Laguna S 2.1 has 117.6B parameters (8B active per token). In BF16 its weights alone take 219 GiB; quantized to 4-bit, about 66.2 GiB. Each 32K-token request adds 1.57 GiB of KV cache. Cheapest way to run it today: 5 × RTX A4000 (16 GB) at $0.360 per hour. It calls tools and has a thinking mode you can switch off.
Read every day from Hugging Face. Split files are added together; vision projectors are left out.
How many GPUs to run Laguna S 2.1
Fewest GPUs that fit the weights plus one 8K-token request. Data-center GPUs use vLLM (90% of memory usable), the others llama.cpp. Several GPUs split the model with tensor or pipeline parallelism.
GPU
BF16 / FP16
8-bit
4-bit
GeForce RTX 4090 24 GB
16 (several machines)
6
4
GeForce RTX 5090 32 GB
8
4
3
RTX PRO 6000 Blackwell 96 GB
3
2
1
Mac M5 Max 128 GB 96 GB usable
3
2
1
Ryzen AI Max+ 395 128 GB 96 GB usable
3
2
1
DGX Spark (GB10) 128 GB
2
1
1
NVIDIA H100 80 GB
4
2
1
NVIDIA H200 141 GB
2
1
1
NVIDIA B200 (HGX) 180 GB
2
1
1
8-bit: FP8 with vLLM (INT8 on GPUs without FP8), Q8_0 with llama.cpp. 4-bit: AWQ / GPTQ with vLLM, Q4_K_M with llama.cpp, both counted at the Q4_K_M size.
Cheapest way to run Laguna S 2.1 today
For each GPU: the fewest cards that fit Laguna S 2.1 with one 8K-token request, the most faithful weight format at that count, and the cheapest on-demand price (2026-10-30).
On-demand prices from AWS, Google Cloud, Azure, RunPod, Vast.ai, Oracle Cloud, OVHcloud, Scaleway, Verda, Akash and UpCloud, checked every hour. Serving many users needs more KV cache, so more memory: size it in the calculator. Some links are affiliate links: StudioTV may earn a commission, at no extra cost to you.
vLLM and SGLang apply these defaults unless a request sets its own. With llama.cpp, pass them yourself: --temp 1 --top-p 1 --top-k 20 --min-p 0
Reasoning. Thinking mode you can switch on or off. With vLLM or SGLang, set it per request: "chat_template_kwargs": {"enable_thinking": false} turns it off, true on. Reasoning tokens take context and KV cache like any other token: leave room for them.
KV cache: how memory grows with context
Architecture: MoE 256 experts + 1 shared · 12 global layers + 36 sliding-window layers (512 tokens), GQA 8 KV × 128. Its KV cache is 3.8× smaller than if every layer used full attention, because most layers only keep a short window of recent tokens. Only 8B parameters are active per token, but all 118B must sit in memory: VRAM depends on the total, speed on the active part.
Context per request
BF16 cache
FP8 cache
8K tokens
0.45 GiB
0.22 GiB
32K tokens
1.57 GiB
0.79 GiB
128K tokens
6.07 GiB
3.04 GiB
1M tokens
48.1 GiB
24.0 GiB
Laguna S 2.1 VRAM FAQ
How much VRAM does Laguna S 2.1 need?
In BF16 the weights alone take 219 GiB (235 GB). In FP8 that is 110 GiB, and about 66.2 GiB with 4-bit quantization (Q4_K_M). Each request then adds KV cache: 0.45 GiB at 8K tokens and 1.57 GiB at 32K (BF16 cache).
Can Laguna S 2.1 run on a single RTX 4090 (24 GB)?
No. Even in 4-bit it needs 4 RTX 4090s. In 4-bit it fits on a single RTX PRO 6000 Blackwell (96 GB).
Can Laguna S 2.1 run on an RTX 5090 (32 GB)?
No. Even in 4-bit it needs 3 RTX 5090s.
Can Laguna S 2.1 run on a Mac?
Yes. In 4-bit (Q4_K_M with llama.cpp) it needs a Mac with 96 GB of unified memory or more, of which macOS gives the GPU about 72 GB, for one request with an 8K-token context. In 8-bit (Q8_0) it needs 192 GB or more.
Can Laguna S 2.1 run on a DGX Spark or a Ryzen AI Max+ 395 (128 GB)?
Yes. On a DGX Spark (128 GB) it fits in Q8_0 (111 GiB with an 8K-token context); on a Ryzen AI Max+ 395 with 128 GB, whose GPU gets up to 96 GB, it fits in Q4_K_M (66.6 GiB with an 8K-token context), with llama.cpp and one request.
What is the cheapest way to run Laguna S 2.1?
On 2026-10-30, the cheapest on-demand setup is 5 × RTX A4000 (16 GB) with Q4_K_M weights on Vast.ai, at $0.360 per hour (about $262.80 per month). Next: 4 × GeForce RTX 3090 (24 GB) with Q4_K_M weights on Vast.ai, at $0.484 per hour. Sized for one 8K-token request; prices are checked every hour.
How many H100 GPUs does Laguna S 2.1 need?
With one request and an 8K-token context: 4 in BF16, 2 in FP8 and 1 in 4-bit (vLLM, 90% of memory usable). Serving many users at once needs more memory for their KV caches: the calculator sizes that for you.
Does Laguna S 2.1 support tool calling?
Yes, Laguna S 2.1 supports tool (function) calling: it can ask your application to run a function and use the result.
Does Laguna S 2.1 have a thinking mode?
Thinking mode you can switch on or off. With vLLM or SGLang, set it per request with "chat_template_kwargs": {"enable_thinking": false} (or true). Reasoning tokens count toward the context and the KV cache.
What temperature should I use with Laguna S 2.1?
The official defaults are temperature 1, top-p 1, top-k 20, min-p 0.
Laguna S 2.1 VRAM badge
For a model card or a README: the 4-bit size and the smallest GPU it fits on, linked to this page. Data as JSON: /api/models/laguna-s-2-1.json.
[](https://studiotvai.com/vram-requirements/laguna-s-2-1)
Estimates, not guarantees: computed from the official config.json with the same engine as the LLM VRAM Calculator. Real usage depends on your engine version and settings.
poolside, United States · Released July 21, 2026