and hardware.
How much storage do model files take?
Local inference requires downloading the model files before use. Disk needs therefore depend on which models you choose and how many you keep. Check the file sizes for your intended downloads and add them together; leave room for updates, other software, and normal computer use. LM Studio describes downloading model weights before running a local model in its download documentation.
#1 Best Overall
- 【High-Performance APU】The MS-S1 MAX features an AMD Ryzen AI Max+ 395 APU, integrating a Zen 5 architecture CPU (up to 5.1GHz, 16C/32T, 64M L3 Cache), an RDNA 3.5 GPU, and an NPU (50 TOPS). The total system output is 126 TOPS. It provides powerful parallel computing capabilities for demanding AI workflows. It is ideal for running local LLMs, multimodal models, and computationally intensive tasks
- 【128GB UMA Memory】Equipped with up to 128GB of LPDDR5x-8000MT/s unified memory, it enables the CPU and GPU to access a shared, high-bandwidth memory pool with extremely low latency. Ideal for large-scale AI inference, 3D workloads, and complex timelines in video editing. It eliminates traditional VRAM bottlenecks, ensuring smoother data transfer during high-intensity computations. The UMA design maximizes performance stability under high loads
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- 【Cluster Design】Two MS-S1 MAX units can be configured as a dual-unit cluster to run a large 235B Q4 model locally, achieving an output speed of 10.87 tok/s. Supporting 2U rack deployment, multiple MS-S1 MAX units can be cascaded into a distributed cluster to create a high-efficiency AI computing center. A cluster of four MS-S1 MAX units successfully ran a DeepSeek-R1 671B Q4 large model. A reserved cluster power-on interface allows for unified start-up and shutdown
Quantization can reduce model weight size and memory use, with trade-offs that vary by model and quantization. The llama.cpp project supports GGUF models and integer quantization from 1.5-bit through 8-bit. A model’s disk file size is a useful clue, but it is not the full runtime memory requirement: context and runtime overhead also count.
Neither LM Studio nor llama.cpp establishes one disk-capacity minimum that works for everyone. An external SSD can be an optional place to keep model files when internal storage is tight; it is not a requirement for running a local model.
Rank #2
- Built for Local AI and Advanced Workflows – The BOSGAME M5 AI Mini PC is powered by AMD Ryzen AI Max+ 395 with 16 cores, 32 threads, up to 5.1GHz, 50 TOPS NPU performance and up to 126 TOPS total AI performance. It is designed for local AI inference, private AI assistants, coding, data analysis, virtualization, content creation and demanding multitasking while keeping sensitive data on the device.
- 128GB Unified Memory for Large Models and Creative Projects – M5 includes 128GB LPDDR5X-8000 unified memory, giving the CPU and Radeon 8060S graphics access to a large shared memory pool. This helps support memory-intensive AI workloads, large project files, multiple virtual machines, 3D work, video editing and complex professional applications without the capacity limits of typical 32GB or 64GB mini computers.
- Radeon 8060S Graphics for Creation, Rendering and Gaming – Integrated Radeon 8060S graphics with 40 RDNA 3.5 compute units delivers high-end visual performance without a separate graphics card. Use the M5 creator workstation for 4K video editing, 3D rendering, CAD, AI image workflows, high-resolution media and modern gaming, while maintaining a compact desktop footprint.
- 2TB PCIe 4.0 SSD and Flexible Expansion – A pre-installed 2TB NVMe PCIe 4.0 SSD provides fast access to models, datasets, media libraries and project files. A second M.2 2280 PCIe 4.0 slot allows additional storage expansion, while the SD 4.0 card reader supports efficient photo and video workflows for creators and production teams.
- Professional Connectivity and Four-Display Support – Dual USB4 ports, HDMI 2.1 and DisplayPort 1.4 support up to four displays and resolutions up to 8K@60Hz. WiFi 7, Bluetooth 5.4 and 2.5GbE deliver fast networking for cloud collaboration, NAS access and business deployment. Windows 11 Pro, performance-mode switching, Wake-on-LAN and auto power-on support flexible workstation use.
Why context length and GPU memory matter
Longer context increases memory pressure because the runtime must manage the conversation context in addition to model weights. A notable example is AMD’s July 29, 2025 demonstration of Llama 4 Scout at a 256,000-token context on a specific Ryzen AI Max+ 395 system with 128 GB of memory, Flash Attention enabled, and an 8-bit KV cache. AMD also described a 4,096-token default context for LM Studio at that time. These are details of a dated vendor demonstration and software configuration, not ordinary writing requirements or universal sizing rules. See AMD’s July 2025 description.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsA dedicated GPU is not an absolute prerequisite. LM Studio recommends at least 4 GB of dedicated VRAM for Windows, but that does not mean a particular model will fit entirely in VRAM. llama.cpp supports CPU inference as well as CPU-plus-GPU hybrid inference, allowing work to be divided when a model exceeds available GPU memory. The backend and available GPU memory affect how inference is handled and its performance; RAM alone does not predict writing speed.
Quick Recap
Best Value
- 【Leading AI Mini Workstation】MINISFORUM AI MS-S1 Max Workstation comes with AMD Ryzen AI Max+ 395 processor, which uses AMD's latest generation Zen 5 architecture. It has 16 Cores and 32 Threads, the boost clock is up to 5.1GHz. The overall processor performance is up to 126 TOPS, and the NPU performance reaches up to 50 TOPS. AMD Ryzen AI enables improved productivity, advanced collaboration, and improved efficiency.
- 【AMD Radeon 8060S Graphics 】The MS-S1 Max Mini PC equipped with AMD Radeon 8060S Graphics which built on the new generation of RDNA 3.5 architecture AMD graphics, it brings ultra-high frame rate experiences and advanced content creation features anywhere and delivers staggering performance. It can handle all your computing and multimedia tasks efficiently.
- 【Five 8K Video Output】This MS-S1 Max Workstation comes with five video outputs, 1x HDMI (8K@60Hz), 2x USB4(40Gbps,Alt DP2.0,PD out 15W) and 2x USB4 V2(80Gbps,Alt DP2.0,PD out 15W) Outputs, which support multiple monitors display at the same time and provide a larger and wider filed of view and improve your work efficiency. It is used in fields that require high-performance computing and graphics processing, including digital signage and securities trading, as well as work that uses CAD, such as engineering design, scientific calculations, animation production, and post-production for movies and television
- 【 Fast and Stable Wire & Wireless Speed】It comes with Two 10G Lan Ports for wired connection and and Wi-Fi 7 / BT5.4 for wireless connection, which increased the network speed greatly and expand its functions and improved performance of computer to a large extent and allows you to use more networks such as software routers (OpenWRT / DD-WRT / Tomato etc.), firewalls, NAT, network isolation etc.
- 【Large Storage & Flexible Expandability】This Workstation equipped with 64GB LPDDR5-8000MHz + 2TB M.2 2280 PCIe4.0 SSD. There is another PCIe4.0 SSD slot available for up to 8TB, these SSD slots are compatible with RAID0 and RAID1, you can store movies, videos, photos, important files easily. What’s more, it also comes with 1x standard PCIex16 slot(PCIe4.0x4) inside.
Rank #4
- Unlock next-generation AI computing with AMD Ryzen AI Max+ 395 processor featuring 16 cores, 32 threads, up to 5.1GHz boost clock, and integrated Ryzen AI engine delivering up to 126 TOPS AI performance. EVO-X3 is designed for local AI models, content creation, development, and professional workloads.
- OCuLink External GPU Expansion – Upgrade Beyond a Mini PC: Take your graphics performance further with a dedicated OCuLink (PCIe 4.0 x4) interface. Connect an external GPU dock to add desktop-class graphics power for AAA gaming, AI acceleration, 3D rendering, video production, and advanced creative applications. EVO-X3 gives you the flexibility of a compact PC with workstation-level expansion capability.
- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
Rank #3
- Built for Local AI Development: AMD Ryzen AI Halo is designed for local AI development and inference, featuring 128GB unified memory and support for up to 200B parameter models to build and run intensive AI workloads locally.
- 128GB Unified Memory: Features 128GB LPDDR5x unified memory at 8000 MT/s with 256 GB/s memory bandwidth, providing a shared memory pool across the CPU, GPU, and NPU to support larger AI models.
- AMD Ryzen AI Max+ 395 Processor: Features 16 cores, 32 threads, and Zen 5 architecture, paired with AMD Radeon 8060S integrated graphics featuring 40 RDNA 3.5 compute units and an AMD XDNA 2 NPU with up to 50 TOPS.
- Linux AI Developer Platform: Purpose-built for Linux-based AI development with full AMD ROCm software support and preloaded tools, models, and workflows optimized for local AI development.
- Compact, Connected Design: Includes a 2TB M.2 SSD, 10GbE LAN, Wi-Fi 7, Bluetooth 5.4, USB-C connectivity, and HDMI 2.1b.
How to size a computer for your writing setup
- Pick the model first. Look up the exact file size and quantization of the model you plan to run. Do not infer its complete memory needs from parameter count alone.
- Choose a context length. Decide how much conversation or source text you expect to keep in context. Longer context needs more memory; avoid treating a maximum context advertised for a specific machine as a general target.
- Check the runtime and hardware. Compare available system or unified memory, dedicated GPU VRAM, and support for the runtime’s backend. CPU inference is possible with llama.cpp; hybrid CPU/GPU operation is another option, with performance depending on the configuration.
- Budget disk from actual downloads. Add the sizes of the model files you intend to keep, then reserve additional space for updates and other uses. If internal space is limited, an external SSD is an option.
- Check upgradeability and workload evidence. Confirm whether your computer’s memory can be upgraded before buying RAM. Desktop upgrade options do not automatically apply to laptops or Apple Silicon systems. If speed matters, seek reliable results for the exact model and intended workload; the cited official guidance does not provide a fair cross-device writing-speed comparison.
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