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- engine = "vllm"
- name = "Qwen-AgentWorld-35B-A3B (vLLM)"
- docker_image = "ghcr.io/timothystewart6/vllm-gb10:v0.23.0-gb10.1"
- model = "Qwen/Qwen-AgentWorld-35B-A3B"
- required_nodes = 2
- tensor_parallel_size = 2
- pipeline_parallel_size = 1
- distributed_backend = "mp"
- # 35B total / 3B active MoE — world model for agentic environment simulation.
- # 2× DGX Spark (GB10), 128 GB shared each.
- # TP=2 → ~35 GB/node for weights, room for 262K context.
- # Requires --language-model-only (no vision weights in checkpoint).
- entrypoint = ["vllm", "serve"]
- args = """
- --port 30000
- --max-model-len 262144
- --reasoning-parser qwen3
- --language-model-only
- --trust-remote-code
- --gpu-memory-utilization 0.90
- --enable-prefix-caching
- """
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