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poolside / laguna

Laguna-XS-2.1

33.4 billion parameters, routing 8 of 256 experts per token.

Open in the calculator →

Architecturepublished data
Quick answer
Laguna-XS-2.1 needs about 21.5 GB at Q4_K_M — the format most people download — with 8,192 tokens of context. It fits entirely on 12 of 18 common devices, starting with the NVIDIA GeForce RTX 4090 (24 GB). What else fits in 24 GB. No hardware for it? Rent a GPU that runs it, priced live.

Sized at 8,192 tokens of context with the whole model in device memory, across 18 common devices. Speeds are estimates, not benchmarks.

Find the NVIDIA GeForce RTX 4090, the smallest card listed here that holds it: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

Weight-file size by format

FormatSizeRangeBasis
FP1666.9 GBexactpublished data
Q8_035.3 GB34.9 GB – 35.8 GBreconstructed size
Q6_K27.3 GB26.9 GB – 27.6 GBreconstructed size
Q5_K_M23.6 GB23.3 GB – 23.9 GBreconstructed size
Q5_022.9 GB22.6 GB – 23.2 GBreconstructed size
Q4_K_M20.3 GBexactpublished data
Q4_018.8 GB18.6 GB – 19.0 GBreconstructed size
Q3_K_M16.5 GB15.2 GB – 17.8 GBreconstructed size
Q2_K12.1 GB11.6 GB – 12.7 GBreconstructed size

2 of these are published files. Open the pinned GGUF repository ↗

A file size is not the memory a run needs: the KV cache and the runtime reserve come on top, and the calculator adds both.

What the labels mean
published data
Read from a published source — a file's byte count, a model's configuration or a manufacturer's specification — or exact arithmetic on such values. Not a measurement on a machine.
reconstructed size
Weight size reconstructed from the pinned architecture, because no published file exists.
size range
Only a lower and an upper bound are claimed for this weight size.

Hardware ladder

Q4_K_M at 8,192 tokens · 12 of 18 devices hold it entirely
DeviceMemoryVerdictDecodeCalculator
NVIDIA GeForce RTX 507021.5 GB of 12 GB19 layers on system RAM~64 tok/s with offloadOpen →
AMD Radeon™ RX 9070 XT21.5 GB of 16 GB11 layers on system RAM~94 tok/s with offloadOpen →
NVIDIA GeForce RTX 408021.5 GB of 16 GB11 layers on system RAM~94 tok/s with offloadOpen →
NVIDIA GeForce RTX 508021.5 GB of 16 GB11 layers on system RAM~102 tok/s with offloadOpen →
NVIDIA GeForce RTX 5070 Ti21.5 GB of 16 GB11 layers on system RAM~100 tok/s with offloadOpen →
NVIDIA GeForce RTX 5060 Ti 16GB21.5 GB of 16 GB11 layers on system RAM~79 tok/s with offloadOpen →
NVIDIA GeForce RTX 409021.5 GB of 24 GBfits~339 tok/sOpen →
AMD Radeon™ RX 7900 XTX21.5 GB of 24 GBfits~370 tok/sOpen →
NVIDIA GeForce RTX 309021.5 GB of 24 GBfits~315 tok/sOpen →
NVIDIA GeForce RTX 509021.5 GB of 32 GBfits~602 tok/sOpen →
NVIDIA RTX 6000 Ada Generation21.5 GB of 48 GBfits~323 tok/sOpen →
Apple M4 Pro21.5 GB of 64 GBfits~74 tok/sOpen →
Apple M5 Pro21.5 GB of 64 GBfits~79 tok/sOpen →
AMD Ryzen AI Max+ 395 with Radeon 8060S21.5 GB of 128 GBfits~99 tok/sOpen →
Apple M3 Max21.5 GB of 128 GBfits~90 tok/sOpen →
Apple M4 Max21.5 GB of 128 GBfits~104 tok/sOpen →
NVIDIA DGX Spark21.5 GB of 128 GBfits~92 tok/sOpen →
Apple M2 Ultra21.5 GB of 192 GBfits~119 tok/sOpen →

Decode is a calibrated estimate from the published calibration; capacity is the manufacturer's published ceiling, not guaranteed free memory. Fitting in memory is not the same as loading: whether the runtime and version you have supports this architecture and format on that machine has not been tested here. Offloaded rows assume there is enough system RAM for the overflow — the calculator checks that against the RAM you declare. Each “Open” link keeps this model, format and context.

Run it in the cloud

Rent a GPU that runs Laguna-XS-2.1

Every rentable machine that holds the whole model, from Vast.ai and RunPod, with this site's speed estimate for each and the provider's own price, read live. Cheapest first; sort by cost per token or speed instead, or switch the format.

Prices · 18:30 UTC, 22 Sept
26 configurations in stock · 7 out of stock
MachineSpeedPer hourPer M tokensWhereRent
2× RTX 306024 GBcheapest~242 tok/sFaster than you read$0.11$0.13Vast.aiMarketplace · 98.0% reliableRent on Vast.ai
1× RTX 309024 GBrecommendedbest value~314 tok/sFaster than you read$0.12$0.11Vast.aiMarketplace · 99.5% reliableRunPod $0.50/h Rent on Vast.ai
1× RTX 3090 Ti24 GB~338 tok/sFaster than you read$0.19$0.15Vast.aiMarketplace · 99.6% reliableRunPod $0.27/h Rent on Vast.ai
2× RTX 407024 GB~338 tok/sFaster than you read$0.19$0.15Vast.aiMarketplace · 99.8% reliableRent on Vast.ai
2× RTX 4060 Ti 16GB32 GB~193 tok/sFaster than you read$0.22$0.32Vast.aiMarketplace · 99.7% reliableRent on Vast.ai
1× L424 GB~100 tok/sFaster than you read$0.26$0.70Vast.aiMarketplace · 98.9% reliableRunPod $0.49/h Rent on Vast.ai
2× RTX 5060 Ti 16GB32 GB~301 tok/sFaster than you read$0.26$0.24Vast.aiMarketplace · 99.5% reliableRent on Vast.ai
1× RTX 6000 Ada48 GB~322 tok/sFaster than you read$0.26$0.22Vast.aiMarketplace · 99.9% reliableRunPod $0.84/h Rent on Vast.ai

No GPU at all

Use Laguna-XS-2.1 by the token

Here the rented card is as cheap as the API or cheaper per token ($0.11 per million), and it keeps your data on a machine you control. The API is still the easier start: nothing to set up and nothing to switch off.

First time renting a GPU? How it works, in four steps
  1. Create an account and add credit. Both providers are prepaid: $10 is enough to try any card on this page for hours. A new RunPod account that arrives through a referral link gets a one-time $5 credit when it first adds $10.
  2. Pick the card and the count shown here, and a template: llama.cpp or vLLM for an OpenAI-compatible API, or one with a web chat if you only want to talk to the model. “How to launch it” below gives the exact image and command.
  3. Wait for the download. The weights are fetched on the machine; tens of gigabytes take a few minutes on a data-centre connection. You pay by the second from the moment it starts.
  4. Stop it when you are done. A running machine bills even when idle. A stopped one costs nothing per hour, though its disk is usually still billed until you delete it.
How to launch Q4_K_M on it (llama.cpp)
Container image
ghcr.io/ggml-org/llama.cpp:server-cuda
Start command / arguments
-hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M -c 8192 -ngl 999 --host 0.0.0.0 --port 8080
Expose port
8080 — an OpenAI-compatible API at /v1

Starting from a saved llama.cpp template instead? Leave the arguments empty and set these variables — only the first two change between models:

LLAMA_ARG_HF_REPO=poolside/Laguna-XS-2.1-GGUF:Q4_K_M
LLAMA_ARG_CTX_SIZE=8192
LLAMA_ARG_N_GPU_LAYERS=999
LLAMA_ARG_HOST=0.0.0.0
LLAMA_ARG_PORT=8080

Already have the runtime? llama-server -hf poolside/Laguna-XS-2.1-GGUF:Q4_K_M -c 8192 -ngl 999 --host 0.0.0.0 --port 8080. Weights: poolside/Laguna-XS-2.1-GGUF. The CUDA image is for NVIDIA cards; an AMD machine needs llama.cpp's ROCm build.

Every machine holds the whole model at Q4_K_M and 8,192 tokens of context, no offload. Speeds are this site’s single-stream decode estimates; prices are what each provider’s own API quoted, on-demand, for the whole machine. Vast hosts below 98% measured reliability are left out.

Referral links Vast.ai, RunPod and Novita pay us a share of what you spend if you sign up through these buttons. It costs you nothing, and it never decides an order or a recommendation: both are computed from the live price and the speed, and options that pay us nothing are listed and recommended on the same terms. How we rank

Buying a card instead, or paying by the token? Compare a year of Laguna-XS-2.1 three ways →

Under the hood

Architecture, read from the publisher’s file

The numbers every figure above is computed from, with the file they came from.

Architecture

✓ Architecture read from the published config.json

Retrieved 2026-09-01 at pinned commit c5f36269bbdb; parameter count from the safetensors index at the same revision.

Architecture
laguna
Layers
40
Hidden size
2,048
Attention heads
48
KV heads
8
Head dimension
128
Feed-forward width
8,192
Vocabulary
100,352
Context ceiling
262,144
Sliding window
512
Experts
256
Experts / token
8
Expert width
512
Shared expert width
512

Where the memory goes

tokenembedding× 40 decoder blocksGrouped-query attention48 query · 8 KV headswindow 512Routed experts8 of 256 per tokenall residentoutputprojectiongrows with contextcapacity ≠ traffic

Grouped-query attention shares each key/value head across 6 query heads, so the KV cache is 17% of what multi-head attention would need at the same context.

Open the exact official config.json ↗

More from poolside

Laguna-S-2.1

Use this answer in your own product

As JSON, from the API
curl -s https://llmbottleneck.com/v1/analyze \
  -H "Authorization: Bearer $LLMB_KEY" -H "content-type: application/json" \
  -d '{"model":"poolside-laguna-xs-2-1","quantization":"Q4_K_M","context":8192,"hardware":"nvidia-geforce-rtx-4090"}'

Same engine, same evidence, every field sourced. Free key in one step →

On your page, as a widget
<script src="https://llmbottleneck.com/widget.js"
  data-model="poolside-laguna-xs-2-1" data-quantization="Q4_K_M"
  data-hardware="nvidia-geforce-rtx-4090" data-context="8192"></script>

No key needed. Unbranded, with your own buy button, on Pro and Business →

Citing this page

LLM Bottleneck. “Laguna-XS-2.1 VRAM and hardware requirements.” Architecture from poolside/Laguna-XS-2.1 at revision c5f36269bbdb, retrieved 2026-09-01. https://llmbottleneck.com/models/poolside-laguna-xs-2-1

Every figure above is either the published value or a reconstruction whose measured error is on the accuracy page.

llmbottleneck
catalogue 2026-10-03models 327devices 135