# GLM-5 (zai-org/GLM-5) served as a single tensor-parallel instance on
# 8 x NVIDIA B200 SXM (192GB each, 1.5 TB aggregate HBM).
#
# GLM-5 is a 744B-total / 40B-active Mixture-of-Experts model (BF16),
# using DeepSeek Sparse Attention (DSA). The HF card does not publish
# layer/head shapes, so the values below are reasonable estimates based
# on the GLM-4.5 lineage; adjust once the official config.json is public.
#
# Hardware values below represent the *aggregate* of the 8-GPU TP group
# (one simulated "instance" == one 8xB200 serving replica). This is how
# the roofline in src/instance/compute.rs wants to see it: gpu_flops and
# gpu_mem_bw are the effective peaks seen by the TP'd model.
#
# Calibrate `flops_per_token_prefill` and `attn_quadratic_coeff` against
# measured prefill latency before trusting absolute TTFT numbers.

model:
  name: glm-5
  # --- estimates; refine from official config.json when available ---
  num_layers: 92
  num_kv_heads: 8              # GQA
  head_dim: 128
  dtype_bytes: 2               # BF16
  block_size_tokens: 16        # trace convention
  # Active-params-driven roofline: MoE activates ~40B params per token,
  # so non-attention prefill FLOPs/token ≈ 2 * 40e9 = 8e10.
  flops_per_token_prefill: 8.0e10
  # Quadratic attention term ≈ 2 * num_heads * head_dim. GLM-5 uses
  # DeepSeek Sparse Attention which is sub-quadratic in practice, so
  # this coefficient is an upper bound — lower it if your measurements
  # show DSA kicking in for long prompts.
  attn_quadratic_coeff:    2048.0
  bytes_per_token_prefill: 0.0

hardware:
  # Aggregate of 8 x B200 in one tensor-parallel group.
  gpu_flops:        1.80e16    # 8 * 2.25 PFLOPS BF16 dense
  gpu_mem_bw:       6.40e13    # 8 * 8 TB/s HBM3e
  # KV-cache budget after weights + activations. GLM-5 @ BF16 is ~1.49TB,
  # which barely fits in 1.5TB HBM; realistic serving uses FP8 weights
  # (~744GB), leaving ~500GB for activations + KV cache. Adjust if your
  # deployment uses a different weight dtype.
  hbm_bytes:        500.0e9
  dram_bytes:       1.5e12     # ~1.5 TB usable CPU DRAM / v6d per node
  pcie_bw:          128.0e9    # PCIe Gen6 x16 ~ 128 GB/s per direction
  pcie_latency_us:  4.0
  rdma_bw:          50.0e9     # ConnectX-7 400 Gbps ≈ 50 GB/s
  rdma_latency_us:  6.0
  max_batch_slots:  256
  prefill_chunk_tokens: 2048

cluster:
  num_instances: 8             # 8 TP replicas -> 64 B200s cluster-wide
  meta_store:
    ttl_seconds: 120.0
  router:
    mode: ttl_aware
    precise_probe_latency_us: 50.0
    precise_probe_topk: 4
    load_alpha: 1.0

sim:
  trace_path: qwen-bailian-usagetraces-anon/qwen_coder_blksz_16.jsonl
  max_requests: null
  output_dir: runs/glm5_8xb200
  sample_interval_s: 1.0
  seed: 42