--- title: "Model Index | Aguasto" description: "Curated open-weight checkpoint references, license distinctions, model memory considerations and serving documentation. Research references, not available Aguasto endpoints." canonical: "https://www.aguasto.com/models" last-updated: "2026-10-12" --- # Model Index | Aguasto Canonical HTML: [Model Index](https://www.aguasto.com/models) > Curated open-weight checkpoint references, license distinctions, model memory considerations and serving documentation. Research references, not available Aguasto endpoints. [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) Aguasto / models ## Models. In context. A curated open-weight index connecting checkpoint identity, licensing, serving software and memory requirements. These are research references, not available Aguasto model endpoints. 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. [Compare model references →](https://www.aguasto.com/models/compare) [Estimate model weight memory →](https://www.aguasto.com/tools) Search modelsCategoryAllZ.aiQwenDeepSeekMetaGoogle DeepMindOpenAIMoonshot AICharacteristicAllApache 2.0Gemma Terms of UseLlama 3.3 Community LicenseMITMIT (model weights)Modified MITimage-inputtext-inputtext-outputClear filters 7 of 7 references Z.ai ### GLM-4.5 Mixture of experts; hybrid thinking and non-thinking text model. MIT (model weights). Public checkpoint reference. Research reference · no Aguasto deployment [GLM-4.5](https://www.aguasto.com/models/glm-4-5) Qwen ### Qwen3-235B-A22B Causal mixture-of-experts language model; thinking and non-thinking modes. Apache 2.0. Public checkpoint reference. Research reference · no Aguasto deployment [Qwen3-235B-A22B](https://www.aguasto.com/models/qwen3-235b-a22b) DeepSeek ### DeepSeek-R1 Mixture-of-experts reasoning language model based on DeepSeek-V3-Base. MIT. Public checkpoint reference. Research reference · no Aguasto deployment [DeepSeek-R1](https://www.aguasto.com/models/deepseek-r1) Meta ### Llama 3.3 70B Instruct Dense autoregressive transformer with grouped-query attention. Llama 3.3 Community License. Publisher access gate applies. Research reference · no Aguasto deployment [Llama 3.3 70B Instruct](https://www.aguasto.com/models/llama-3-3-70b-instruct) Google DeepMind ### Gemma 3 27B IT Multimodal transformer; text decoder with vision encoder. Gemma Terms of Use. Publisher access gate applies. Research reference · no Aguasto deployment [Gemma 3 27B IT](https://www.aguasto.com/models/gemma-3-27b-it) OpenAI ### gpt-oss-120b Mixture-of-experts reasoning language model. Apache 2.0. Public checkpoint reference. Research reference · no Aguasto deployment [gpt-oss-120b](https://www.aguasto.com/models/gpt-oss-120b) Moonshot AI ### Kimi-K2-Instruct Mixture-of-experts language model with Multi-head Latent Attention. Modified MIT. Public checkpoint reference. Research reference · no Aguasto deployment [Kimi-K2-Instruct](https://www.aguasto.com/models/kimi-k2-instruct) ## 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. 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