Model ID
mindlab-research/Macaron-V1-Coding-Venti
Capabilities
Macaron V1 Coding Venti
Macaron V1 Coding Venti is a 753B parameter sparse Mixture-of-Experts coding specialist with 40B active parameters, created by merging the Macaron-V1-Venti L2 Coding LoRA into the GLM-5.2 BF16 base model. It is optimized for code understanding, repository-level software engineering, terminal use, and coding-agent workflows. The model uses 78 layers with 256 routed experts, activating 8 experts per token alongside 1 shared expert. Supporting a 1M-token context window, it provides a merged checkpoint without runtime LoRA routing for efficient deployment.
About this model
Macaron V1 Coding Venti is a 753B parameter sparse Mixture-of-Experts coding specialist with 40B active parameters, created by merging the Macaron-V1-Venti L2 Coding LoRA into the GLM-5.2 BF16 base model. It is optimized for code understanding, repository-level software engineering, terminal use, and coding-agent workflows. The model uses 78 layers with 256 routed experts, activating 8 experts per token alongside 1 shared expert. Supporting a 1M-token context window, it provides a merged checkpoint without runtime LoRA routing for efficient deployment.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (mindlab-research/Macaron-V1-Coding-Venti). 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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