Model ID
mindlab-research/Macaron-V1-Tall
Capabilities
Macaron V1 Tall
Macaron V1 Tall is a Mixture of LoRA (MoL) model built on Qwen3.6-35B-A3B for personal intelligence, tool use, coding, and Generative UI. It combines four LoRA specialists for Chat, Agent, Coding, and GenUI, with an L0 router selecting the appropriate specialist per request. The underlying MoE architecture has 40 layers, 2,048 hidden size, 256 experts, and 8 experts activated per token. Supporting a 262K-token context window, Macaron V1 Tall is designed for personal-agent workflows, repository-level coding, tool use, and UI-driven applications.
About this model
Macaron V1 Tall is a Mixture of LoRA (MoL) model built on Qwen3.6-35B-A3B for personal intelligence, tool use, coding, and Generative UI. It combines four LoRA specialists for Chat, Agent, Coding, and GenUI, with an L0 router selecting the appropriate specialist per request. The underlying MoE architecture has 40 layers, 2,048 hidden size, 256 experts, and 8 experts activated per token. Supporting a 262K-token context window, Macaron V1 Tall is designed for personal-agent workflows, repository-level coding, tool use, and UI-driven applications.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (mindlab-research/Macaron-V1-Tall). Released under MIT. Further reading: Blog
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on B200 · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
NVIDIA HGX B200
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