01What it can do
- 68.2% SWE-Bench Verified (top open level)
- Runs on a Mac with 36 GB of RAM
- Apache 2.0 commercial
- Native tool calls + streaming
- Official multi-quant tag Ollama
- —Specialized in coding (not general-purpose)
- —MoE/SWA architecture → transformers support starting with v5.6.2
05Install
Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.
02Required memory
Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.
What hardware do you need for Laguna XS.2?
To run Laguna XS.2 locally with Q4 quantization, you need about 19 GB of VRAM. An option to compare: GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) — leave some headroom for the system and context; check engine compatibility with the GPU.
Why this choice? Our complete guide on GMKtec EVO-X2 64GB / 1TB (Ryzen AI Max+ 395) →
Affiliate links — commission possible at no extra cost to you; independent recommendations. As an Amazon Associate, BestLLMfor earns from qualifying purchases.
On the go: Laguna XS.2 also runs on a RTX laptop PC (24 GB of VRAM) →
03Expected speed
Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.
04Public benchmarks
Scores reproduced from model cards or MMLU-Pro / community sources. Unit: % correct answers.