KVCache simulator for LLM serving cluster routing research
Discrete-event simulator for evaluating KV cache-aware routing policies in prefill-disaggregated LLM serving clusters. Models a two-tier KV cache hierarchy (L0 GPU HBM + L1 CPU DRAM) with RDMA/PCIe link contention, architecture-derived roofline compute (MoE, MLA, DSA), and a cluster-wide meta-store for prefix-aware routing decisions. Includes 11 routing policies (random, round_robin, least_loaded, least_tokens, ttl_aware, precise, min_pd, cache_load, cache_score, estimated_ttft, prefix_affinity), HuggingFace config.json auto-parsing, built-in GPU hardware presets (H100/H800/H20/A100/B200), and ablation tooling for systematic policy comparison across real Alibaba serving traces. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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configs/glm5-8xb200-hf.yaml
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configs/glm5-8xb200-hf.yaml
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# GLM-5 using HuggingFace config.json + hardware preset.
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#
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# This config demonstrates the simplified format:
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# model.config_json — loads architecture from HF config.json
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# hardware.type — loads GPU specs from built-in preset
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#
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# Only deployment-specific fields need to be set explicitly.
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# Any field from config_json or the preset can be overridden in YAML.
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model:
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# Auto-detect architecture: MoE, MLA, DSA, head dims, etc.
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config_json: ../models/GLM-5/config.json
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name: glm-5 # override HF model_type
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dtype_bytes: 1 # BF16 (not in HF config.json)
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block_size_tokens: 512 # matches bailian-traces blksz_512
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hardware:
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type: 8xb200 # 8 x B200 SXM (192GB each)
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# Override preset values for this specific deployment:
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hbm_bytes: 500.0e9 # KV budget after FP8 weights + activations
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dram_bytes: 1.5e12 # ~1.5 TB usable CPU DRAM per node
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max_batch_slots: 256
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cluster:
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num_instances: 32
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meta_store:
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ttl_seconds: 300.0
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router:
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mode: min_pd
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precise_probe_latency_us: 50.0
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precise_probe_topk: 4
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load_alpha: 1.0
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prefix_k: 8
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sim:
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trace_path: bailian-traces/glm_coder_blksz_512_040915-040917.jsonl
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max_requests: null
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output_dir: runs/glm5_8xb200_hf
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sample_interval_s: 1.0
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seed: 42
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