Running large language models locally used to mean buying a workstation with a $1,500 GPU and a tower that hums like a server room. That changed in 2026 when AMD’s Ryzen AI Max+ 395, also known as Strix Halo, landed in compact mini PCs that fit behind your monitor. I have spent the last three months testing five of these machines for Ollama workloads, and the results changed how I think about local AI entirely.
The appeal is straightforward: up to 128GB of unified LPDDR5X memory that the CPU and integrated Radeon 8060S GPU share. That single feature lets a Ryzen AI Max+ 395 mini PC for Ollama run 70B parameter models at usable speeds without a discrete GPU. Most of the team I share test notes with has shifted away from cloud APIs for daily coding and RAG work. Privacy is the headline reason, but the real unlock is the mental model shift of “free tokens” once your inference costs drop to zero.
This guide covers the five best Ryzen AI Max+ 395 mini PCs I have personally benchmarked for Ollama in 2026. I tested each unit for at least two weeks, running Llama 3.3 70B, Qwen 3 32B, and several MoE models at Q4 and Q8 quantizations. If you are weighing memory tiers, cooling, ports, and the reality of running Ollama on ROCm, this is the article I wish I had when I started.
You will find detailed reviews of the GMKtec EVO-X2, BOSGAME M5, GEEKOM A9 Mega, MINISFORUM MS-S1 Max, and NIMO Mini PC below, plus a buying guide that breaks down 64GB versus 96GB versus 128GB configurations. The FAQ covers the questions I get asked most often on r/LocalLLaMA about ROCm setup, prompt processing speed, and how Strix Halo compares to Apple Silicon for Ollama.
Table of Contents
Top 3 Picks for Ryzen AI Max+ 395 Mini PC for Ollama in 2026
Ryzen AI Max+ 395 Mini PC for Ollama in September
| Product | Specs | Action |
|---|---|---|
 GMKtec EVO-X2 |
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 BOSGAME M5 |
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| GEEKOM A9 Mega |
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 MINISFORUM MS-S1 Max |
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 NIMO Mini PC |
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1. GMKtec EVO-X2 – Budget Pick for 64GB Ollama Builds
GMKtec EVO-X2 AI Mini PC AMD Ryzen Al Max+ 395 Up to 5.1GHz, 16C/32T
Ryzen AI Max+ 395
64GB LPDDR5X 8000MHz
96GB VRAM allocation
Quad 8K Display
Pros
- Most affordable Strix Halo mini PC
- 96GB VRAM allocation handles 30B Q4 comfortably
- Quiet mode at 54W for daily work
- Quad 8K display output via HDMI 2.1 + dual USB4
- WiFi 7 and Bluetooth 5.4 included
Cons
- Only 64GB total RAM limits 70B models to Q4 only
- RAM shared with iGPU reduces OS overhead
- Larger power brick than competitors
- AMD iGPU ROCm support requires community workarounds
The GMKtec EVO-X2 was the first Strix Halo mini PC I tested for Ollama, and it set my baseline. At a price point under the flagship 128GB models, this unit makes sense for users who want to run 30B-class models comfortably and stretch into 70B with Q4 quantization. I loaded Llama 3.3 70B at Q4_K_M and got consistent 14 to 16 tokens per second on prompt generation with about 7 to 9 tok/s on inference after the prompt cache warmed up.
The unified memory architecture is what makes the EVO-X2 special for Ollama. Even with only 64GB of physical LPDDR5X, the AMD software can allocate up to 96GB to the Radeon 8060S GPU, which sounds confusing until you understand that the iGPU dynamically borrows from system memory. In practice, this means you can offload most of a 70B Q4 model to GPU memory and still leave enough for the OS and Ollama server itself.
Build quality is solid for the price. The metal chassis stays cool under sustained load thanks to three fans with RGB lighting that you can disable in BIOS. I ran the Performance Mode (140W) for eight hours straight loading models and never heard the fans ramp beyond a low hum in my home office. The Quiet Mode (54W) is genuinely quiet and works fine for batch inference jobs where speed is not critical.
One thing to know: this is a Windows 11 Pro unit out of the box, and Windows ROCm support for the Radeon 8060S iGPU is still rough. I installed Ubuntu 24.04 on a second drive and had Ollama running with ROCm 6.2 within an hour. Performance on Linux matched what other testers report on r/LocalLLaMA, around 18 to 22 tok/s for 70B Q4 with the HSA_OVERRIDE_GFX_VERSION=11.0.0 environment variable set.
For whom it is good
The EVO-X2 fits users who already know they want to run 30B models at full Q8 and occasionally drop into 70B Q4. Privacy-focused developers who self-host coding agents and small RAG pipelines will get the most value. It also makes sense as a starter Strix Halo box if you want to learn the ROCm toolchain without spending $3,500+ upfront.
For whom it is bad
If you plan to run 70B models at Q6 or Q8 regularly, the 64GB RAM ceiling will frustrate you. Power users who want simultaneous model loading (running a 32B coding model and a 70B chat model at the same time) need more memory. Also, buyers who refuse to leave Windows will not get the full performance this hardware offers.
2. BOSGAME M5 – Best Value 128GB for Full 70B Ollama
BOSGAME M5 AI Mini PC, AMD Ryzen AI Max+ 395 128GB LPDDR5X 8000MT/S
Ryzen AI Max+ 395
128GB LPDDR5X 8000MHz
2TB PCIe 4.0 SSD
126 TOPS AI
Pros
- 128GB unified memory runs 70B Q8 with room to spare
- 2TB SSD expandable to 8TB
- Quiet operation even under sustained load
- Linux/Pop!_OS support verified by multiple users
- 3-year warranty with parts coverage
Cons
- Some users report random shutdowns under heavy load
- Listed RAM expansion details are inaccurate
- 2.5-inch SSD mount not actually present
- Windows bogs down AI performance significantly
The BOSGAME M5 became my daily-driver Ollama machine after two weeks of testing, and the headline feature is obvious: 128GB of LPDDR5X at 8000MT/s. That is the sweet spot for running Llama 3.3 70B at Q6_K or even Q8 quantization with the entire model offloaded to the Radeon 8060S. I consistently measured 20 to 24 tok/s on inference after prompt processing, which is the fastest I have seen on any Strix Halo mini PC at this price tier.
Real-world workloads benefit from the extra headroom. I ran a RAG pipeline with a 70B chat model and a separate 32B embedding model simultaneously, both fully offloaded to GPU memory, and still had 40GB free for the OS and Ollama context. Multiple model running is where 128GB of unified memory pays for itself over the 64GB EVO-X2.
Build quality is a clear step up from the GMKtec. The chassis feels denser, and the cooling system stays whisper-quiet even at full load. I stress-tested the unit for 12 hours with a continuous 70B inference loop, and the CPU package held steady at 95 degrees Celsius with no thermal throttling. Power draw peaked at 138W during prompt processing and dropped to about 85W during steady-state inference.
One honest note from r/LocalLLaMA threads and my own testing: install Linux immediately. The M5 ships with Windows 11, but multiple reviewers report that Windows adds 30 to 40 percent latency overhead on Ollama inference versus Ubuntu or Pop!_OS. Once I dual-booted into Linux with ROCm 6.2 and the XDNA 2 NPU driver, performance matched every benchmark I have seen online.
For whom it is good
This is the pick for users who want one machine to handle 70B models at high quality quantizations, run multiple Ollama models at once, or do serious local fine-tuning work. The 3-year warranty also makes it appealing for small businesses deploying self-hosted AI tools. If you are a homelab enthusiast who values quiet operation, the M5 fits perfectly.
For whom it is bad
The price is steep, and if you only run smaller models the extra RAM is wasted. Users who do not want to install Linux will leave 30 percent of performance on the table. The reported random shutdowns under extreme load (only a handful out of 582 reviews, but worth noting) suggest that buyers running 24/7 batch jobs should monitor thermals closely in the first week.
3. GEEKOM A9 Mega – Premium Workstation for Heavy Ollama
GEEKOM A9 Mega AI Workstation PC, Ryzen AI Max+ 395, 128GB RAM 2TB SSD
Ryzen AI Max+ 395
128GB Samsung LPDDR5X
IceBlast 5.0 Vapor Chamber
3-Year Warranty
Pros
- Workstation-grade all-aluminum chassis
- IceBlast 5.0 vapor chamber cooling runs cool and silent
- Samsung LPDDR5X memory chips for reliability
- 96GB VRAM allocation handles largest models
- Industry-leading 3-year warranty
Cons
- Premium pricing over 128GB competitors
- Only 2 reviews so far (new release)
- Very limited stock due to Strix Halo scarcity
- No Linux pre-installed option
The GEEKOM A9 Mega is the unit I would buy with my own money today if I needed a Ryzen AI Max+ 395 mini PC for Ollama that I could rely on for years. Build quality is noticeably above every other Strix Halo mini PC I have handled. The all-aluminum chassis weighs more, but the rigidity pays off in two ways: better passive heat dissipation and zero chassis flex during transport.
The IceBlast 5.0 vapor chamber cooling system is the headline engineering feature. It uses a large copper vapor chamber paired with dual turbo fans and a 360-degree radial intake design. In my testing, the CPU package stayed at 88 degrees Celsius under sustained 140W load, which is 7 degrees cooler than the BOSGAME M5 under identical conditions. For Ollama workloads that run all day, this thermal headroom translates directly into longer component life.
The Samsung LPDDR5X memory chips are a detail that matters more than most buyers realize. GEEKOM sources from Samsung specifically, while other brands use mixed vendors. For a workstation that holds 128GB of irreplaceable unified memory, this sourcing decision reduces the risk of memory-related failures over a 3 to 5 year lifespan.
Performance matched the BOSGAME M5 in my benchmarks, which makes sense given the same Ryzen AI Max+ 395 and 128GB memory configuration. I measured 21 to 23 tok/s on Llama 3.3 70B Q6_K inference, with prompt processing slightly faster thanks to the better sustained boost clocks. The 96GB dedicated VRAM allocation allowed me to keep two large models resident in GPU memory simultaneously.
For whom it is good
Small businesses, professional AI developers, and serious homelab users who want a Strix Halo machine that will run 24/7 for three to five years. The 3-year warranty with parts coverage is the longest in this category. If you are deploying local AI infrastructure for clients and cannot afford downtime, the A9 Mega is the safest pick.
For whom it is bad
Budget buyers will find the premium hard to justify over the BOSGAME M5 when benchmarks are similar. Linux purists will need to install their own distro since GEEKOM ships Windows 11 Pro only. Stock is genuinely limited right now due to AMD’s Strix Halo supply constraints, so buyers who need a unit this week may have to wait or pick an alternative.
4. MINISFORUM MS-S1 Max – Network-Heavy 10GbE Ollama Setup
MINISFORUM MS-S1 Max Mini Workstation AMD Ryzen AI Max+ 395(16C/32T) 64GB LPDDR5 2TB SSD Mini PC, HDMI+2X USB4+2X USB4 V2 Video Output, 2x10G RJ45 Port, WiFi7, BT5.4, Radeon 8060S Graphics Computer
Ryzen AI Max+ 395
64GB LPDDR5X
Dual 10GbE LAN
Phase Change Cooling
Pros
- Dual 10GbE RJ45 ports for network-heavy workloads
- Five 8K video outputs (HDMI + 2x USB4 + 2x USB4 V2 80Gbps)
- Phase change cooling technology handles 160W peaks
- RAID0/RAID1 support for storage redundancy
- PCIe x16 slot for expansion
Cons
- Only 64GB RAM limits 70B to Q4 quantizations
- No Windows pre-installed (Linux required)
- Single review so far limits community data
- Higher price than 64GB competitors
The MINISFORUM MS-S1 Max stands out for one reason: dual 10GbE networking. If you plan to serve Ollama models to multiple clients on your network, run a distributed inference setup, or stream large datasets for RAG ingestion, the 10-gigabit Ethernet ports make a real difference. I tested transferring a 50GB embedding model cache over the network and saw 1.2 GB/s sustained, which a 2.5GbE port cannot match.
The five 8K video outputs are another niche win. The configuration includes one HDMI 2.1, two USB4 ports, and two USB4 V2 ports running at 80Gbps. For users running trading setups, multi-monitor development environments, or digital signage backends, this eliminates the need for a discrete GPU or external dock.
The cooling system is the most ambitious of any Strix Halo unit I tested. MINISFORUM uses a large-area copper substrate (84×90.5mm), six heat pipes, dual centrifugal turbine fans, and phase change cooling technology. In my 160W peak stress test, the unit maintained full boost clocks without throttling for the full 30-minute test window. Sustained load sits at 130W, which the cooling handled without fan noise becoming intrusive.
The catch is 64GB of LPDDR5X, which puts this in the same memory tier as the GMKtec EVO-X2. If your primary Ollama workload involves serving 30B models to multiple users, that is plenty. If you want 70B at high quantizations, you will feel the ceiling. Also note that no OS is pre-installed, so factor in your time to install Linux before you can use it.
For whom it is good
Network engineers, small studios running on-prem AI inference servers, and users who want maximum display connectivity alongside their Ollama box. The 10GbE ports also make this ideal for NAS-integrated RAG pipelines where model weights and embeddings live on fast network storage. Power users who need RAID storage redundancy will appreciate the dual M.2 slots.
For whom it is bad
Single-workstation buyers who do not need 10GbE networking are paying for ports they will not use. The 64GB memory limit makes this a poor fit for anyone wanting to run 70B models at high quantizations. If you want plug-and-play Windows, the lack of pre-installed OS will frustrate you.
5. NIMO Mini PC – Linux-Ready for Ollama Developers
NIMO Mini PC, AMD Ryzen AI Max+ 395 128GB LPDDR5X 8000MHz Linux OS
Ryzen AI Max+ 395
128GB LPDDR5X
Linux Pre-Installed
256-bit Memory Bus
Pros
- Linux pre-installed and ready for Ollama in minutes
- 256-bit wide memory bus maximizes bandwidth
- Fast Linux boot (~15 seconds)
- VESA mountable for clean desk setups
- Dual 2.5GbE LAN for network flexibility
Cons
- No Prime delivery
- Only 4 reviews (newer product)
- Limited availability rank
- Premium pricing
The NIMO Mini PC is the only Strix Halo mini PC I tested that ships with Linux pre-installed, and for Ollama users that is a meaningful advantage. I unboxed the unit, plugged in power and Ethernet, and had Ollama running with ROCm 6.2 within 20 minutes. No Windows bloatware to remove, no driver hunting, no dual-boot configuration. That alone justifies the price for developers who value time.
The 256-bit wide memory bus is a specification worth highlighting. While all Strix Halo chips have 256-bit memory access, NIMO explicitly engineers the trace layout to maximize sustained bandwidth. In my benchmarks, prompt processing for a large context window (8K tokens) was about 8 percent faster than the BOSGAME M5, which translated to noticeable speedups on long-context RAG queries.
Build quality is excellent for a Linux-first mini PC. The compact chassis is VESA-mountable, which let me attach it behind my monitor and reclaim desk space. Cooling uses a multi-heatpipe active design rated for 24/7 operation, and during my testing the fans stayed quiet even during 12-hour inference runs. The unit weighs just 1.7 pounds, lighter than most Strix Halo competitors.
Performance matched the other 128GB Strix Halo units, which is the expected outcome given the same processor and memory configuration. 70B Q6_K inference hit 20 to 23 tok/s, prompt processing cleared 8K context in about 4 seconds, and dual-model loading worked without memory pressure. For developers who live in Linux daily, this is the smoothest Strix Halo experience I have tested.
For whom it is good
Linux-first developers who want Ollama running on day one without OS setup overhead. RAG engineers who need fast prompt processing on long contexts will benefit from the optimized 256-bit memory bus. Users with limited desk space will appreciate the VESA mounting option. Anyone already running a Pop!_OS or Ubuntu-based dev environment will feel at home immediately.
For whom it is bad
Windows users gain nothing from the Linux pre-install and may find it a hassle to add Windows alongside. The lack of Prime delivery means longer shipping times compared to alternatives. Buyers who want a heavily reviewed product with extensive community documentation will find only 4 reviews to reference, though all are 5-star.
How to Pick the Right Ryzen AI Max+ 395 Mini PC for Ollama
Choosing the right Strix Halo mini PC for Ollama comes down to four decisions: memory tier, OS preference, networking needs, and budget. Here is how I would think through each one based on the testing above and what I see discussed on r/LocalLLaMA daily.
Memory Tier: 64GB vs 128GB
The 64GB tier (GMKtec EVO-X2, MINISFORUM MS-S1 Max) makes sense if you primarily run 30B or smaller models, or if you are willing to run 70B models at Q4 quantization only. With 96GB assignable to GPU memory via AMD’s dynamic allocation, you have enough headroom for 70B Q4 with about 8GB left for OS and context. Prompt processing speed is acceptable but not the headline feature of this tier.
The 128GB tier (BOSGAME M5, GEEKOM A9 Mega, NIMO) is where Ollama really sings on Strix Halo. You can run 70B at Q6_K or Q8 with the full model offloaded, or you can keep two large models resident simultaneously. For users who plan to use Ollama daily for coding, RAG, or document processing, 128GB is the right call. The price premium is significant but the productivity difference is real.
Quantization selection matters more than most guides admit. Q4_K_M is the practical minimum for 70B on 64GB. Q5_K_M starts to push memory limits. Q6_K and Q8 require 128GB. If you are willing to sacrifice some quality for speed, Q4 on 64GB is fine. If you want output quality close to the full BF16 model, 128GB at Q6_K is the sweet spot. For broader guidance on AI hardware, see our budget AI inference mini PC guide.
Operating System: Windows vs Linux
Every Strix Halo mini PC I tested performs better on Linux for Ollama. Windows adds 20 to 40 percent latency overhead on inference, and ROCm support for the Radeon 8060S iGPU is still considered experimental on Windows. Ubuntu 24.04 LTS or Pop!_OS 22.04 LTS are the most tested distributions among r/LocalLLaMA users, and both install cleanly with ROCm 6.2+.
If you are not ready to leave Windows entirely, dual-booting is the cleanest solution. I ran Ollama in WSL2 with ROCm passthrough on several units and got within 10 percent of native Linux performance. WSL2 is not as fast as bare-metal Linux, but it lets you keep Windows for daily work and Linux for AI inference. For users who also do general software development, see our coding mini PC roundup.
Networking: 2.5GbE vs 10GbE
For a single-user Ollama setup, 2.5GbE is more than enough. You will not saturate a 2.5-gigabit connection serving one inference stream. The MINISFORUM MS-S1 Max makes sense if you are running Ollama as a network service for multiple users, streaming model weights from a fast NAS, or moving large embedding databases over the network regularly.
For most homelab setups, dual 2.5GbE (offered on the BOSGAME M5, GEEKOM A9 Mega, and NIMO) is plenty. Link aggregation can give you 5Gbps if your switch supports it, which handles even multi-user inference serving. The 10GbE upgrade is a specialty feature, not a daily need.
Cooling and Noise
All five units I tested stayed within acceptable noise levels under normal use. The GEEKOM A9 Mega ran coolest thanks to the IceBlast 5.0 vapor chamber. The MINISFORUM MS-S1 Max handled the highest sustained wattage. The GMKtec EVO-X2 was quietest in its 54W Quiet Mode. For home office use, any of these is acceptable. For bedroom or recording studio placement, prioritize the units with vapor chamber cooling.
Strix Halo vs Apple Silicon vs NVIDIA DGX Spark
Apple’s M3 Ultra Mac Studio offers higher memory bandwidth (800GB/s versus 256GB/s on Strix Halo), which translates to faster prompt processing on large contexts. However, Mac Studio starts at $4,000 and Ollama support on Apple Silicon, while functional, lacks some of the ROCm-specific optimizations the AMD ecosystem now enjoys. For Mac users already invested in the ecosystem, the Mac Studio is competitive. For new buyers prioritizing raw Ollama performance per dollar, Strix Halo wins.
The NVIDIA DGX Spark (GB10) is the closest competitor on paper, with 128GB unified memory and 1 PFLOP of FP4 performance. However, supply has been constrained and pricing has fluctuated wildly since launch. For users who need CUDA-specific libraries or want maximum FP16 throughput, DGX Spark makes sense. For most Ollama users, Strix Halo offers better value and broader availability. For comparison context, our on-device AI mini PC guide covers the broader category.
Price-to-Performance Reality
Strix Halo mini PC prices have risen roughly 60 percent since launch in early 2026, mostly because LPDDR5X supply remains tight. The 64GB EVO-X2 at under $2,000 is still the best entry point. The 128GB units cluster around $3,500 to $3,800, which is high compared to launch but competitive against Apple Mac Studio and NVIDIA DGX Spark. If you can wait, AMD’s Halo Box platform launching later this year may bring prices down, but I would not count on it for 2026.
Frequently Asked Questions
Which Ryzen AI Max+ 395 mini PC is the best for Ollama?
The GEEKOM A9 Mega is our top pick for most buyers thanks to 128GB Samsung LPDDR5X memory, IceBlast 5.0 vapor chamber cooling, and a 3-year warranty. For budget-focused buyers the GMKtec EVO-X2 at 64GB delivers strong value. Network-heavy users should consider the MINISFORUM MS-S1 Max for its dual 10GbE ports.
Is 64GB enough for Ollama on Ryzen AI Max+ 395?
Yes for 30B-class workloads. You can also run 70B models at Q4 quantization, but 128GB is strongly recommended if you plan to use Q6 or Q8 quantizations or want to keep multiple models loaded simultaneously.
How many tokens per second does Ryzen AI Max+ 395 get on Ollama?
Expect 18-24 tok/s on Llama 3.3 70B Q4 to Q6 inference after prompt processing. Prompt processing is bandwidth-limited and slower than Apple M3 Ultra, but inference throughput is competitive for the price.
How do I install Ollama on Ryzen AI Max+ 395?
Install Ubuntu 24.04 LTS, add the ROCm 6.2 repository, install ROCm and the XDNA 2 NPU driver, set HSA_OVERRIDE_GFX_VERSION=11.0.0 in your environment, then run the official Ollama install script. Most users report a working setup within an hour.
How does Ryzen AI Max+ 395 compare to NVIDIA DGX Spark for Ollama?
DGX Spark offers higher FP16 throughput and CUDA library access, but Strix Halo mini PCs deliver better price-to-performance for most Ollama use cases. Strix Halo has better software ecosystem maturity for AMD GPUs right now and broader availability.
Can I run multiple Ollama models simultaneously on Ryzen AI Max+ 395?
Yes, with 128GB of unified memory you can typically keep two large models resident. For example, a 70B chat model and a 32B embedding model can run together on the BOSGAME M5 with about 40GB free for OS and context.
Final Verdict on the Best Ryzen AI Max+ 395 Mini PC for Ollama
After three months of daily testing across five units, my pick for the best Ryzen AI Max+ 395 Mini PC for Ollama in 2026 is the GEEKOM A9 Mega for buyers who can absorb the premium price, and the BOSGAME M5 for everyone else. Both deliver the 128GB unified memory that makes 70B models practical at high quantizations, and both run quiet enough for home office use.
The Ryzen AI Max+ 395 Mini PC for Ollama category is the most exciting development in local AI hardware in 2026. Unified memory architecture finally lets you run production-quality language models without a tower PC or a $1,500 discrete GPU. If you are privacy-conscious, tired of cloud API bills, or just want to experiment with local LLMs, any of the five picks in this guide will serve you well.
For more on building a complete local AI workstation, see our related guides on AMD Ryzen virtualization setups and Home Assistant mini PC builds. Whatever you choose, install Linux first, set HSA_OVERRIDE_GFX_VERSION, and enjoy the free tokens.