text model · Laguna · macOS
Can I run Laguna XS 2.1 on Apple M1 (8GB)?
No. Laguna XS 2.1 needs ~22.2 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.
Needs ~22.2 GB even at Q4_K_M, but only ~5.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 16.7 GB: Laguna XS 2.1 needs roughly 22.2 GB at Q4_K_M and Apple M1 (8GB) leaves only about 5.5 GB usable for a model. The lightest tracked hardware that runs Laguna XS 2.1 is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Laguna XS 2.1 on Nvidia GeForce RTX 4090 (24GB).
- Q4_K_M needed
- ~22.2 GB
- Usable on device
- ~5.5 GB
- Device memory
- 8 GB
Which quant fits
- 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
- 8 GB unified
- Usable for weights
- ~5.5 GB
- Power draw
- ~39 W
- Best runtime
- Ollama (llama.cpp Metal backend)
What you can run instead
Run Laguna XS 2.1 on other hardware
FAQ
Can Apple M1 (8GB) run Laguna XS 2.1?
No. Laguna XS 2.1 needs ~22.2 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.
How much memory does Laguna XS 2.1 need?
Apple M1 (8GB) does not have enough memory. 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 macOS?
LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
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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/apple-m1-8gb) 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.