MODEL PROFILE

GLM5.3-Flash-E224-DGX-Spark

autotrust · GLM5.3-Flash-E224-DGX-Spark

> [!IMPORTANT] > **Try AutoTrust/GuruSearch live (new, 11 October 2026).** A recommendation and search demo in which every ranking is a > calibrated System 1 decision, made in about half a second: > * **Recommendation:** the latest headlines from 6 news feeds, ranked by importance. > * **Search:** results from Google News, Bing News and Yahoo News re-ranked by relevance, side by side with the > search engines' own order, plus a short answer with citations. **autotrust/GLM5.3-Flash-E224-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.** > 🚀 **Deploying on DGX Spark?** A complete, field-tested runbook for > serving this checkpoint on **two ConnectX-7-linked DGX Sparks** > (vLLM 0.31.0, TP=2, fp8 KV cache) is here: > **DEPLOY-2X-DGX-SPARK.md** — network setup, > NCCL/CX7 pitfalls, memory budgeting, ops & troubleshooting, and > measured throughput (15.7 tok/s single-stream, 71.8 tok/s at 8-way > concurrency, both GPUs ≈96% utilized).

Visual QAlicense:mit
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/10/31 20:30:18
OVERVIEW

Model overview

> [!IMPORTANT] > **Try AutoTrust/GuruSearch live (new, 11 October 2026).** A recommendation and search demo in which every ranking is a > calibrated System 1 decision, made in about half a second: > * **Recommendation:** the latest headlines from 6 news feeds, ranked by importance. > * **Search:** results from Google News, Bing News and Yahoo News re-ranked by relevance, side by side with the > search engines' own order, plus a short answer with citations. **autotrust/GLM5.3-Flash-E224-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.** > 🚀 **Deploying on DGX Spark?** A complete, field-tested runbook for > serving this checkpoint on **two ConnectX-7-linked DGX Sparks** > (vLLM 0.31.0, TP=2, fp8 KV cache) is here: > **DEPLOY-2X-DGX-SPARK.md** — network setup, > NCCL/CX7 pitfalls, memory budgeting, ops & troubleshooting, and > measured throughput (15.7 tok/s single-stream, 71.8 tok/s at 8-way > concurrency, both GPUs ≈96% utilized).

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

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These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.

Sources & traceability

许可证:mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。