---
license: mit
base_model: zai-org/GLM-5.2
library_name: basert
pipeline_tag: text-generation
tags:
- basert
- apple-silicon
- quantized
---

# GLM-5.2

[BaseRT](https://github.com/basecompute/baseRT) `.base` build of [`zai-org/GLM-5.2`](https://huggingface.co/zai-org/GLM-5.2) for local inference on Apple Silicon (Metal).

A 78-layer mixture-of-experts reasoning model: 256 routed experts with 8 active per token plus one always-on shared expert (sigmoid gating), Multi-head Latent Attention (MLA, 64 heads over a single 576-wide latent KV row) with DeepSeek-style sparse attention (DSA), a 155k vocabulary and a 1M-token context window. The first three layers use a dense FFN.

Needs 512 GB of unified memory: the Q4 weights alone are ~418 GB resident. Apple Silicon only — the MLA/DSA kernels have no CUDA build.

## Files

Q4 is 432 GB, past the Hub's per-file ceiling, so it ships as 9 parts of ≤45 GiB under `parts/`.

| File | Precision | Size |
|------|-----------|------|
| `parts/GLM-5.2-Q4.base.part-000` … `part-008` | 4-bit (group-quantized, f16 attention norms / MLA projections) | 432 GB total |
| `parts/GLM-5.2-Q4.base.manifest.json` | part count, per-part sizes and sha256s, whole-file sha256 | 2 KB |

`basert pull` (BaseRT 0.2.4 or newer) downloads the parts and reassembles them on install, checking each part against the manifest as it lands. The parts are streamed into place one at a time and each is deleted once appended, so the pull needs the bundle plus one part of free disk (about 480 GB), not two bundles. A pull that is interrupted resumes at the next part. The repo is private, so the token has to be in the environment:

```sh
export HF_TOKEN=hf_...
basert pull basecompute/GLM-5.2
```

Without `basert pull`, the parts concatenate in order into one file; stream them so the download never needs a second 432 GB of free space:

```sh
for i in 0 1 2 3 4 5 6 7 8; do
  curl -L -H "Authorization: Bearer $HF_TOKEN" \
    "https://huggingface.co/basecompute/GLM-5.2/resolve/main/parts/GLM-5.2-Q4.base.part-00$i" >> GLM-5.2-Q4.base
done
shasum -a 256 GLM-5.2-Q4.base   # expect e7cf483188806908f290a6eed1d96ec6a974cb3d4751a867d5c67a317646c230
```

## Usage

GLM 5.2 needs BaseRT 0.2.4 or newer.

```bash
curl -LsSf https://basecompute.co/install.sh | sh
basert chat basecompute/GLM-5.2       # after basert pull
basert serve basecompute/GLM-5.2      # OpenAI-compatible HTTP server
basert chat ./GLM-5.2-Q4.base         # or a hand-reassembled file
```

DSA sparse attention is opt-in. Dense MLA is the default and matches llama.cpp's glm-dsa implementation bitwise; set `BASERT_DSA=1` to decode on the sparse top-k path once the context is deeper than the indexer's selection width (2048 tokens):

```bash
BASERT_DSA=1 basert serve ./GLM-5.2-Q4.base
```

## Measured

Mac Studio M3 Ultra (80-core GPU, 512 GB), BaseRT 0.2.4 (staging build of commit 36bd7e24), greedy, single stream, `basert bench` (pp = prompt tokens/s, tg = generated tokens/s):

| test | dense MLA | `BASERT_DSA=1` |
|---|---:|---:|
| pp512 | 236 t/s | — |
| tg128 | 22.9 t/s | — |
| pp4096 | 178 t/s | 188 t/s |
| tg128 after 4096 | 22.7 t/s | 22.4 t/s |

Weights resident: ~418 GB. First load after a reboot streams the whole file from disk (about 3.5 minutes on the Studio's internal SSD); a warm load is under 10 seconds. At a 4k context the sparse path is a wash — its selection width is 2048 tokens, so it only starts to pay past that, and generation is weight-bandwidth-bound at this size either way.

Released under the mit license, inherited from the base model.