Laguna XS.2 — Hardware Requirements & GPU Compatibility
Chat
Laguna XS.2 is a 33.4B-parameter open language model from poolside. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 20.48 GB of VRAM — see which GPUs and Macs can run it below.
231.5K downloads
287 likesSpecifications
- Publisher
- poolside
- Parameters
- 33.4B
- Architecture
- LagunaForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 100,352
- Release Date
- 2026-06-03
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Laguna XS.2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_M | 2.70 | 11.7 GB | 25.9 GB | 11.29 GB | Importance-weighted 2-bit, medium |
| Q4_K_M | 4.80 | 20.5 GB | 34.7 GB | 20.07 GB | 4-bit medium quantization — most popular sweet spot |
| Q8_0 | 8.00 | 33.9 GB | 48.1 GB | 33.44 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Laguna XS.2?
Q4_K_M · 20.5 GBLaguna XS.2 (Q4_K_M) requires 20.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 27+ GB is recommended. Using the full 262K context window can add up to 14.2 GB, bringing total usage to 34.7 GB. 5 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Laguna XS.2?
Q4_K_M · 20.5 GB21 devices with unified memory can run Laguna XS.2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Laguna XS.2 need?
Laguna XS.2 requires 20.5 GB of VRAM at Q4_K_M, or 33.9 GB at Q8_0. Full 262K context adds up to 14.2 GB (34.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 33.4B × 4.8 bits ÷ 8 = 20.1 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 14.6 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M20.5 GBQ4_K_M + full context34.7 GB- Can NVIDIA GeForce RTX 4090 run Laguna XS.2?
Yes, at Q4_K_M (20.5 GB) or lower. Higher quantizations like Q8_0 (33.9 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Laguna XS.2?
For Laguna XS.2, Q4_K_M (20.5 GB) offers the best balance of quality and VRAM usage. Q8_0 (33.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 11.7 GB.
VRAM requirement by quantization
IQ2_M11.7 GBQ4_K_M ★20.5 GBQ8_033.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Laguna XS.2 on a Mac?
Laguna XS.2 requires at least 11.7 GB at IQ2_M, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Laguna XS.2 locally?
Yes — Laguna XS.2 can run locally on consumer hardware. At Q4_K_M quantization it needs 20.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Laguna XS.2?
At Q4_K_M, Laguna XS.2 can reach ~142 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~32 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 20.5 × 0.55 = ~142 tok/s
Estimated speed at Q4_K_M (20.5 GB)
~142 tok/s~32 tok/s~106 tok/s~88 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Laguna XS.2?
At Q4_K_M, the download is about 20.07 GB. The full-precision Q8_0 version is 33.44 GB. The smallest option (IQ2_M) is 11.29 GB.
- Which GPUs can run Laguna XS.2?
5 consumer GPUs can run Laguna XS.2 at Q4_K_M (20.5 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Laguna XS.2?
21 devices with unified memory can run Laguna XS.2 at Q4_K_M (20.5 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.