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Community benchmark: RTX 5060 Ti + RTX 3090, Unsloth UD-IQ4_XS, 262K.

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Difficoltà
1/5
Tempo stimato
Meno di un'ora
Idoneità per principianti
85/100
Tipo di issue
Documentazione
Chiarezza
Specificata chiaramente
Stato di attività
Attiva
Stack tecnologico
cpp
Ambito
documentation

Direzione di ricerca

Questa issue documenta un benchmark misurato con tools/strata_bench.py; inizia leggendo quello script e il formato per contribuire benchmark nel repository. Aggiungi l’hardware, la configurazione del modello, il metodo e i risultati riportati come voce di benchmark della community. Il lavoro è completato quando il rapporto è registrato con i limiti dichiarati, relativi solo alla velocità, e le misurazioni.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

Measured on 2026-10-18 with tools/strata_bench.py (suite 1, 3 runs per test, medians with ranges). Speeds are the engine's own timings; first-token times are measured by the client over HTTP. Speed only: no correctness check was run.

Hardware and software

  • GPU and VRAM: NVIDIA GeForce RTX 5060 Ti + NVIDIA GeForce RTX 3090, 40 GiB VRAM
  • CPU: AMD Ryzen 7 9700X 8-Core Processor (8 cores, 16 threads); installed RAM: 91.6 GiB; storage: not measured
  • OS: Linux 7.0.0-34-generic; GPU driver: 595.91.07; CUDA: 13.2
  • Strata: project 0.1.45, commit 1b7f1fe0; engine 0.1.45
  • Background workloads: Ubuntu desktop running, monitors attached to the RTX 5060 Ti; only very light background work
    during the run (the client application that normally uses the server was paused)
  • Power limits: RTX 5060 Ti 150 W (default 180 W), RTX 3090 250 W (default 350 W), set by the owner, all runs
  • PCIe: RTX 5060 Ti in the first slot at PCIe 4.0 x8 (14.4 GB/s measured); RTX 3090 in the second slot at PCIe 4.0 x4
    (CPU root port, 6.7 GB/s measured). The board cannot run the cards the other way round. Layer split
    auto: CUDA0 layers 0-6, CUDA1 layers 7-47
  • RAM: the model (about 94 GB, 3 GGUF shards) is larger than the installed RAM (91.6 GiB), so part of the experts is read
    from the SSD; about 10 GB of RAM were free while the server ran
  • Build: CUDA 13.2.2 (13.2.0/13.2.1 nvcc produce wrong results on sm_120, #892/#968). The engine is 0.1.45 plus one local
    change that only touches the Qwen3.6 path (src/qwen35), not Flash-Next

Model and configuration

  • Model and quantization: qwen3.8-flash-next-unsloth-ud-iq4_xs
  • Context: 262144 tokens; parallel slots: 1
  • Settings: setup's configuration, changed by hand: 262K context, --prompt-cache-root 256, conversation cache 4 GiB / 4 slots; thinking off for the suite; temperature 0
strata --pack unsloth-ud-iq4_xs --native Qwen3.8-Flash-Next-UD-IQ4_XS-00001-of-00003.gguf --expert-profile expert-profile.bin --expert-cache auto --prefill auto --spec 4 --spec-min-p 0.5 --mtp rt --max-context 262144 --kv int8 --prompt-cache-root 256 --kv-resident 32768 --conversation-cache-mib 4096 --conversation-cache-slots 4

Environment of the config: STRATA_HC_Q8=1, STRATA_ROUTE_TAIL_SKIP=0

Method

Run by python tools/strata_bench.py --long against the HTTP API of a Strata server (already running); model loading is not timed. One warm-up chat and a 1K prompt are discarded first. Every prompt starts with a unique tag, so the conversation cache cannot help the fresh-prompt tests.

  • short chat: the three questions of tools/ab_engine.py, up to 100 new tokens, 3 runs
  • 4K / 16K prompts: generated word lists sized by a calibration request, 24 new tokens, 3 fresh prompts each
  • agent session: system prompt and four tools, then 5 turns that each add about 1500 words of a project file and continue from the model's own earlier answers (the way agent clients resend a conversation), 64 new tokens per turn, 3 sessions; the cache numbers come from the answer's usage (cached_tokens) and timings (prompt_n)
  • concurrency (when run): N requests sent at the same moment, total tokens over the wall time of the burst, latency over all requests

Results

Configuration Actual prompt tokens Reused tokens Generated tokens Runs Prompt tok/s median and range Decode tok/s median and range TTFT seconds median and range
short chat 46 (43-51) 0 100 (100-100) 9 not measured 60.7 (51.9-68.7) 0.57 (0.46-0.81)
4K prompt, fresh 4019 (4019-4019) 0 (0-0) 24 cap 3 583.4 (581.6-584.2) not measured 6.91 (6.90-6.93)
16K prompt, fresh 16058 (16058-16058) 0 (0-0) 24 cap 3 1093.2 (1093.0-1098.0) not measured 14.71 (14.65-14.71)
32K prompt, fresh 32049 (32049-32049) 0 (0-0) 24 cap 3 1128.9 (1127.2-1130.4) not measured 28.42 (28.38-28.46)
agent session, follow-up turns 14594 (14590-14611) at the last turn see below up to 64 3 see below 75.3 (54.8-86.5) 6.81 (6.33-7.00)

Ranges are the lowest and highest of the runs; Runs counts requests that returned a token.

Agent session (conversation cache)

Cache hit over the follow-up turns: 68.8 % (68.6-68.8); earlier-turn tokens read again per session: 3 (3-72) (earlier turns that the cache did not hold; the 13720 (13717-13720) new tokens of the files are read once); first turn (nothing cached) first token: 4.91 s (4.91-4.92).

Turn (run 1) Prompt tokens Reused Read Prompt tok/s First token (s) Decode tok/s
0 609 0 609 124.7 4.905 49.9
1 3757 637 3120 453.7 6.903 54.8
2 5290 3821 1469 232.7 6.332 57.4
3 8723 5353 3370 483.4 6.995 73.5
4 11646 8784 2862 420.8 6.823 75.3
5 14590 11691 2899 426.8 6.813 61.4

Per-request data: results.json. Engine log lines from the start: engine-log-excerpt.txt (when this run started the server).

Correctness and limitations

Not measured: this is a speed suite. The conversation uses the model's own replies, so token counts differ a little between runs. Machine details come from the server, nvidia-smi/rocm-smi and the OS; background load and power limits were added by hand. The short chat reads only 46 tokens, so the prompt speed of short prompts is
dominated by a fixed start-up cost per request (about 2 s here).

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Preparare l'ambiente

Questo progetto non fornisce container di sviluppo, Dockerfile né guida per i contributori, quindi l'ambiente è a tuo carico: parti dal suo README e consulta la nostra guida al primo contributo per i passaggi generali.

Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

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