Running your own AI at home through Ollama or LM Studio is catching on fast among Indonesian users. But once you start comparing hardware, you’ll run into a flood of numbers — and not all of them can be compared apples-to-apples.
So Soxvo built a meta-analysis. Soxvo gathered results from the most credible sources that had actually run AI models on their own hardware, then weighted each source based on its credibility and the depth of its testing.
Below is what came out of that process: the scoring methodology, the consensus rankings, weighted scores, comparison tables, and Indonesian market pricing — which turned out to hold a few big surprises.
How Soxvo Rated the Sources: Meta-Review Methodology
Before rating the sources, Soxvo had to answer one question first: what makes a benchmark trustworthy?
The easy way is to just go by name recognition. But when you’re trying to combine data from different places, what matters is different: can this number be compared against a number from somewhere else? A number with no clear origin can’t be compared to anything, no matter how big the name behind it is.
So Soxvo broke the scoring into five independent categories:
| Criterion | Max Points | What It Means |
|---|---|---|
| Context clarity | 4 | One point for each detail disclosed: the model name, quantization level, backend, and build or OS version |
| Open evidence | 3 | Raw data published and repeated multiple times (3), testing documented cleanly (2), a single undocumented run (1), just a claim (0) |
| Measured with tools | 3 | Metrics actually measured, not copied from a spec sheet: power draw, noise in dBA, temperature, and memory bandwidth |
| Independence | 3 | Unit source disclosed, product drawbacks included, no affiliate pressure |
| Direct AI testing | 3 | Actually ran a language model (3), used a proxy benchmark (1), just quoted specs (0) |
Here’s how the eight sources scored:
| Source | Context | Evidence | Tools | Independence | AI Test | Total |
|---|---|---|---|---|---|---|
| kyuz0’s dataset | 4 | 3 | 1 | 3 | 3 | 14 |
| llama.cpp community | 4 | 2 | 1 | 3 | 3 | 13 |
| nishtahir.com | 4 | 2 | 1 | 3 | 3 | 13 |
| Level1Techs | 3 | 2 | 2 | 3 | 3 | 13 |
| ServeTheHome | 3 | 1 | 3 | 2 | 3 | 12 |
| geerlingguy | 3 | 2 | 1 | 3 | 3 | 12 |
| Notebookcheck | 0 | 2 | 3 | 2 | 1 | 8 |
| r/LocalLLaMA | 1 | 0 | 0 | 3 | 3 | 7 |
Two things jump out from that table, and both change how Soxvo reads the data that follows.
First, the ranking flips the expected order. kyuz0’s dataset, put together by a single person, actually lands at the top, ahead of professional review outlets. The reason is simple: it publishes raw data across five backends, repeated three times, down to the kernel parameters used. Anyone can reproduce the test exactly. For the purpose of combining numbers, transparent methodology turns out to matter more than a big name.
Second — and this matters more for you — no single source is strong at everything. The total score actually hides that fact. Want to know how fast something runs? The strongest sources are kyuz0’s dataset and the llama.cpp community. Change the question to how hot, how loud, or how power-hungry, and the answer shifts to ServeTheHome, Notebookcheck, and Level1Techs, because they’re the only ones who actually hooked up measuring equipment to the machines.
Notebookcheck is the clearest example. Its total score is only 8, second from the bottom, even though its testing is far from careless. It measures temperature and noise rigorously, but it has never published tokens-per-second figures. For speed, this source falls short. For noise, it’s actually one of the strongest.
That’s why every figure in this article is pulled from whichever source is strongest for that particular question. The total score is deliberately not used as the sole deciding factor.
Consensus Results and Weighted Scores
The first ranking is based on how many independent sources tested each device. The second applies the weighting above, so one test from a genuine lab counts for more than a handful of brief mentions.
| Device | Number of Sources | Weighted Score | Tested By |
|---|---|---|---|
| GMKtec EVO-X2 | 3 | 17 | ServeTheHome, nishtahir.com, r/LocalLLaMA |
| Mac mini M4 | 3 | 17 | llama.cpp community, geerlingguy, r/LocalLLaMA |
| Minisforum MS-S1 Max | 2 | 15 | ServeTheHome, Notebookcheck |
| Framework Desktop | 2 | 15 | kyuz0’s dataset, ServeTheHome |
| Mac mini M4 Pro | 2 | 15 | llama.cpp community, geerlingguy |
| Beelink GTR9 Pro | 2 | 11 | ServeTheHome, r/LocalLLaMA |
The top two names come from different worlds entirely. GMKtec EVO-X2 represents the Strix Halo class with 128GB of unified memory, while Mac mini M4 represents the power-efficient, far more affordable class.
This ranking is useful, but there’s something even more valuable here. When all the data from eight sources gets stacked together, a pattern emerges that you wouldn’t spot from a single review.
The Findings That Only Emerge Once You Combine All the Data
Soxvo pulled gpt-oss-120b numbers from four separate tests run on the exact same chip, the Ryzen AI Max+ 395. Here’s what came out:
| Test Conditions | Speed | Source |
|---|---|---|
| Windows, LM Studio, default settings | 31.41 tokens/second | ServeTheHome |
| Linux, Ollama | 33.05 tokens/second | nishtahir.com |
| Linux, llama.cpp ROCm | 46.05 tokens/second | nishtahir.com |
| Optimized Linux, Vulkan RADV | 56.61 tokens/second | kyuz0’s dataset |
Same chip, a 1.8x difference. The only thing that changed was the operating system and software.
Now compare three influential variables side by side, all pulled from the data already gathered:
| What You Change | Impact on Speed |
|---|---|
| Model type (MoE 120B vs. dense 31B, same engine) | 4.9x |
| Hardware (Mac mini M4 to Strix Halo) | 2.4x |
| OS and backend (same chip) | 1.8x |
This is what changed how Soxvo reads the entire list above. Choosing the right model matters far more than choosing the right machine. Hardware is actually the smallest variable of the three.
So the consensus ranking above is still useful for screening which machines have been properly tested, but it shouldn’t be your main decision factor. Pick the wrong type of model, and even a Rp100 million machine can feel slower than a Mac mini running the right one.
There’s another consequence just as important: tokens-per-second figures from two different reviews are almost never directly comparable, unless both name the same model, quantization, and backend. The 1.8x gap in the first table above came entirely from different test conditions — the hardware quality was practically identical.
Which Model Should You Actually Run?
Since model type has the biggest impact, this is the decision you should make first, before even thinking about the machine. Here are all the numbers Soxvo could gather, sorted from fastest to slowest:
| Model | Type | Speed | Tested On | Source |
|---|---|---|---|---|
| gpt-oss-20b | MoE | 65.73 tokens/second | GMKtec EVO-X2 (128GB) | nishtahir.com |
| gpt-oss-120b | MoE | 56.61 tokens/second | Framework Desktop (128GB) | kyuz0’s dataset |
| Qwen3-Coder-30B-A3B Q8 | MoE | 50.16 tokens/second | GMKtec EVO-X2 (128GB) | nishtahir.com |
| Llama-2 7B Q4 | dense | 49.64 tokens/second | Mac mini M4 Pro (24GB) | llama.cpp community |
| Llama 3.2 3B | dense | 41.31 tokens/second | Mac mini M4 (16GB) | geerlingguy |
| Llama-2 7B Q4 | dense | 24.11 tokens/second | Mac mini M4 (16GB) | llama.cpp community |
| Dense 31B model | dense | 11.45 tokens/second | Framework Desktop (128GB) | kyuz0’s dataset |
| qwen3:32b | dense | 9.43 tokens/second | GMKtec EVO-X2 (128GB) | nishtahir.com |
| Dense 27B Q8 model | dense | 6.51 tokens/second | Framework Desktop (128GB) | kyuz0’s dataset |
| Llama 3.3 70B | dense | roughly 5 tokens/second | Beelink GTR9 Pro (128GB) | ServeTheHome |
There’s a clear pattern in that table. Every MoE model sits above 50 tokens/second, while every dense model above 27 billion parameters drops below 12 tokens/second — even when some of them ran on the exact same machine.
For context, a speed of around 20 tokens per second is roughly the pace of human reading. Above that, responses feel like they’re flowing; below it, you start waiting. Use that as your benchmark when reading the table again.
So the logical order is: decide on the model you want to use first, check how much memory it needs, then find a machine with enough memory for it — not the other way around.
Choosing Based on Your Budget
Now let’s put it all together into a decision you can act on today.
| Budget | Get | Realistic Models | Speed |
|---|---|---|---|
| Around Rp10 million | Mac mini M4 16GB, official warranty | Llama 3.2 3B, Llama-2 7B Q4 | 24 to 41 tokens/second |
| Around Rp32 million | Mac mini M4 Pro 24GB, official warranty | 7B models and up, more headroom | Around 50 tokens/second |
| Rp70 million and up | Strix Halo 128GB, grey-market import | Large MoE models up to 120B | 46 to 56 tokens/second |
There’s one warning that falls straight out of this data, and it’s the one that trips people up most often. Don’t spend tens of millions of rupiah on a 128GB machine just to run a 30-billion-parameter dense model. You’ll get around 9 tokens per second — well below reading speed — while a Mac mini M4 at Rp9,999,000 delivers 24 tokens per second on a 7B model. The expensive machine isn’t the problem; the model choice is.
A 128GB machine only pays off if what you’re actually after is a large MoE model like gpt-oss-120b, because that model won’t fit anywhere except in that much memory.
Comparison Table of Mini PCs for Local AI
All figures below are measured results, not manufacturer claims. Each source tested different models, so don’t compare them directly without checking the conditions.
| Device | Memory | Bandwidth | Measured Speed | Indonesia Price |
|---|---|---|---|---|
| GMKtec EVO-X2 | 128GB unified | ~215 GB/s | 46.05 tokens/second (gpt-oss-120b, llama.cpp ROCm) | Rp75 million–Rp112 million (imported) |
| Framework Desktop | 128GB unified | ~215 GB/s | 56.61 tokens/second (gpt-oss-120b, Linux Vulkan RADV) | Not yet available officially |
| Minisforum MS-S1 Max | 128GB unified | ~215 GB/s | ~120W power draw during inference, 43 dBA | Rp69.3 million–Rp107.8 million (imported) |
| Beelink GTR9 Pro | 128GB unified | ~215 GB/s | 31.41 tokens/second (gpt-oss-120b, Windows, LM Studio) | Not yet verified |
| Mac mini M4 Pro | 24–64GB unified | 273 GB/s | 49.64 tokens/second (Llama-2 7B Q4) | Rp31,999,000 (official iBox) |
| Mac mini M4 | 16–32GB unified | 120 GB/s | 24.11 tokens/second (Llama-2 7B Q4) | Rp9,999,000 (official iBox) |
1. GMKtec EVO-X2 — Highest Consensus Score
This device has been tested by the most independent sources, and the results are consistent. With a Ryzen AI Max+ 395 processor and 128GB of unified memory, the EVO-X2 can load models that simply won’t fit on typical hardware.
Its test numbers are comprehensive: gpt-oss-120b runs at 46.05 tokens per second via llama.cpp with ROCm, gpt-oss-20b hits 65.73 tokens per second, and Qwen3-Coder-30B-A3B at Q8 quantization delivers 50.16 tokens per second.
From the same round of testing, running gpt-oss-120b through Ollama produced only 33.05 tokens per second — a significant drop from llama.cpp. The dense qwen3:32b model managed just 9.43 tokens per second. Software choice makes a big difference here.
Soxvo paused to double-check those two numbers. A 120-billion-parameter MoE model runs at 46 tokens per second, while a 32-billion-parameter dense model manages only 9.4 tokens per second, despite being almost four times smaller. So model architecture matters more than parameter count, because MoE models only activate a fraction of their parameters per token.
| Specification | GMKtec EVO-X2 |
|---|---|
| Processor | AMD Ryzen AI Max+ 395 (Strix Halo) |
| Memory | 128GB unified |
| Memory bandwidth | ~215 GB/s |
| Fastest speed | 46.05 tokens/second (gpt-oss-120b, llama.cpp ROCm) |
| Speed via Ollama | 33.05 tokens/second (same model) |
| Dense 32B model | 9.43 tokens/second (qwen3:32b) |
| Power draw | Not measured by any source |
| Source of figures | nishtahir.com, ServeTheHome |
| Indonesia price | Rp75 million to Rp112 million (imported, store warranty) |
Pros: 128GB of memory fits large models; test results are corroborated by multiple other sources. Cons: Indonesian pricing is very high due to grey-market imports; warranty comes from the retailer, not the manufacturer.
2. Mac mini M4 — The Most Sensible Choice for Most People
Its score ties with the EVO-X2, but it’s in a completely different class. The Mac mini M4 was tested by the llama.cpp community using a consistent methodology, producing 24.11 tokens per second for Llama-2 7B at Q4 quantization. A separate test recorded 41.31 tokens per second for the lighter Llama 3.2 3B.
What makes it stand out for Indonesian readers is its status. The Mac mini is the only device on this list you can buy through official channels with an Indonesian warranty, and it costs far less than any Strix Halo machine.
| Specification | Mac mini M4 |
|---|---|
| Processor | Apple M4 |
| Memory | 16GB to 32GB unified |
| Memory bandwidth | 120 GB/s |
| Measured speed | 24.11 tokens/second (Llama-2 7B, Q4 quantization) |
| Lighter model | 41.31 tokens/second (Llama 3.2 3B) |
| Power draw | Not measured by any source |
| Source of figures | llama.cpp community, geerlingguy |
| Indonesia price | Rp9,999,000 (16GB/256GB, iBox, official warranty) |
Pros: Official Indonesian warranty, power-efficient, the most affordable option on this list. Cons: Limited memory capacity, less suited to large models.
3. Minisforum MS-S1 Max — The Most Complete Thermal Data
Tested by ServeTheHome and Notebookcheck. Its power draw sits around 120W during AI inference, with a noise level of 43 dBA in performance mode. At idle, it draws just 9 to 12W.
These power figures matter if you’re planning to run the device 24/7, especially given the electricity rates covered further down.
| Specification | Minisforum MS-S1 Max |
|---|---|
| Processor | AMD Ryzen AI Max+ 395 (Strix Halo) |
| Memory | 128GB unified |
| Memory bandwidth | ~215 GB/s |
| Measured speed | No tokens-per-second figure published yet |
| Power draw (AI workload) | ~120W |
| Power draw (idle) | 9W to 12W |
| Noise | 43 dBA in performance mode |
| Source of figures | ServeTheHome, Notebookcheck |
| Indonesia price | Rp69.3 million to Rp107.8 million (imported) |
Pros: The most complete thermal and power data among its competitors. Cons: High import pricing, noise flagged by reviewers.
4. Framework Desktop — The Highest Measured Number
The fastest figure in all the data Soxvo collected comes from here: 56.61 tokens per second for gpt-oss-120b, measured with llama.cpp using Vulkan RADV on an optimized Linux setup. The raw data is fully published, covering five backends with three repeated runs.
The same dataset produced the most useful finding in this article. The 120B MoE model runs at 56.61 tokens per second, while the much smaller 31B dense model manages only 11.45 tokens per second, and a 27B dense model at Q8 gets just 6.51 tokens per second.
| Specification | Framework Desktop |
|---|---|
| Processor | AMD Ryzen AI Max+ 395 (Strix Halo) |
| Memory | 128GB unified |
| Memory bandwidth | ~215 GB/s |
| Fastest speed | 56.61 tokens/second (gpt-oss-120b, Vulkan RADV on Linux) |
| Dense 31B model | 11.45 tokens/second |
| Dense 27B model (Q8) | 6.51 tokens/second |
| Power draw | Not measured by any source |
| Source of figures | kyuz0’s dataset, ServeTheHome |
| Indonesia price | No official channel yet |
Pros: Highest measured performance, most transparent test data. Cons: No official purchase channel in Indonesia yet.
5. Beelink GTR9 Pro — Nearly a Misread Number
Soxvo almost put the 31.41 tokens-per-second figure for the Beelink GTR9 Pro straight into the consensus table. That number circulates widely and looks convincing. Fortunately, before finalizing it, Soxvo checked the methodology notes — and that’s where the problem showed up.
That figure was measured on Windows using LM Studio with default settings. The exact same Strix Halo hardware, once optimized on Linux, reaches around 56 tokens per second. Nearly double the gap, purely because of who measured it and how.
That near-miss reinforced the rule above: if one number can mislead this easily, any figure that comes without methodology context deserves suspicion.
| Specification | Beelink GTR9 Pro |
|---|---|
| Processor | AMD Ryzen AI Max+ 395 (Strix Halo) |
| Memory | 128GB unified |
| Memory bandwidth | ~215 GB/s |
| Widely circulated figure | 31.41 tokens/second (gpt-oss-120b, Windows, default LM Studio) |
| Potential on the same chip | ~56 tokens/second on optimized Linux |
| 70B model | ~5 tokens/second |
| Power draw | Not measured by any source |
| Source of figures | ServeTheHome |
| Indonesia price | Not yet verified |
Pros: Hardware on par with its Strix Halo competitors. Cons: Indonesian pricing unverified, and its reputation has been shaped by a Windows-only benchmark.
6. Mac mini M4 Pro — The Mid-Range Sweet Spot
The Mac mini M4 Pro sits between the entry-level Mac mini M4 and the Strix Halo machines, and the data shows it earning that middle spot. Tested by the llama.cpp community, it delivered 49.64 tokens per second running Llama-2 7B at Q4 quantization — nearly double the base M4’s result on the identical model, thanks to its higher memory bandwidth.
Like the base Mac mini, it comes with an official Indonesian warranty through iBox, which keeps it in a different risk category than the imported Strix Halo options above.
| Specification | Mac mini M4 Pro |
|---|---|
| Processor | Apple M4 Pro |
| Memory | 24GB to 64GB unified |
| Memory bandwidth | 273 GB/s |
| Measured speed | 49.64 tokens/second (Llama-2 7B, Q4 quantization) |
| Power draw | Not measured by any source |
| Source of figures | llama.cpp community, geerlingguy |
| Indonesia price | Rp31,999,000 (24GB/512GB, iBox, official warranty) |
Pros: Official Indonesian warranty, roughly double the bandwidth and speed of the base M4, comfortable headroom for 7B-class models. Cons: Still far short of the 128GB Strix Halo machines for large MoE models, and costs over three times as much as the base Mac mini M4.
Budget-Friendly Class for Small Models
If your target is just a 3B to 8B model, this class is already enough, and the pricing is reasonable. Soxvo didn’t find independent LLM benchmarks for these devices, so treat this list as a pricing reference, not a performance one.
| Device | Configuration | Market Price |
|---|---|---|
| Beelink SER9 | Ryzen AI 9 HX 370, 32GB/1TB | Rp21,459,000 |
| GMKtec G3 Plus | Intel N150, 16GB/512GB | Rp7,025,000–Rp8,157,600 |
| Beelink EQ14 | Intel N150, 16GB/500GB | Rp5,399,000 |
For more options in this price range, check out Soxvo’s other picks for the best mini PCs.
The NPU and “50 TOPS” Trap
Every new device advertises an NPU rated at “50 TOPS,” which sounds impressive. Unfortunately, when it comes to running language models with the tools most people actually use, that NPU barely gets touched.
Ollama has no NPU support at all; the feature request is still open and has never shipped. LM Studio also has no NPU runtime — all its engines rely on CPU, GPU, or Apple’s MLX. Even llama.cpp can’t use AMD’s NPU yet. Most tellingly, Apple’s own MLX refuses to support the Apple Neural Engine, citing its closed API.
To understand why, you need to know how AI generates an answer. There are two phases: prefill, when the computer reads your prompt, and decode, when it writes the answer word by word. Think of prefill as reading the exam question, and decode as writing the essay answer.
Even chip makers acknowledge this limitation. AMD’s own developer documentation states that the prefill phase is well-suited to the NPU, while the decode phase actually runs on the integrated graphics instead. So AMD — the company selling a 50 TOPS NPU — deliberately routes the answer-writing phase away from its own NPU.
There’s also a benchmark report on the Intel Core Ultra 9 288V showing the NPU losing to the CPU. Running Llama 3.2 3B and Llama 2 7B, average processing time was 6.1 seconds on the CPU, versus 9.4 seconds on the NPU. Intel’s own team followed up on that report directly in the official OpenVINO repository.
To be fair, there is one NPU path that works — AMD calls it hybrid mode. But it’s narrow: Windows only, limited to the Ryzen AI 300 series, restricted to AMD’s own ONNX models, and capped at roughly 8-billion-parameter models. The GGUF models used by Ollama aren’t supported on that path.
What Actually Determines Speed: Memory Bandwidth
If TOPS doesn’t determine speed, what does? The answer is memory bandwidth — how fast data can be read from memory. This number correlates almost linearly with token generation speed.
Look at the data from the same test, Llama-2 7B at Q4 quantization:
| Device | Memory Bandwidth | Generation Speed |
|---|---|---|
| Apple M4 | 120 GB/s | 24.11 tokens/second |
| Apple M4 Pro | 273 GB/s | 49.64 tokens/second |
Bandwidth roughly doubles, and speed roughly doubles right along with it. The TOPS figure predicts nothing in that table, while the bandwidth figure predicts almost everything.
The reason is that every time the model writes one word of its answer, the computer has to re-read the model’s weights from memory, over and over for each subsequent word. As long as the model fits in memory, memory speed is the main bottleneck — raw compute power matters far less.
How Much RAM Do You Actually Need?
RAM is a hard requirement. The model has to fit in memory, or it simply won’t run. As a rough guide, at Q4 quantization, a 7-8 billion parameter model needs about 5-6GB, a 30B MoE model needs about 18-20GB, and very large models need considerably more.
There’s a note on memory allocation, too. On Apple hardware, Metal reserves about 75 percent of unified RAM for the GPU by default. On the Intel side, the latest Arc drivers let you raise shared GPU memory from a default of 57 percent up to 87 percent of total system RAM.
What matters most is still the model type, as the Framework Desktop data above shows. MoE models are far lighter than dense models of similar size. So check the architecture first — parameter count alone is often misleading.
Pricing and Warranty Reality in Indonesia
The GMKtec EVO-X2 with 128GB of memory sells for around USD 1,800 to 2,000 on the global market — roughly Rp30 million. Check listings on local marketplaces, though, and the price jumps to Rp75 million to Rp112 million.
To put that in perspective: one EVO-X2 unit at the cheapest local price equals about seven Mac mini M4 units at Rp9,999,000 each, with money left over for a monitor. Almost all of the markup comes from personal imports or drop-shipping outside official distribution channels, with import duties and reseller margins baked into the price.
It’s worth knowing where things stand. As far as Soxvo could determine, there is no official manufacturer distribution for Strix Halo mini PCs in Indonesia yet. Marketplace stores that call themselves “official” are still only offering store warranties, meaning you can’t take a faulty unit directly to a manufacturer service center.
The Mac mini is the only device on this list available with an official Indonesian warranty, through iBox. At current listings, the Mac mini M4 16GB/256GB sits around Rp9,999,000, while the M4 Pro 24GB/512GB is around Rp31,999,000. Apple pricing changes fairly often, so check current listings before deciding.
Thinking about importing one yourself? Factor in duties first. The duty-free threshold for shipped goods is now just USD 3 per shipment, and anything above that up to USD 1,500 is subject to a 7.5 percent import duty plus 12 percent VAT.
Two other local factors people often overlook. RAM prices are climbing sharply — traders at ITC Jakarta report DDR5 16GB moving from around Rp1.9 million to Rp2.9 million. Then there’s the electricity cost of running a device continuously, with PLN’s R-1/TR 1,300 VA rate at Rp1,444.70 per kWh. A device drawing 120W nonstop will show up on your bill.
Some Indonesian users take a different route entirely: building a PC around a used GPU. There’s a documented local build using an Nvidia Tesla P4 at around Rp2.3 million, chosen for its low 75W power draw, along with local build guides that put a used RTX 3090 24GB as a high-end pick. If your goal is running large models on a reasonable budget, this route is worth comparing before committing to an imported mini PC.
Try What You Already Have Before You Buy
After all the numbers above, Soxvo still recommends the cheapest step first. Small models like Llama 3.2 3B or a 7B model aren’t as demanding as people assume, and a laptop with 16GB of RAM can usually already run them.
Download Ollama, run a 3B model, and see how it performs. Already good enough? You’ve just saved yourself tens of millions of rupiah. A 128GB-class device only makes sense if you’re running large models daily, need strict data privacy, or work without an internet connection.
FAQ
1. Is the NPU on new laptops actually useful for local AI? For common tools like Ollama and LM Studio, the NPU currently goes unused. Neither supports it, and the answer-writing phase runs on the GPU or CPU instead. The NPU is only useful in narrow scenarios, like AMD’s built-in ONNX models on Windows.
2. What’s the minimum RAM to run Ollama? For a 3B model, 8GB of RAM is workable, though tight. For the more common 7-8B models, the model alone needs about 5-6GB, so 16GB of system RAM is the comfortable baseline.
3. Why are local marketplace prices for AI mini PCs so high? Because almost all of them are personal imports or drop-shipped, with import duties and reseller margins baked into the price. A device that costs around Rp30 million on the global market can show up at Rp75 million to Rp112 million locally, with only a store warranty attached.
4. Why do tokens-per-second numbers vary so much between reviews? Because the operating system, backend, and configuration all have a major impact. The same Strix Halo device produced 31.41 tokens per second on Windows with LM Studio, but around 56 tokens per second on optimized Linux. Always check the model, quantization, and backend the reviewer used.
5. Why does the model run but feel painfully slow? It’s most likely too large for your device’s memory bandwidth, or you’re running a dense model. Even a 27B dense model can drop to 6.51 tokens per second on a machine capable of running a 120B MoE model at 56 tokens per second.
Final Thoughts
The EVO-X2 leads the consensus score, and technically, it’s an impressive machine. But in Indonesia, its price runs two-and-a-half to four times higher than the global price, and all you get in return is a store warranty. For most readers, that’s not a trade worth making.
So Soxvo’s recommendation for Indonesian readers lands on the Mac mini M4. It’s sold through official channels, backed by an iBox warranty, priced at Rp9,999,000, and its performance is more than enough for the 3B-to-8B models that cover most people’s needs. The EVO-X2 only makes sense if you regularly run massive models, don’t mind the hassle of importing, and are comfortable with a warranty that rests entirely on the seller’s word.
One last principle worth keeping: memory bandwidth and RAM capacity shape your real-world experience, while the TOPS number on the box predicts nothing for this kind of work. And before spending tens of millions of rupiah on new hardware, try it on the computer you already own first.
