autotrust/GLM5.3-Flash-E192-DGX-Spark
image-text-to-text model by autotrust
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Model card & documentation
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[](https://news.guru.so/ui) autotrust/GLM5.3-Flash-E192-DGX-Spark is a compact build of zai-org/GLM-5.3-Flash for desktop Blackwell systems like NVIDIA DGX Spark. It's an unofficial derivative. It keeps 192 of the 288 routed experts in each layer by Neural Architecture Search (NAS), uses NVFP4 for the experts and still activates 18 B parameters per token. The weights take 124 GiB, the smallest in the family: two DGX Sparks connected by ConnectX-7 get about 62 GiB of weights per node and the most KV room of any build, and a single 180 GB Blackwell GPU (B200/GB200) runs it with 32 GiB of KV cache. The KV cache uses FP8. The repo includes a ready-to-use NVFP4 MTP draft in mtp-nvfp4/ (6.95 GB) for speculative decoding. On a single B200 it gives 1.80× single-stream decode speed, and output quality is unchanged. The model keeps the full 154,880-token vocabulary and has the vision tower intact. Measured on one B200 with…
Read the full model card ↗ · Preview checked 2026-10-30T04:32:30.438Z
Inside the original model card — Document outline
- GLM5.3-Flash-E192-DGX-Spark
- 🔴 Live: AutoTrust/GuruSearch recommendation & search demo → news.guru.so
- Benchmarks
- Tool use: BFCL v4 (function calling, AST match)
- Throughput and MTP speculative decoding (single B200)
- The mtp-nvfp4/ draft
- Deployment
- Requirements
- Single B200 / GB200 (validated)
- 2× DGX Spark
- Request format
- Evaluation protocol
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Model overview
autotrust/GLM5.3-Flash-E192-DGX-Spark is a image-text-to-text model repository published by autotrust on Hugging Face. The source reports the vllm library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- image-text-to-text
- Library
- vllm
- Recent downloads (30 days)
- 9,444
- Cumulative likes
- 180
- Hugging Face trending score
- 110
- Reported safetensors parameters
- 110,710,222,718
- Architecture
- Glm5NextForConditionalGeneration
- License
- mit
- Access
- Not gated by Hugging Face
- Created
- 2026-10-09T23:02:37.000Z
- Last modified
- 2026-10-11T03:49:51.000Z
Compatibility and lineage
Reported languages: en, zh
Reported base models: zai-org/GLM-5.3-Flash
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-10-18T06:42:00.150Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.