---

base_model: poolside/Laguna-S-2.1

base_model_relation: quantized

tags:

• gguf
• laguna
• moe
• ds4
• pulsar
• ssd-streaming
• imatrix

---

Laguna-S-2.1 (118B) GGUF for pulsar / ds4 (SSD streaming, CUDA)

Mixed-precision GGUF of poolside/Laguna-S-2.1

(118B total / 8B active MoE, 256 experts top-10 + 1 shared) built for

SSD-streaming inference engines: routed experts live on disk and stream

per token, so the model runs on GPUs that cannot hold it.

Runs on pulsar (Rust + CUDA).

Measured on RTX 5060 Ti 16GB + RTX 4060 Ti 16GB, Ryzen 9900X, Gen5 NVMe

(greedy, warm cache):

| metric | pulsar |

|---|---|

| decode | 17.3 tok/s |

| decode + CPU expert lane (PULSAR_CPU=1) | 22.4 tok/s |

| prefill (328-token prompt) | ~53 tok/s |

Per token only 10 of 256 experts per layer are read; attention, the

shared expert, and the router stay resident. At this quant ~15.5GB of hot

experts stay VRAM-resident (85% cache hit rate on the decode path), which

is why the 2-bit build is both smaller and faster than the Q4_K_M

edition. Laguna's hybrid attention (one full-window layer every fourth,

sliding-window 512 for the rest), per-head output gate, and per-layer-type

RoPE (YaRN on full layers, plain on sliding) are all handled by the engine.

Files

| file | provenance | recommendation |

|---|---|---|

| Laguna-S-2.1-IQ2XXS-RouterF32-AttnQ8-SExpQ8-OutQ6.gguf | routed gate/up experts IQ2_XXS with an importance matrix (36 GB) | use this one |

| Laguna-S-2.1-Q2K-RouterF32-AttnQ8-SExpQ8-OutQ6.gguf | routed experts Q2_K, no imatrix (41 GB) | kept for continuity |

Both quantize straight from the BF16 checkpoint (235 GB) and keep an

identical high-precision layout for everything that steers a token. They

differ only in the routed-expert codec: the IQ2_XXS build uses a

general-purpose importance matrix so the 2-bit gate/up weights keep the

dimensions that matter; the Q2_K build is uniform 2-bit with no

calibration. The imatrix file is smaller and closer to the source.

Recipe

The layout targets a streaming expert cache: routed experts must be

uniform fixed-size slabs, and everything that steers a token stays high

precision. Same design as antirez's GLM-5.2 ds4 build.

| Tensors | Type | Why |

|---|---|---|

| routed experts, gate/up | IQ2_XXS (imatrix) / Q2_K | streamed from disk per token; uniform slabs |

| routed experts, down | Q2_K | streamed per token; down is the sensitive one |

| shared expert + dense layer 0 FFN | Q8_0 | resident, paid once |

| attention q/k/v/output, all layers | Q8_0 | resident (source ships F16, converted at quant time) |

| token embeddings, output head | Q6_K | resident |

| router (ffn_gate_inp), expert bias, all norms | F32 | decision makers stay exact |

The router stays F32 because pulsar runs it through an F32 matmul; a

quantized ffn_gate_inp is read as raw floats and destroys routing.

Usage

pulsar

git clone https://github.com/giannisanni/pulsar
cd pulsar
CXX=g++-12 cargo build --release -p engine

./target/release/pulsar-cli -m Laguna-S-2.1-IQ2XXS-RouterF32-AttnQ8-SExpQ8-OutQ6.gguf \
    -p "The capital of France is" -n 64
# or the OpenAI-compatible API + web UI: cargo build --release -p serve

Zero-config multi-GPU: pulsar measures each card's PCIe bandwidth at

startup, streams experts over the fastest link, and fills spare GPUs with

resident hot experts. First run is cold; a .warm sidecar makes every

later run start hot. Main knob: PULSAR_CACHE_GB (host expert cache,

defaults to measured free RAM minus a reserve).