--- title: "GLM-4.5 | Aguasto" description: "Research reference for GLM-4.5: official checkpoint, license and memory considerations. Documented in Aguasto Research; no Aguasto deployment or API availability is represented." canonical: "https://www.aguasto.com/models/glm-4-5" last-updated: "2026-10-12" --- # GLM-4.5 | Aguasto Canonical HTML: [GLM-4.5](https://www.aguasto.com/models/glm-4-5) > Research reference for GLM-4.5: official checkpoint, license and memory considerations. Documented in Aguasto Research; no Aguasto deployment or API availability is represented. [Aguasto](https://www.aguasto.com/) [Model Index](https://www.aguasto.com/models) GLM-4.5 [Resources](https://www.aguasto.com/resources) [GPU Library](https://www.aguasto.com/gpus) [Model Index](https://www.aguasto.com/models) [Inference Stack](https://www.aguasto.com/inference-stack) [Learn](https://www.aguasto.com/learn) [Insights](https://www.aguasto.com/insights) [Research](https://www.aguasto.com/research) [Engineering tools](https://www.aguasto.com/tools) Z.ai / Open-weight checkpoint reference ## GLM-4.5 Documented in Aguasto Research. This checkpoint has not been evaluated or deployed by Aguasto and is not available through an Aguasto API. Original Aguasto conceptual illustration. Not manufacturer geometry, an operating Aguasto asset, or a measured benchmark. Aguasto Research documents third-party hardware, models and software. References do not represent Aguasto inventory, evaluated deployments or available inference services. ### Checkpoint identity Official identifier zai-org/GLM-4.5[Z.ai GLM-4.5 model card (external source)](https://huggingface.co/zai-org/GLM-4.5) · [GLM-4.5 checkpoint configuration (external source)](https://huggingface.co/zai-org/GLM-4.5/blob/main/config.json) · [GLM-4.5 code repository license (external source)](https://github.com/zai-org/GLM-4.5/blob/main/LICENSE) Architecture Mixture of experts; hybrid thinking and non-thinking text model[Z.ai GLM-4.5 model card (external source)](https://huggingface.co/zai-org/GLM-4.5) · [GLM-4.5 checkpoint configuration (external source)](https://huggingface.co/zai-org/GLM-4.5/blob/main/config.json) · [GLM-4.5 code repository license (external source)](https://github.com/zai-org/GLM-4.5/blob/main/LICENSE) Total parameters 355 billionPublished rounded counts; total stored weights determine the preliminary footprint, including all MoE experts.[Z.ai GLM-4.5 model card (external source)](https://huggingface.co/zai-org/GLM-4.5) · [GLM-4.5 checkpoint configuration (external source)](https://huggingface.co/zai-org/GLM-4.5/blob/main/config.json) · [GLM-4.5 code repository license (external source)](https://github.com/zai-org/GLM-4.5/blob/main/LICENSE) Active parameters 32 billion per tokenActive parameters are a compute-related count, not the model's full stored weight count.[Z.ai GLM-4.5 model card (external source)](https://huggingface.co/zai-org/GLM-4.5) · [GLM-4.5 checkpoint configuration (external source)](https://huggingface.co/zai-org/GLM-4.5/blob/main/config.json) · [GLM-4.5 code repository license (external source)](https://github.com/zai-org/GLM-4.5/blob/main/LICENSE) Modalities text-input, text-output[Z.ai GLM-4.5 model card (external source)](https://huggingface.co/zai-org/GLM-4.5) · [GLM-4.5 checkpoint configuration (external source)](https://huggingface.co/zai-org/GLM-4.5/blob/main/config.json) · [GLM-4.5 code repository license (external source)](https://github.com/zai-org/GLM-4.5/blob/main/LICENSE) Context 131,072 tokensNative configuration where directly verified. Extensions, advertised K-labels and configuration differences are preserved in the notes below.[Z.ai GLM-4.5 model card (external source)](https://huggingface.co/zai-org/GLM-4.5) · [GLM-4.5 checkpoint configuration (external source)](https://huggingface.co/zai-org/GLM-4.5/blob/main/config.json) · [GLM-4.5 code repository license (external source)](https://github.com/zai-org/GLM-4.5/blob/main/LICENSE) Release date Exact release date not independently established in the reviewed artifacts[Z.ai GLM-4.5 model card (external source)](https://huggingface.co/zai-org/GLM-4.5) · [GLM-4.5 checkpoint configuration (external source)](https://huggingface.co/zai-org/GLM-4.5/blob/main/config.json) · [GLM-4.5 code repository license (external source)](https://github.com/zai-org/GLM-4.5/blob/main/LICENSE) Download access Public checkpoint reference[Z.ai GLM-4.5 model card (external source)](https://huggingface.co/zai-org/GLM-4.5) · [GLM-4.5 checkpoint configuration (external source)](https://huggingface.co/zai-org/GLM-4.5/blob/main/config.json) · [GLM-4.5 code repository license (external source)](https://github.com/zai-org/GLM-4.5/blob/main/LICENSE) License MIT (model weights)The checkpoint card identifies the weights as MIT and describes commercial use. The separate code repository has an Apache 2.0 LICENSE. Check the terms for each downloaded artifact; this description is not a deployment clearance.[Z.ai GLM-4.5 model card (external source)](https://huggingface.co/zai-org/GLM-4.5) · [GLM-4.5 checkpoint configuration (external source)](https://huggingface.co/zai-org/GLM-4.5/blob/main/config.json) · [GLM-4.5 code repository license (external source)](https://github.com/zai-org/GLM-4.5/blob/main/LICENSE) ### Theoretical weight-memory footprint Using the publisher's approximately 355 billion total parameters at 16 bits per parameter gives **710 GB** (661.2 GiB) of nominal weights. weight GB = total parameters in billions × bits per weight / 8 This estimate excludes KV cache, activations, quantization metadata, non-weight buffers and serving overhead. It is not a tested GPU fit. MoE routing activates only part of the network per token while the complete expert weights still need storage. [Explore the memory estimator](https://www.aguasto.com/tools) #### Quantization This checkpoint is BF16; the publisher also lists a separate FP8 checkpoint. FP8 needs compatible hardware and runtime kernels. [Z.ai GLM-4.5 model card (external source)](https://huggingface.co/zai-org/GLM-4.5) · [GLM-4.5 checkpoint configuration (external source)](https://huggingface.co/zai-org/GLM-4.5/blob/main/config.json) · [GLM-4.5 code repository license (external source)](https://github.com/zai-org/GLM-4.5/blob/main/LICENSE) ### Serving considerations - Parameter counts are the publisher's rounded, approximate counts; active parameters do not represent the full weight memory footprint. - The config specifies 131,072 positions; the card describes this as 128K context. Long context and batching increase cache memory requirements. - Framework links are documentation starting points. Aguasto has not evaluated or deployed this checkpoint, and no throughput or GPU-fit result is claimed. [Z.ai GLM-4.5 model card (external source)](https://huggingface.co/zai-org/GLM-4.5) · [GLM-4.5 checkpoint configuration (external source)](https://huggingface.co/zai-org/GLM-4.5/blob/main/config.json) · [GLM-4.5 code repository license (external source)](https://github.com/zai-org/GLM-4.5/blob/main/LICENSE) #### Framework documentation These are third-party documentation starting points. Support depends on the exact checkpoint, runtime version and hardware. ### Continue the research [vLLM](https://www.aguasto.com/inference-stack/vllm) [SGLang](https://www.aguasto.com/inference-stack/sglang) [Compare model references](https://www.aguasto.com/models/compare) [GPU specification library](https://www.aguasto.com/gpus) [Weights, cache and serving overhead](https://www.aguasto.com/learn/model-weights-and-runtime-memory) ### Sources & verification Reference revision 1 · Reviewed October 12, 2026. Manufacturer claims and documentation are attributed; Aguasto has not run the cited products in production. - [Z.ai GLM-4.5 model card ↗](https://huggingface.co/zai-org/GLM-4.5) 355B total / 32B active rounded counts; text model and reasoning modes; BF16 and separate FP8 downloads; MIT weight license declaration; vLLM and SGLang references; open checkpoint access. Publication: date not stated in the reviewed source. Verified: 2026-10-12. - [GLM-4.5 checkpoint configuration ↗](https://huggingface.co/zai-org/GLM-4.5/blob/main/config.json) 131072 max\_position\_embeddings; glm4\_moe architecture; BF16 dtype. Publication: date not stated in the reviewed source. Verified: 2026-10-12. - [GLM-4.5 code repository license ↗](https://github.com/zai-org/GLM-4.5/blob/main/LICENSE) Separate repository code LICENSE is Apache 2.0. Publication: date not stated in the reviewed source. Verified: 2026-10-12. ### Reference journey [Compare models ↗](https://www.aguasto.com/models/compare) [Estimate weights ↗](https://www.aguasto.com/tools) [Hardware references ↗](https://www.aguasto.com/gpus) [Aguasto inference strategy ↗](https://www.aguasto.com/ai-inference) [Sources & verification ↓](https://www.aguasto.com/models/glm-4-5#sources) Research reference · No available Aguasto service ## Company navigation - [Home](https://www.aguasto.com/) - [Infrastructure](https://www.aguasto.com/infrastructure) - [GPU Cloud](https://www.aguasto.com/gpu-cloud) - [AI Inference](https://www.aguasto.com/ai-inference) - [Development & Investment](https://www.aguasto.com/development) - [Company](https://www.aguasto.com/company) - [Contact](https://www.aguasto.com/contact) - [Privacy](https://www.aguasto.com/privacy) - [Resources](https://www.aguasto.com/resources) - [GPU Library](https://www.aguasto.com/gpus) - [Model Index](https://www.aguasto.com/models) - [Inference Stack](https://www.aguasto.com/inference-stack) - [Learn](https://www.aguasto.com/learn) - [Insights](https://www.aguasto.com/insights) - [Research](https://www.aguasto.com/research) - [Industry cases](https://www.aguasto.com/research/cases) - [Why Modular](https://www.aguasto.com/why-modular) - [Partners](https://www.aguasto.com/partners) - [GPU comparison](https://www.aguasto.com/gpus/compare) - [Model comparison](https://www.aguasto.com/models/compare) - [Engineering tools](https://www.aguasto.com/tools) - [Development friction](https://www.aguasto.com/research/hyperscale-development-friction) - [NVIDIA H100 SXM](https://www.aguasto.com/gpus/h100) - [NVIDIA H200 SXM](https://www.aguasto.com/gpus/h200) - [NVIDIA B200 SXM](https://www.aguasto.com/gpus/b200) - [NVIDIA B300](https://www.aguasto.com/gpus/b300) - [NVIDIA GB200 NVL72](https://www.aguasto.com/gpus/gb200-nvl72) - [NVIDIA GB300 NVL72](https://www.aguasto.com/gpus/gb300-nvl72) - [NVIDIA Vera Rubin NVL72](https://www.aguasto.com/gpus/vera-rubin-nvl72) - [AMD Instinct MI300X](https://www.aguasto.com/gpus/mi300x) - [AMD Instinct MI325X](https://www.aguasto.com/gpus/mi325x) - [AMD Instinct MI355X](https://www.aguasto.com/gpus/mi355x) - [AMD Instinct MI455X](https://www.aguasto.com/gpus/mi455x) - [AMD Helios](https://www.aguasto.com/gpus/helios) - [GLM-4.5](https://www.aguasto.com/models/glm-4-5) - [Qwen3-235B-A22B](https://www.aguasto.com/models/qwen3-235b-a22b) - [DeepSeek-R1](https://www.aguasto.com/models/deepseek-r1) - [Llama 3.3 70B Instruct](https://www.aguasto.com/models/llama-3-3-70b-instruct) - [Gemma 3 27B IT](https://www.aguasto.com/models/gemma-3-27b-it) - [gpt-oss-120b](https://www.aguasto.com/models/gpt-oss-120b) - [Kimi-K2-Instruct](https://www.aguasto.com/models/kimi-k2-instruct) - [vLLM](https://www.aguasto.com/inference-stack/vllm) - [SGLang](https://www.aguasto.com/inference-stack/sglang) - [TensorRT-LLM](https://www.aguasto.com/inference-stack/tensorrt-llm) - [NVIDIA Dynamo](https://www.aguasto.com/inference-stack/dynamo) - [NVIDIA Triton Inference Server](https://www.aguasto.com/inference-stack/triton) - [AMD ROCm](https://www.aguasto.com/inference-stack/rocm) - [Ray Serve](https://www.aguasto.com/inference-stack/ray-serve) - [Kubernetes GPU orchestration — NVIDIA device plugin](https://www.aguasto.com/inference-stack/kubernetes) - [Model weights are only the beginning of the memory budget](https://www.aguasto.com/learn/model-weights-and-runtime-memory) - [Why tokens per second is not enough to benchmark inference](https://www.aguasto.com/research/inference-benchmark-methodology) - [Prefill, decode and the cost of separating them](https://www.aguasto.com/learn/prefill-decode-and-disaggregated-serving) - [Choosing between vLLM, SGLang and TensorRT-LLM](https://www.aguasto.com/learn/choosing-an-inference-serving-framework) - [From Power to Inference—Building a Distributed AI Compute Network](https://www.aguasto.com/learn/from-power-to-inference) - [Dedicated compute and shared inference: compare the whole workload](https://www.aguasto.com/insights/dedicated-and-shared-inference-economics) - [Distributed inference reliability begins with the failure boundary](https://www.aguasto.com/learn/distributed-inference-reliability) - [Core Scientific: reading contracted, energized and billed capacity](https://www.aguasto.com/research/cases/core-scientific-capacity-disclosures) - [Why Modular AI Infrastructure Matters Now](https://www.aguasto.com/research/why-modular-ai-infrastructure-matters-now) - [Official LinkedIn](https://www.linkedin.com/company/aguasto) This is a generated representation of the public HTML page. Diagrams are conceptual. GPU and inference services are planned and are not currently available to book or call. The contact page describes the available inquiry flow.