UW-SyFI/servingstudio-workload

ServingStudio workload captures

This repository holds the routing data that ServingStudio Sim needs to cost the MoE layers of a model. Each capture measures one model on one workload. In this card, "model" means everything that changes which tokens are routed: the checkpoint, the serving engine, and the speculative proposer with its draft length k. The workload is a trace shape plus a prompt source.

The deployment a capture was taken on (GPU, TP/EP/DP, PD or AFD) is not part of the key. A capture is recorded once and then bound by every deployment of the same model on the same workload: EP4, EP8 and both pools of a PD deployment all read the same files. Each capture's provenance.json records its deployment under server.

Licensed under Apache 2.0. The files hold request lengths, expert ids and counts, not prompt text.

Layout

<model>/<engine>[_<proposer>_k<k>]/<workload>/capture/<YYYYMMDD>/
    provenance.json         every input that produced the capture, and how each value was established
    trace.csv               the request trace the capture actually replayed (and, for some
                            speculative captures, the acceptance each request recorded)
    popularity.json         aggregate expert counts             (popularity captures; beside some corpora)
    draft_popularity.json   the MTP draft layer's counts        (speculative popularity captures)
    manifest.json           corpus dimensions and checksum      (per-token captures)
    routes.u16              per-token routed experts            (per-token captures)

• <model> names the checkpoint, for example glm52_nvfp4. The exact Hugging Face id and revision are in provenance.json.
• The second level is the engine (vllm, sglang). When the model serves with a speculative proposer, the proposer and its draft length k are appended, as in vllm_mtp_k5, vllm_dflash2_k7 or vllm_dspark_k7.
• <YYYYMMDD> is the date the capture was taken. A second measurement of the same key is a replicate: it gets a new date directory next to the first and never overwrites it. Its provenance.json names the first capture in replicate_of.
• Data files never change after upload. Only provenance.json may be revised.
• Some per-token captures also carry the popularity.json (and draft_popularity.json) of a popularity replay of the same model and trace. Each file's files.<name>.extra.same_replay_as_corpus says whether it came from the corpus replay itself or from a separate one in the same Slurm job.

Nothing reads this directory scheme by name. Presets give each capture's full path, so the layout exists for people browsing the repo.

Referencing a capture

Pin a commit, never a branch:

hf://datasets/UW-SyFI/servingstudio-workload@<commit-sha>/glm53_nvfp4/vllm_dflash2_k7/diverse_100/capture/20260922/

The datasets/ prefix is part of the path, because this is a dataset repo. huggingface_hub calls need repo_type="dataset".

File formats

trace.csv

The independent-request CSV that ServingStudio Sim and req-frontend share:

id,input_len,output_len,arrival_time

arrival_time is in milliseconds. The file is the exact one the capture replayed, byte for byte, except for the column below. Whether arrival_time was honoured depends on the replay policy recorded in provenance.json under workload.arrival: a saturated replay ignores it. When a workload also has a reviewed spec in the ServingStudioSim git repo, provenance.json gives its path in workload.spec (for example trace/diverse_100.csv).

A speculative capture whose pass recorded each request's decode progress adds one column, accept_rate, the simulator's speculative tag:

id,input_len,output_len,arrival_time,accept_rate

accept_rate is the acceptance that request had in this capture, a JSON list with one probability per draft position: the chance position i is accepted given that the positions before it were (a rejection at the first position counts as a trial). It comes from the same pass as the routing, its per-step drafted_tokens and accepted_draft_tokens. A request with no draft reaching a position takes the pooled rate of the capture's requests there. provenance.json gives, under files["trace.csv"].extra, the source files and their sha256 (accept_rate.sources), the pooled rates (which equal the server's own counters), how many (request, position) cells took them, and the sha256 of the trace without the column (requests.sha256). A speculative capture without this column recorded only the run's aggregate counters.

popularity.json: aggregate expert counts

counts_by_layer[layer][logical_expert] counts routed-token assignments to logical experts. The counts are taken after EPLB's physical-to-logical mapping and the EP-group reduction, so they are logical demand and do not depend on expert placement. counts_all_layers sums the counts over layers. The probabilities_* fields are the same counts normalized. Four schema versions appear here:

schema_version	Adds	aggregation.scope
1	record_count, num_moe_layers, num_logical_experts, counts and probabilities only; no EP fields, no aggregation block	—
2	expert_parallel_size, experts_per_rank, experts_per_token, count_semantics, expert_partitioning, an aggregation block	all_captured_eplb_steps
3	a per-step token ceiling: raw_record_count, discarded_oversized_*, max_tokens_per_step	captured_eplb_steps_within_token_ceiling
4	model_role (target or draft) and a replay time window: replay_*_monotonic_ns, observed_monotonic_ns_*, max_forwards_per_step	replay_window_within_role_specific_token_ceiling

expert_parallel_size and experts_per_rank describe the deployment the counts were captured on, not the counts themselves. A consumer takes EP from its own binding. JSON schemas for v2–v4 are in ServingStudioSim at alignment/schema/expert_popularity_v{2,3,4}.schema.json.

draft_popularity.json is the schema-4 model_role: draft file from the same replay. For MTP it covers the one MTP MoE layer.

manifest.json + routes.u16: per-token routes

routes.u16 is a raw array of little-endian u16, in C order [token, layer, top_k]. Each value is a logical expert id. manifest.json gives the dimensions and an integrity checksum:

{"schema_version": 1, "data_file": "routes.u16", "num_tokens": 135144,
 "num_layers": 75, "num_experts": 256, "top_k": 8, "checksum_fnv1a64": 9937083654806801569}

checksum_fnv1a64 is 64-bit FNV-1a over the bytes of routes.u16 (offset basis 0xcbf29ce484222325, prime 0x100000001b3). The simulator's loader checks it. The file size is num_tokens * num_layers * top_k * 2 bytes.

The current corpora cover accepted generated tokens only. They have no prompt routes and no routes for rejected drafts. provenance.json lists the model layer index of each corpus layer (files["routes.u16"].extra.hub_provenance.model_layer_indices) and where each request's tokens start and end (request_segments). Corpora copied from the old model repo keep these under extra.hub_provenance; corpora staged from their run directory keep the run's own provenance file under extra.run_provenance, and, when that file has no layer list, give it as extra.model_layer_indices.

provenance.json

Key	Meaning
model	checkpoint, revision, and revision_inferred. revision_inferred is true when the server was given a repo id and the revision is the only snapshot that was in its cache.
engine	name, and version as the engine reported it at startup
proposer	null, or kind, k, checkpoint and revision
workload	name, spec (git path under trace/, or null), prompts, requests, concurrency, and arrival (the replay policy)
server	the deployment the capture ran on: gpu, tp, ep, dp, plus other (full server argv, engine fork commit, and so on)
captured	date, the producing run directory (host-qualified), and the Slurm job
files	one entry per data file: sha256, copied_from, kind, schema_version, scope, and extra for structured detail
replicate_of	the capture this one repeats, or null
missing	facts that could not be established
notes	how each value was established, with every check that was run

Unknown values are null; nothing is guessed.

Captures

Capture	Checkpoint	Engine	Proposer	Workload	Captured on	Files	Data
deepseek_v41_flash/vllm/diverse_100/capture/20260924/	deepseek-ai/DeepSeek-V4.1-Flash	vLLM	—	diverse_100 (100 req, enwik9)	B200 TP4 EP4 DP1	manifest.json, routes.u16, popularity.json, trace.csv	135,144 tokens x 40 layers x top-6 of 384; popularity v3 from the same replay, 9,049 records
deepseek_v4_flash/vllm/shape_grid_128/capture/20260822/	deepseek-ai/DeepSeek-V4-Flash-0731	vLLM	—	shape_grid_128 (128 req, enwik8)	H200 TP1 EP4 DP4	popularity.json, trace.csv	v2, 43 layers x 256 experts, 5,296 records
deepseek_v4_flash/vllm_dspark_k7/diverse_100/capture/20260922/	deepseek-ai/DeepSeek-V4-Flash-0731	vLLM	DSpark k=7 (draft in the checkpoint, greedy draft sampling)	diverse_100 (100 req, enwik9)	B200 TP4 EP4 DP1	manifest.json, routes.u16, trace.csv	135,144 tokens x 43 layers x top-6 of 256
glm52_fp8/vllm/ctx8k_out1k/capture/20260803/	zai-org/GLM-5.2-FP8	vLLM	—	ctx8k_out1k (256 req, enwik8)	H200 TP1 EP8 DP8	popularity.json, trace.csv	v1, 75 layers x 256 experts, 11,296 records
glm52_nvfp4/sglang/diverse_100/capture/20260829/	nvidia/GLM-5.2-NVFP4	SGLang	—	diverse_100 (100 req, enwik9)	B200 TP4 EP1 DP1	popularity.json, trace.csv	v3, 75 layers x 256 experts, 1 record (whole replay)
glm52_nvfp4/vllm/c32_long/capture/20260824/	nvidia/GLM-5.2-NVFP4	vLLM	—	c32_long (64 req, enwik9)	B200 TP4 EP4 DP1	popularity.json, trace.csv	v3, 75 layers x 256 experts, 532 records
glm52_nvfp4/vllm/diverse_100/capture/20260830/	nvidia/GLM-5.2-NVFP4	vLLM	—	diverse_100 (100 req, enwik9)	B200 TP4 EP4 DP1	popularity.json, trace.csv	v3, 75 layers x 256 experts, 13,577 records
glm52_nvfp4/vllm/diverse_100/capture/20260922/	nvidia/GLM-5.2-NVFP4	vLLM	—	diverse_100 (100 req, enwik9)	B200 TP4 EP4 DP1	manifest.json, routes.u16, trace.csv	135,144 tokens x 75 layers x top-8 of 256; replicate of 20260830, on the v0.28 fork
glm52_nvfp4/vllm_mtp_k5/diverse_100/capture/20260830/	nvidia/GLM-5.2-NVFP4	vLLM	MTP k=5	diverse_100 (100 req, enwik9)	B200 TP4 EP4 DP1	popularity.json, draft_popularity.json, trace.csv	v4 target (75 layers) + draft (1 layer), 7,973 records
glm52_nvfp4/vllm_mtp_k5/diverse_100/capture/20260902/	nvidia/GLM-5.2-NVFP4	vLLM	MTP k=5	diverse_100 (100 req, enwik9)	B200 TP8 EP8 DP1	popularity.json, draft_popularity.json, trace.csv	v4 target + draft, 4,007 records; replicate of 20260830
glm52_nvfp4/vllm_mtp_k5/diverse_100/capture/20260922/	nvidia/GLM-5.2-NVFP4	vLLM	MTP k=5	diverse_100 (100 req, enwik9)	B200 TP4 EP4 DP1	manifest.json, routes.u16, popularity.json, draft_popularity.json, trace.csv	135,144 tokens x 75 layers (no MTP layer) x top-8 of 256; popularity v4 target + draft from a separate replay, 6,780 records; replicate of 20260830, on the v0.28 fork
glm52_nvfp4/vllm_mtp_k5/quadrant_c48/capture/20260923/	nvidia/GLM-5.2-NVFP4	vLLM	MTP k=5	quadrant_c48 (192 req, enwik9)	B200 TP4 EP4 DP1	manifest.json, routes.u16, trace.csv	307,008 tokens x 76 layers (75 body + MTP) x top-8 of 256
glm53_flash_fp8/vllm/balanced_c32/capture/20260924/	zai-org/GLM-5.3-Flash	vLLM	—	balanced_c32 (256 req, enwik9)	B200 TP4 EP4 DP1	manifest.json, routes.u16, trace.csv	48,896 tokens x 42 layers x top-8 of 288
glm53_flash_fp8/vllm/diverse_100/capture/20260925/	zai-org/GLM-5.3-Flash	vLLM	—	diverse_100 (100 req, enwik9)	B200 TP4 EP4 DP1	manifest.json, routes.u16, trace.csv	135,144 tokens x 42 layers x top-8 of 288
glm53_flash_fp8/vllm_mtp_k5/diverse_100/capture/20260922/	zai-org/GLM-5.3-Flash	vLLM	MTP k=5	diverse_100 (100 req, enwik9)	B200 TP4 EP4 DP1	manifest.json, routes.u16, popularity.json, draft_popularity.json, trace.csv	135,144 tokens x 42 layers (no MTP layer) x top-8 of 288; popularity v4 target + draft from a separate replay, 2,703 records
glm53_nvfp4/vllm_dflash2_k7/diverse_100/capture/20260920/	incoai/GLM-5.3-NVFP4	vLLM	DFlash2 k=7 (incoai/GLM-5.3-DFlash2)	diverse_100 (100 req, enwik9)	B200 TP4 EP4 DP1	manifest.json, routes.u16, trace.csv	135,144 tokens x 75 layers x top-8 of 256; replicate of 20260922
glm53_nvfp4/vllm_dflash2_k7/diverse_100/capture/20260922/	incoai/GLM-5.3-NVFP4	vLLM	DFlash2 k=7 (incoai/GLM-5.3-DFlash2)	diverse_100 (100 req, enwik9)	B200 TP4 EP4 DP1	manifest.json, routes.u16, trace.csv	135,144 tokens x 75 layers x top-8 of 256
qwen36_35b_a3b_fp8/vllm/ctx8k_out1k/capture/20260818/	Qwen/Qwen3.6-35B-A3B-FP8	vLLM	—	ctx8k_out1k (256 req, enwik8)	H200 TP2 EP2 DP1	popularity.json, trace.csv	v2, 40 layers x 256 experts, 4,170 records
qwen3_235b_thinking_fp8/vllm/ctx8k_out1k/capture/20260803/	Qwen/Qwen3-235B-A22B-Thinking-2507-FP8	vLLM	—	ctx8k_out1k (256 req, enwik8)	H200 TP4 EP4 DP1	popularity.json, trace.csv	v1, 94 layers x 128 experts, 4,172 records

The workloads:

• diverse_100: ServingStudioSim trace/diverse_100.csv. It has 100 requests with input 256–131,070 and output 1–8,192 tokens, both log-uniform, and Poisson arrivals at a mean of 1 req/s. The four 2026-08 vLLM and SGLang popularity captures, and the GLM-5.3-Flash no-spec corpus, replay it at its trace times with no client concurrency cap. The other corpora, and the popularity files beside them, replay it at trace times with at most 64 requests in flight.
• c32_long: ServingStudioSim trace/c32_long.csv. 64 requests, eight of each pair in input {256, 1024, 2048, 4096} x output {128, 256}. All arrive at t = 0 and replay saturated at concurrency 32.
• ctx8k_out1k: ServingStudioSim trace/ctx8k_out1k.csv. 256 requests of 8,192 input and 1,024 output tokens, all at t = 0, at concurrency 64. The Qwen3.6 capture's trace.csv has the same rows but writes arrival_time as 0.000000, so it is not a byte copy of the spec.
• shape_grid_128: ServingStudioSim trace/shape_grid_128.csv. Four requests for each pair in input {128 … 16,384} (8 values) x output {32, 128, 512, 1024}. All arrive at t = 0 and replay at concurrency 64.
• quadrant_c48: no trace/ spec. Case 07 of the GLM-5.2 spec5 alignment campaign, with 192 requests over eight IO shapes, saturated at concurrency 48.
• balanced_c32: ServingStudioSim trace/balanced_c32.csv. 256 requests, 32 of each pair in input {256, 1024, 2048, 4096} x output {128, 256}, saturated at concurrency 32.

The prompt source is part of the workload. The H200 captures drew their prompts from enwik8 and the B200 captures from enwik9. Each provenance.json records the prompt file's checksums.