{
  "_about": "fak position rows for the 'distributed' group. dim_id links each to a _taxonomy.json dimension.",
  "rows": [
    {
      "id": "prefill-decode-disaggregation",
      "dim_id": "prefill-decode-disaggregation",
      "axis": "Prefill-decode disaggregation: independent scaling of compute-bound prefill and memory-bound decode",
      "category": "parallelism",
      "competitor": "DistServe; Splitwise; SGLang-PD / vLLM disaggregated; llm-d (vLLM + NIXL KV connector)",
      "competitor_class": "sota",
      "competitor_value": null,
      "competitor_source": "https://rocm.blogs.amd.com/software-tools-optimization/disaggregation/README.html (2025)",
      "fak_value": null,
      "unit": "× goodput under SLO",
      "higher_is_better": true,
      "verdict": "no-claim",
      "status": "stub",
      "fak_commit": "",
      "fak_artifact": "",
      "fak_doc": "No P/D-disaggregation goodput/TTFT number in BENCHMARK-AUTHORITY.md or CLAIMS.md. fak runs a single fused forward (prefill+decode together); no disaggregated prefill/decode pool is built or measured.",
      "apples_to_apples": true,
      "comparison_note": "OUT OF SCOPE for the reuse kernel: fak does not disaggregate prefill from decode into independently-scaled pools; its differentiator is cross-agent prefix fusion on one kernel, not P/D separation. DistServe/Splitwise/SGLang-PD/llm-d numbers have no fak counterpart; named as out-of-scope rather than a measured gap.",
      "measured_on": "2026-06-23"
    },
    {
      "id": "phase-disaggregation-slo-isolation",
      "dim_id": "phase-disaggregation-slo-isolation",
      "axis": "Prefill-decode disaggregation & per-phase SLO isolation",
      "category": "parallelism",
      "competitor": "NVIDIA Dynamo; DistServe; llm-d; DeepSeek (production PD-disagg)",
      "competitor_class": "sota",
      "competitor_value": 7.4,
      "competitor_source": "https://developer.nvidia.com/blog/introducing-nvidia-dynamo-a-low-latency-distributed-inference-framework-for-scaling-reasoning-ai-models/ (2025-03)",
      "fak_value": null,
      "unit": "x request rate / SLO-attainment at >90%",
      "higher_is_better": true,
      "verdict": "no-claim",
      "status": "stub",
      "fak_commit": "",
      "fak_artifact": "",
      "fak_doc": "No prefill-decode disaggregation is shipped or claimed. fak's own engine runs a single co-located forward; on the GPU server it fronts SGLang TP=8 (which itself is not run in a disaggregated config in the committed GPU server artifacts).",
      "apples_to_apples": false,
      "comparison_note": "OUT OF SCOPE for a reuse kernel. PD-disaggregation with per-phase SLO isolation (DistServe 7.4x request rate / 12.6x tighter SLO; NVIDIA Dynamo Planner) is a serving-stack architecture; fak owns neither the prefill/decode scheduler nor the per-phase GPU pools. It is an adjudication/coherence plane that rides on whatever engine does this. no-claim.",
      "measured_on": "2026-06-23"
    },
    {
      "id": "expert-parallelism-large-moe",
      "dim_id": "expert-parallelism-large-moe",
      "axis": "Large-scale expert parallelism (EP) for giant MoE models",
      "category": "parallelism",
      "competitor": "SGLang; DeepSeek (DeepEP); vLLM",
      "competitor_class": "sota",
      "competitor_value": null,
      "competitor_source": "https://www.lmsys.org/blog/2025-05-05-large-scale-ep/ (2025-05-05)",
      "fak_value": null,
      "unit": "EP degree across nodes",
      "higher_is_better": true,
      "verdict": "no-claim",
      "status": "stub",
      "fak_commit": "",
      "fak_artifact": "",
      "fak_doc": "none — fak runs GLM-5.2 MoE experts on ONE datacenter GPU (CLAIMS Engine, commit 498a4ab), never EP across nodes",
      "apples_to_apples": true,
      "comparison_note": "OUT OF SCOPE for a single-box reuse kernel. fak DOES run GLM-5.2's MoE/FFN experts + router on its own pure k_q8_gemm GPU kernel (the in-kernel GLM-5.2 / DeepSeek-class path, #1010 / #1026; cosine=1.0, datacenter GPU, commit 498a4ab) — but on a SINGLE device, with no expert-parallel sharding across nodes and the DSA sparse-attention still host-side. fak's own-engine ceiling is ~7B (see max-model-and-moe-coverage); giant-MoE EP (the MLPerf v6.0 / DeepSeek-R1 671B reference regime) is delegated to SGLang/DeepEP, which fak fronts. No fak EP number exists; verdict no-claim.",
      "measured_on": "2026-06-23"
    },
    {
      "id": "data-parallel-attention",
      "dim_id": "data-parallel-attention",
      "axis": "Data-parallel (DP) attention / hybrid attention-FFN parallelism",
      "category": "parallelism",
      "competitor": "SGLang; vLLM",
      "competitor_class": "sota",
      "competitor_value": null,
      "competitor_source": "https://www.lmsys.org/blog/2025-07-17-mtp/ (2025-07-17)",
      "fak_value": null,
      "unit": "DP-attention throughput uplift",
      "higher_is_better": true,
      "verdict": "no-claim",
      "status": "stub",
      "fak_commit": "",
      "fak_artifact": "",
      "fak_doc": "none — no fak number exists",
      "apples_to_apples": true,
      "comparison_note": "OUT OF SCOPE for a single-device reuse kernel. DP attention is a multi-device memory-deduplication technique (eliminate KV duplication across GPUs); fak runs one in-kernel KV cache on one device and has no hybrid attention/FFN parallelism. fak's KV story is prefix-REUSE across agents on one cache (RadixAttention parity, kv-hit-rate row), an orthogonal axis from DP-attention's cross-device dedup. No fak number; verdict no-claim.",
      "measured_on": "2026-06-23"
    },
    {
      "id": "multi-node-scale-out",
      "dim_id": "multi-node-scale-out",
      "axis": "Multi-node scale-out (combined TP x PP x EP x DP across hosts)",
      "category": "parallelism",
      "competitor": "SGLang; NVIDIA Dynamo; TensorRT-LLM; NVIDIA GB200/GB300 NVL72",
      "competitor_class": "sota",
      "competitor_value": null,
      "competitor_source": "https://docs.dynamo.nvidia.com/dynamo/design-docs/disaggregated-serving (2025-03-18)",
      "fak_value": null,
      "unit": "GPUs in one served instance",
      "higher_is_better": true,
      "verdict": "no-claim",
      "status": "stub",
      "fak_commit": "",
      "fak_artifact": "",
      "fak_doc": "none — fak's own engine is single-box (CLAIMS GPU backends are all one device); the 8-GPU datacenter server run is SGLang-serves + fak-adjudicates",
      "apples_to_apples": true,
      "comparison_note": "OUT OF SCOPE for the reuse/adjudication kernel. fak's OWN engine runs on a single device (CPU, or one RTX 4070 / RX 7600 / M3 Pro / datacenter GPU). The only multi-GPU run in the evidence (8-GPU datacenter server, Qwen3.6-27B) is SGLang doing the TP scale-out with fak as the in-front gateway/adjudication plane — and there fak TRAILS raw SGLang on throughput (served-throughput-vs-sglang row). fak has no own TPxPPxEPxDP scale-out number; verdict no-claim.",
      "measured_on": "2026-06-23"
    }
  ]
}