I have spent the last few weeks testing the Olares ONE as a compact local-AI workstation rather than treating it as another spec-sheet mini PC.
The machine measures 320 × 197 × 55 mm and weighs 2.15 kg without its power supply. Inside it has:
- Intel Core Ultra 9 275HX
- NVIDIA GeForce RTX 5090 Laptop with 24 GB GDDR7
- 96 GB DDR5-5600 RAM
- 2 TB PCIe 4.0 NVMe SSD
- 2.5 GbE, Thunderbolt 5, HDMI 2.1, Wi-Fi 7 and Bluetooth 5.4
- 330 W power adapter
The word Laptop matters. This is not a desktop RTX 5090, and the 96 GB of system RAM does not become 96 GB of VRAM. Models can spill work into system memory, but fitting a model and running it at a practical speed are two different things.
Instead of only measuring tokens per second, I wanted to know whether local models could complete real multi-step work: maintaining state across conversations, using tools, creating and checking files, handling contradictory evidence, respecting privacy constraints and recovering from failed operations.
What I tested
Across the full project I tested more than 30 model variants through llama.cpp, vLLM and Ollama. My archive contains 236 rows in the main comparison and more than 1,400 deduplicated checks across roughly 20 hours of measured model execution.
Those runs used different batteries and environments, so I did not combine them into one misleading score. For the most useful comparison, I made five finalists run the same 33-check battery twice under controlled conditions.
The common setup was llama.cpp commit 6d5a910, OpenClaw 2026.7.1-2, a 98,304-token context, one slot, Flash Attention, Q4 KV cache and a maximum output of 8,192 tokens. The model was already loaded before timing.
Prompts, tools, inputs and checks were identical, but sampling was tuned per model family: temperature 0.1 for Ornith, Qwopus and Qwen3.6, and 0.7 for Qwen3.8. This compares practical configurations, not isolated model architectures with every parameter held constant.
| Suite |
Checks |
| Persistent conversation |
15 |
| Demanding conversation |
12 |
| Operations automation |
1 |
| Personal administration |
1 |
| Agentic recovery |
1 |
| Large tool context |
3 |
| Total |
33 |
Repeated results
| Model |
Pass 1 |
Pass 2 |
Mean time |
Peak VRAM |
| Ornith 1.0 35B Q4_K_M |
33/33 · 442 s |
33/33 · 445 s |
444 s |
21,232 MiB |
| Qwopus3.6 27B v2 MTP IQ4_XS |
33/33 · 1,075 s |
33/33 · 959 s |
1,017 s |
17,676 MiB |
| Qwen3.8 27B IQ4_XS |
31/33 · 1,189 s |
33/33 · 1,135 s |
1,162 s |
17,978 MiB |
| Qwen3.6 27B UD-Q4_K_XL |
33/33 · 1,447 s |
33/33 · 1,469 s |
1,458 s |
20,080 MiB |
| Qwen3.8 27B UD-Q4_K_XL |
33/33 · 1,571 s |
32/33 · 1,512 s |
1,542 s |
20,028 MiB |
Ornith 1.0 35B Q4_K_M was the best overall result on this machine: both audited passes completed in 442/445 seconds. Qwopus also completed both runs and used less VRAM, but took about 2.3 times as long in this particular battery. That does not mean Ornith will be 2.3 times faster on every prompt.
I also recorded generation rates, where Ornith reached 239.89 tok/s and Qwopus 221.34 tok/s. They are not the same as end-to-end agent speed: tool calls, prefill, web requests and recovery loops are included in the suite times instead.
The failures were more useful than a perfect score
Passing 33 checks once did not make any model reliable in every situation. In the longer tests:
- Qwopus exhausted its output limit during a research task and failed to create the required files.
- Ornith missed indexed evidence after a retry and, in a different long conversation, exposed private information.
- Qwen3.8 had a genuine privacy failure in one pass and passed the same check in another.
- An Ornith MTP variant produced the fastest single pass, but its second pass was much slower and included a privacy leak, a search loop and insufficient evidence.
- One optional MTP sidecar could not load because it lacked a tensor required by this llama.cpp build. I counted that as a packaging/runtime compatibility problem, not a VRAM or intelligence failure.
I manually reviewed suspected false negatives from the automatic verifier, but only corrected cases where the output was demonstrably right and the checker expected an exact phrase or field name. Missing files, wrong paths, unsafe actions, weak evidence and real timeouts remained failures.
My practical take on the hardware
For a machine of this size, the 5090 Laptop GPU makes 27B–35B quantized models genuinely useful for local agentic work. The 24 GB VRAM ceiling is still the defining limit: the Olares ONE is fast when the model fits well on the GPU, but it is not a compact substitute for a workstation with a very large VRAM pool.
My current choice on this hardware would be Ornith 1.0 35B Q4_K_M, with Qwopus3.6 27B v2 MTP IQ4_XS as the lower-VRAM alternative. I would still start any agent with read-only access, test it on disposable data and require approval before it sends messages, changes permissions or modifies services.
Olares supplied the machine, but did not pay for the video. The company only checked technical facts; the methodology, tests and conclusions are mine.
I documented the hardware and several of the tests here: What can a local AI actually do? Real tests on an RTX 5090 Laptop mini PC
For a follow-up comparison, what would be most useful here: a 128 GB Strix Halo mini PC, an NVIDIA DGX Spark, or a desktop RTX 5090 system?
