text model · Laguna · Windows
Can I run Laguna XS 2.1 on AMD Ryzen AI Halo (128GB)?
Yes. Laguna XS 2.1 runs on AMD Ryzen AI Halo (128GB) at Q4_K_M (~22.2 GB of ~96 GB usable).
Runs at Q4_K_M using ~22.2 GB of ~96 GB usable. You have room for FP16 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. AMD Ryzen AI Halo (128GB) leaves ~73.8 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~22.2 GB
- Usable on device
- ~96 GB
- Device memory
- 128 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Loads the Q4_K_M weights.
ollama run laguna-xs-2.1 - Parameters
- 33.4B (MoE, 3B active)
- Q4_K_M size
- 20 GB
- Q8_0 size
- 36 GB
- Context
- 256k
- Ollama tag
- laguna-xs-2.1
- Memory
- 128 GB unified
- Usable for weights
- ~96 GB
- Power draw
- ~120 W
- Best runtime
- llama.cpp (Vulkan/ROCm) / LM Studio
You could also run
Run Laguna XS 2.1 on other hardware
FAQ
Can AMD Ryzen AI Halo (128GB) run Laguna XS 2.1?
Yes. Laguna XS 2.1 runs on AMD Ryzen AI Halo (128GB) at Q4_K_M (~22.2 GB of ~96 GB usable).
How much memory does Laguna XS 2.1 need?
AMD Ryzen AI Halo (128GB) has room to spare. At Q4_K_M the weights are ~20 GB; with KV cache and runtime overhead, budget ~22.2 GB at a 4k context. It is a Mixture-of-Experts model (33.4B total / 3B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Laguna XS 2.1 on Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/laguna-xs-2.1/amd-ryzen-ai-halo-128gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.