Laguna XS 2.1 — Hardware Requirements & GPU Compatibility
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Laguna XS 2.1 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.
Runs great on a 24 GB GPU
Runs from 12 GB (IQ2_S · 16K)Best on 24 GB (Q4_K_M · ~199 tok/s · 64K)Macs from 16 GB (IQ2_M · ~64 tok/s)
openmdw-1.1
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Laguna XS 2.1
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run hf.co/poolside/Laguna-XS-2.1-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 2.7 GB free · 16K context
Download: Laguna-XS-2.1-Q4_K_M.gguf20.27 GBpoolside
Other ways
each opens step-by-stepRun in cloud
Fits on RTX A6000 (48 GB) (26 GB headroom) · Q4_K_M
- Generation speed
- ~170 tok/s
- generation speed
- Cost per 1M output tokens
- $0.54
- per 1M output tokens
How Much VRAM Does Laguna XS 2.1 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 14.6 GB | 16.3 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 128K context · ~237 tok/s | |
| 15.0 GB | 16.7 GB | 3-bit small — 3-bit small quantization | Fits: 128K context · ~234 tok/s | |
| 16.7 GB | 18.4 GB | 3-bit medium — 3-bit medium quantization | Fits: 128K context · ~222 tok/s | |
| 17.1 GB | 18.8 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 64K context · ~219 tok/s | |
| 20.5 GB | 22.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 64K context · ~199 tok/s | |
| 24.2 GB | 25.9 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 25 GB | |
| 28 GB | 29.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 28.8 GB | |
| 33.9 GB | 35.5 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 34.6 GB |
Selected Q4_K_M
4-bit medium quantization21.3GBat 16K context
- Weights
- 20.1 GB
- KV cache
- 0.9 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 2.7 GB free · ~199 tok/s
≥50 tok/s needs ≥160 GB/s · RTX 5090 ~264, RTX 3090 Ti ~199
Which Hardware Can Run Laguna XS 2.1?
Q4_K_M · 20.5 GBLaguna XS 2.1 (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. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run Laguna XS 2.1, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M1 Max (32 GB).
20 GB
Q4_K_M · best fit Q4_K_S (20.0 GB)
1 GPU
RX 7900 XT~86Simulate output on RX 7900 XT32–40 GB
fits · 10.7 GB free · up to 128K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~264Simulate output on RTX 5090M5 Max36 GB~73Simulate output on Mac Studio M5 Max (36 GB)M532 GB~40Simulate output on MacBook Air 13" M5 (32 GB)Orin~62Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~40Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.6 35B A3BAlibaba | 106.8 | 22 GB | ~151 | same size |
| MiniCPM V 4.7 35B A3BOpenBMB | — | 22.1 GB | ~169 | same size |
| Ornith 1.5 35B A3Bornith-ai | — | 22 GB | ~151 | same size |
| NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA | — | 19.3 GB | ~197 | same size |
| Agents A1InternScience | — | 21.5 GB | ~171 | same size |
| Intern S2 PreviewInternLM | — | 73.2 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- poolside
- Parameters
- 33.4B
- Active parameters
- 3B per token
- Architecture
- LagunaForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 100,352
- Release Date
- 2026-06-20
- License
- openmdw-1.1
Frequently Asked Questions
How much VRAM does Laguna XS 2.1 need?
Laguna XS 2.1 requires 20.5 GB of VRAM at Q4_K_M, or 67.3 GB at BF16. 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 14.6 GB (at full 262K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Laguna XS 2.1?
Yes, at Q5_K_S (23.4 GB) or lower. Higher quantizations like Q5_K_M (24.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Laguna XS 2.1?
For Laguna XS 2.1, Q4_K_M (20.5 GB) offers the best balance of quality and VRAM usage. Q4_K_L (20.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 9.6 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Laguna XS 2.1 on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Laguna XS 2.1, the smallest at IQ2_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 20.5 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M5 Pro (24 GB).
Can I run Laguna XS 2.1 locally?
Yes — Laguna XS 2.1 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Laguna XS 2.1?
At Q4_K_M, Laguna XS 2.1 can reach ~119 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~199 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 1.8 GB active ÷ (8000 × 0.65) = 0.36 ms, plus 40 layers × 0.055 ms = 2.20 ms, so 1000 ÷ 2.56 ms = ~391 tok/s
Estimated speed at Q4_K_M (20.5 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Laguna XS 2.1?
At Q4_K_M, the download is about 20.07 GB. The full-precision BF16 version is 66.89 GB. The smallest option (IQ2_XXS) is 9.20 GB.
Which GPUs can run Laguna XS 2.1?
7 consumer GPUs can run Laguna XS 2.1 at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run Laguna XS 2.1?
122 devices with unified memory can run Laguna XS 2.1 at Q4_K_M (20.5 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.