Can a Strix Halo GMKtec EVO-X3, 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 $3,600 and 500k tokens a day, it pays back in 275 years.
Run the numbers on this machine
What the GMKtec EVO-X3, 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.
| Model | Speed | Class | Memory | Longest context |
|---|---|---|---|---|
| Qwen3.8 27BQ4_K_M | 10 tok/s estimated | Sonnet-class | 19 GB | 256k |
| Ling 3.0 flashQ4_K_M | 41 tok/s estimated | Haiku-class | 78 GB | 256k |
| Qwen3.6 27BQ4_K_M | 10 tok/s estimated | Haiku-class | 19 GB | 256k |
| Nemotron 3.5 Bolt 74B-A9BIQ2 | 48 tok/s estimated | Haiku-class | 22 GB | 256k |
| Qwen3.6 35B-A3BQ4_K_M | 56 tok/s estimated | Haiku-class | 23 GB | 256k |
| Muse Glimmer 30BQ4_K_M | 11 tok/s estimated | Haiku-class | 18 GB | 128k |
| Gemma 4 26B-A4BQ4_K_M | 41 tok/s estimated | Haiku-class | 18 GB | 256k |
| Qwen3.5 122B-A10BUD-Q4_K_M | 20 tok/s estimated | Haiku-class | 79 GB | 256k |
| Gemma 4 31B itQ4_K_M | 8.3 tok/s estimated | Haiku-class | 23 GB | 256k |
| Granite 4.2 30BQ4_K_M | 7.3 tok/s estimated | Haiku-class | 26 GB | 128k |
| GLM-4.7-FlashQ4_K_M | 40 tok/s estimated | Haiku-class | 20 GB | 128k |
| Gemma 4 12BQ4_K_M | 24 tok/s estimated | Below every hosted tier | 8.0 GB | 256k |
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.
Other machines to weigh against the GMKtec EVO-X3, 128GB
| Machine | Price | Memory | Models that fit | Pay-back |
|---|---|---|---|---|
| Other Strix Halo machines | ||||
| Strix Halo Framework Desktop, 32GB | $1,269 | 32 GB | 29 | Pays back in 91 years |
| Strix Halo Corsair AI Workstation 300, 64GB | $1,700 | 64 GB | 32 | Pays back in 121 years |
| Strix Halo Framework Desktop, 64GB | $1,959 | 64 GB | 32 | Pays back in 140 years |
| Strix Halo GMKtec EVO-X2, 64GB | $2,200 | 64 GB | 32 | Pays back in 168 years |
| Strix Halo Framework Desktop, 128GB | $3,449 | 128 GB | 39 | Pays back in 246 years |
| Strix Halo GMKtec EVO-X2, 128GB | $3,500 | 128 GB | 39 | Pays back in 267 years |
| Strix Halo Minisforum MS-S1 Max, 128GB | $3,799 | 128 GB | 39 | Pays back in 271 years |
| Strix Halo Beelink GTR9 Pro, 128GB | $4,349 | 128 GB | 39 | Pays back in 311 years |
| Strix Halo Corsair AI Workstation 300, 128GB | $4,700 | 128 GB | 39 | Pays back in 336 years |
| Strix Halo HP Z2 Mini G1a, 128GB | $5,544 | 128 GB | 39 | Pays back in 396 years |
| Strix Halo Framework Desktop, 192GB | not published | 192 GB | 42 | Not enough data to compute a pay-back. |
| Nearest in price elsewhere on the list | ||||
| NVIDIA GeForce RTX 5090, 32GB | $1,999card only | 32 GB | 32 | Pays back in 123 years |
| Mac mini M5 Pro, 64GB | $2,699 | 64 GB | 32 | Pays back in 182 years |
| Mac Studio M5 Max, 64GB | $3,499 | 64 GB | 32 | Pays back in 197 years |
| MacBook Pro M5 Pro (16-inch), 48GB | $3,599 | 48 GB | 32 | Pays back in 242 years |
| DGX Spark GB10 Grace Blackwell, 128GB | $4,699 | 128 GB | 39 | Pays back in 346 years |
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: Framework Desktop, 128GB · MacBook Pro M5 Pro (16-inch), 48GB. There are 111 machine match-ups on the site.
The specifics
- Chip
- GMKtec EVO-X3 — 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). 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) — No separate measurement for the X3; ServeTheHome measured 147-160 W at the wall on the EVO-X2, same chip and TDP.
- Availability
- Shipping. Replaced the EVO-X2 in July 2026.
- Sources
- GMKtec