AI PC · mini-pc
GMKtec EVO-X2 (Ryzen AI Max+ 395) review
128GB of Strix Halo on Windows - though a 2026 memory shortage has knocked it off its value perch.
- Memory
- 128 GB
- Bandwidth
- 256 GB/s
- AI compute
- 50 TOPS
- £ / GB
- £25
What it runs
With 128 GB of unified memory, leaving the system its share and room for context, you can load at 4-bit a model up to roughly
~141B params
Verdict
A 128GB Strix Halo box that loads a 70B - once the value pick, but a 2026 memory shortage has pushed its 128GB model to about $3,500, level with the Framework and a few hundred below the Beelink.
BEST FOR
- + big models on Windows
- + buyers who catch it on one of GMKtec's frequent sales
NOT FOR
- - fast tokens on dense models
- - anyone who needs CUDA
- - value hunters at the current shortage price
The GMKtec EVO-X2 takes the Ryzen AI Max+ 395 and its 128GB of shared memory and wraps it in a mini-PC that, for most of its life, undercut nearly everything else with the same chip. That changed in 2026: an LPDDR5X memory shortage spiked its 128GB price to around $3,500-3,650. The rivals went up too, so it still undercuts the Beelink and sits level with the Framework, but it’s the capacity that sells it now rather than a bargain price.
The take
This used to be the value pick of the 128GB boxes, plain and simple - same Strix Halo chip as the Beelink, same 128GB you can hand mostly to the graphics side, and it routinely sold for around half what the premium rivals asked. The 2026 memory shortage changed that: the EVO-X2’s 128GB SKU jumped to around $3,500-3,650, and while the Beelink rose further, to $4,199, the gap is nothing like the half-price gulf it was. It still won’t win a speed contest - the memory tops out near 256 GB/s in theory and lower in practice, so a dense 70B answers at a walk - and the capacity remains superb. But buy it now for capacity, and only for value if you catch one of GMKtec’s regular sales that drops it back toward its old price.
What it’ll actually run
The Radeon 8060S draws on the shared pool, so with the memory split set generously you can put most of the 128GB toward models. That means a 70B at Q4 fits with room for context, and big mixture-of-experts models are where it shines. GMKtec’s own figures on the 128GB unit have gpt-oss-120b around 19 tokens a second, a Qwen3 30B MoE near 55, and even a 235B MoE ticking over about 11 - impressive for the money. Dense models are the slow lane: a DeepSeek R1 32B lands under 10 tokens a second, so temper expectations there.
The bottleneck is bandwidth, same as every Strix Halo box. Around 256 GB/s on paper, and real-world measurements come in lower, so dense large models feel measured rather than quick. There’s a 50 TOPS NPU on board too, but no local LLM tooling makes proper use of it yet, so the GPU does the work. Software is the reassuring part: it ships with Windows 11, runs Linux happily, and the AMD stack keeps improving. You’ll want to set the memory split in the BIOS to feed the GPU, but that’s a one-time job.
Who should buy it
Buy it if capacity is the point and you catch it at a sensible price. It’s still a fine machine for loading big models at home on Windows or Linux, and at a sale price it’s back to being the value champion. At the current shortage price, though, check the Framework Desktop too, which Framework lists at a similar $3,449 for 128GB (out of stock at the time of writing).
Look elsewhere if you need speed or CUDA. If your models fit in 24GB a used 3090 runs them faster for less, and if you’re tied to Nvidia’s tooling none of it lives here. The EVO-X2 is still the most model you can load in a tidy little Windows box - just no longer automatically the cheapest way to do it.
Starting settings
Starting configurations, each with where its figure comes from. A sensible starting point, not gospel - tune to your own kit.
Biggest model that fits
gpt-oss-120b MoE (~60GB); BIOS memory split set high
GMKtec's 128GB figure is about 19 tokens/s; up to 96-112GB of the pool can go to the GPU.
Everyday sweet spot
Qwen3 30B MoE, 8B-32B Q4
Qwen3 30B around 55 tokens/s, gpt-oss-20b around 57; comfortable for daily work.
Dense large model
DeepSeek R1 32B / Llama 70B Q4
Dense 32B under 10 tokens/s; a dense 70B fits but drags. MoE models are far happier here.
Backend tip
llama.cpp with the Vulkan backend on Windows, or Lemonade on Linux
Vulkan tends to beat ROCm on Strix Halo; set the BIOS memory split first.
Notes from people who've run one
Each note below names its source underneath.
- “
GMKtec's published 128GB benchmarks: gpt-oss-120b about 19.25 tokens/s, gpt-oss-20b about 57, Qwen3 30B about 55, DeepSeek R1 32B about 9.3, and a 235B MoE about 11. MoE models fly, dense models drag.
- “
The 256 GB/s is the theoretical ceiling for Strix Halo; real-world measured bandwidth lands lower, around 210-215 GB/s, and that is what caps token speed on large dense models.
- “
The 50 TOPS NPU can't yet be used for local LLM inference; the Radeon 8060S iGPU does the work, and Vulkan often beats ROCm on this chip.
3 claim(s) we couldn't fully verify
- · memory_bandwidth_gbs 256 - GMKtec doesn't publish a bandwidth figure; 256 GB/s is the Strix Halo platform theoretical (LPDDR5X-8000, 256-bit), and real-world measurements land nearer 210-215 GB/s.
- · power_w 140 - 140W is the vendor's peak figure; sustained is 120W, and the bundled charger is rated about 230W.
- · Price around GBP 3,200 (USD 3,500 verified on gmktec.com 2026-11-12) - USD matches GMKtec's own store ($3,499.99 for 128GB + 1TB). The Sept claim that the Beelink sat near $1,999 is not borne out by bee-link.com ($4,199), so the 'dearer than the Beelink' framing was corrected. The ~1,999 pre-shortage price was not re-checked. GBP 3,200 is a Sept estimate, not re-checked against a UK store.
Sources we drew on
The spec sheets, vendor pages and reviews behind this page.
- GMKtec EVO-X2 product page (vendor spec + 128GB benchmarks)GMKtec · primary source
- Beelink GTR9 Pro product page (store price comparison)Beelink · primary source
- Framework Desktop configurator (store price comparison)Framework · primary source
- AMD Ryzen AI Max+ 395 product specificationsAMD · primary source
- Strix Halo (Ryzen AI Max+ 395) LLM Benchmark Resultslhl (Level1Techs forum) · forum
Common questions
How much memory can the EVO-X2 give to models?+
Up to 128GB is shared between the CPU and the Radeon 8060S graphics. You set the split in the BIOS, and with it set generously the graphics side can take most of the pool, which is what lets a 70B at Q4 fit with room for context.
Is the GMKtec EVO-X2 fast for local LLMs?+
It depends on the model. Mixture-of-experts models are quick - GMKtec's own figures have gpt-oss-120b near 19 tokens a second - but dense models are the slow lane, with a DeepSeek R1 32B under 10. Memory tops out near 256 GB/s in theory and lower in practice, so that's the ceiling.
Does it need CUDA to run AI models?+
No. It's an AMD machine, so it runs on Vulkan or ROCm with standard tools like llama.cpp and Ollama on Windows or Linux. If your workflow depends specifically on Nvidia CUDA, this isn't the box for you.
Is the EVO-X2 still cheaper than the Beelink GTR9 Pro?+
Yes, though not by the margin it used to. The 2026 LPDDR5X memory shortage pushed the 128GB EVO-X2 to $3,499.99 on GMKtec's own store, but Beelink's price rose too and the GTR9 Pro lists at $4,199. So it still undercuts the Beelink by about $700; it's just no longer the bargain it once was.
