Can a Strix Halo GMKtec EVO-X2, 128GB run local LLMs?

Yes — 39 of the 48 current models on this site fit in its 96 GB of usable memory. The strongest it holds, Qwen3.8 27B, runs at 10 tok/s with a 32k window, worked out from memory bandwidth. Whether that saves you money is a different question: at $3,500 and 500k tokens a day, it pays back in 267 years.

Run the numbers on this machine

What the GMKtec EVO-X2, 128GB runs

Memory never runs out first here. Every model below holds 256k, where the calculator's list ends, or the longest setting its own context limit allows, so the window you get is the model's choice rather than this machine's.

Speed and memory are at 32k context, the setting the calculator starts on; the memory column is the weights plus the cache for that much of it. Each speed says how it was arrived at, and every one here is estimated from this machine's 256 GB/s of memory bandwidth rather than taken from a published benchmark. There is a page on how that sum works, and what each size needs, and one on what 128 GB runs, machine by machine. The longest context is the longest setting the calculator offers that this machine still holds the model at, cache included, and each one opens the calculator on that model at that length. A figure tagged memory is one this machine ran out of room for, and the rest are stopped by the model's own limit or by the end of the list.

For scale, the calculator starts from 80 tok/s for a hosted API and times a local machine against it. Nothing above reaches it, and the quickest is 56 tok/s.

27 more fit. Ranked below the twelve above: Qwen3.5 9B, Nemotron 3.5 Lightning 30B-A3B, MiniCPM5 2B, Qwen3.5 4B, gpt-oss-120b, Ling 3.0 tiny, Granite 4.2 8B, Mistral Small 4 (119B-2603), Qwen3-Coder 30B-A3B, Qwen3-Coder Next, gpt-oss-20b, Gemma 4 E4B, Devstral 2 123B, LFM2.5 2.6B, Devstral Small 2 24B, Ministral 3 14B, Ministral 3 8B. The other 10 have no intelligence-index score, so the ranking cannot place them: Laguna XS 2.1, LFM3 1.7B, Ornith 1.5 9B, Ornith 1.5 35B-A3B, Ornith 1.5 122B, Spark-X2.5 4B, Spark-X2.6 4B, KAT-Coder V2.5 Dev 35B-A3B, LFM2.5 24B-A2B, Flint 40B-A2.5B. Their pages show what each one needs and what runs it.

The 48 counted above are the current models. This site prices 16 superseded ones as well, because people still run them, and this machine holds 15 of those at 32k, strongest first: GLM-4.5-Air, Qwen3 30B-A3B Instruct 2507, DeepSeek-R1-Distill-Qwen-32B, DeepSeek-R1-Distill-Llama-70B, Llama 3.3 70B Instruct, Llama 3.1 8B Instruct at Q4_K_M, Llama 3.1 8B Instruct at Q8_0, Mistral Small 3.2 24B Instruct, Qwen3 32B at Q4_K_M, Qwen3 32B at Q8_0, Qwen3 14B, Llama 4 Scout 17B-16E, Qwen3 8B, Gemma 3 27B it, Gemma 3 12B it. Each of those pages asks whether the model is still worth running.

What has been measured on the GMKtec EVO-X2, 128GB

Not one of the speeds above was measured on this machine, and that is not because nobody has run a model on one. This site holds five published speeds taken on this machine, three of them on superseded models the table above leaves out. They are below, as published and again at 32k of context, which is what every other speed on this page is taken at.

A published figure is what its source reports, at the window the model was run at. The 32k figure is that measurement read at 32k of context: every token generated reads the weights and the whole key-value cache, so the longer the window the slower it runs. The figures come from visorcraft/strix-halo-llm-perf.

Other machines to weigh against the GMKtec EVO-X2, 128GB

Every row uses the same defaults as the figures above: 500k tokens a day at 15:1 input to output, 32k context, and each machine's strongest model that fits, counted against the same 48 current models. The GeForce RTX 5090, 32GB is priced as the card alone, so add the PC around it before comparing it with a complete computer. All seven cards here are ranked by what each one holds.

Head to head with another computer: Mac mini M5 Pro, 24GB · Mac Studio M5 Max, 128GB · DGX Spark, 128GB · MacBook Air M5 (15-inch), 16GB · MacBook Pro M5 Pro (16-inch), 64GB · Framework Desktop, 128GB · Mac Studio M5 Max, 64GB. With a graphics card: RTX PRO 6000 Blackwell, 96GB · Radeon AI PRO R9700, 32GB. With the same machine at another memory size: 64GB. There are 111 machine match-ups on the site.

The specifics

Chip

GMKtec EVO-X2 — Ryzen AI Max+ 395 · Radeon 8060S, 40 CU

Memory bandwidth

256 GB/s — Measured GPU bandwidth is ~212-215 GB/s of the 256 GB/s theoretical, so estimates here run a little high.

Usable by the GPU

96 GB — Windows lets you dedicate up to 75% of RAM to the GPU (Variable Graphics Memory in AMD Adrenalin; Framework documents 96 GB on a 128 GB board). On Linux the amdgpu GTT pool can be raised to roughly 108-120 GB with ttm.pages_limit / amdgpu.gttsize boot parameters. The Windows figure is used here as the conservative one.

Power under load

150 W (measured by a third party) — ServeTheHome measured 147-160 W at the wall running Llama 3.3 70B Q6_K; 8-14 W idle.

Availability

128 GB / 1 TB $3,499.99 in stock when checked (2 TB $3,649.99). Launched at about $2,000 in May 2025.

Sources

GMKtec, ServeTheHome