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dima0000/ninfer-benchmarks

published by @dima0000

Alignment dataset tracked in the /datasets sub-catalog.

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Created on HF
2026-09-21
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Description

NInfer on one RTX 5090 — recorded benchmark evidence

Historical results from 19–20 September 2026, published by dima0000. This is a collection of benchmark evidence, not a model checkpoint or a live inference service. It preserves successful measurements, failed checks and incomplete experiments.

Hardware: one NVIDIA RTX 5090 with 32 GB VRAM per trial. Models: regular and uncensored Qwen3.8-27B with NVFP4 weights. Engine: NInfer commit 9e163eee4b8acec21ab0ac765107b6a3f287b217… See the full description on the dataset page: https://huggingface.co/datasets/dima0000/ninfer-benchmarks.

Tags

language:ensize_categories:n<1Kformat:csvmodality:tabularmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantregion:usbenchmarkinferenceninferrtx-5090qwennvfp4kv-cachespeculative-decoding

README current version from Hugging Face


pretty_name: NInfer RTX 5090 Benchmark Evidence
language:

  • en
    tags:
  • benchmark
  • inference
  • ninfer
  • rtx-5090
  • qwen
  • nvfp4
  • kv-cache
  • speculative-decoding
    configs:
  • config_name: summary
    data_files:
    • split: test
      path: data/measurements.csv

NInfer on one RTX 5090 — recorded benchmark evidence

Historical results from 19–20 September 2026, published by dima0000. This is a collection of benchmark evidence, not a model checkpoint or a live inference service. It preserves successful measurements, failed checks and incomplete experiments.

Hardware: one NVIDIA RTX 5090 with 32 GB VRAM per trial. Models: regular and uncensored Qwen3.8-27B with NVFP4 weights. Engine: NInfer commit 9e163eee4b8acec21ab0ac765107b6a3f287b217, CUDA 13.1; uncensored runs include the recorded frontend compatibility patch. Host CPU/RAM assignments and some serving configurations differed between rentals.

Models used

Model on Hugging Face Exact tested revision Used in
neroued/Qwen3.8-27B-nvfp4-NInfer f0b43ad436b9 Regular-model trial; downloaded as a preconverted NInfer artifact.
orcarouter/Qwen3.8-27B-Uncensored-NVFP4 96d4d0b66d94 First uncensored trial and retry; source checkpoint converted locally into NInfer format with the recorded compatibility patch and recipe.

The uncensored source repository is gated; users may need to sign in and accept its access conditions. The final NVFP4-head experiment also planned to use that same uncensored revision, but no weight download, conversion or GPU inference ran in that attempt. This dataset contains results and provenance, not model weights or a downloadable converted uncensored artifact.

Start here

Attempt Report What was actually measured
Regular model Load, NVFP4 KV, RAM cache Concurrency, long prompts, FP8/NVFP4 KV capacity and RAM restoration. The near-479K RAM extension completed only its cold phase.
First uncensored run Report, postmortem Frontend/tokenizer validation, short decoding, retrieval and physical-cache hit/miss diagnostics. Several intended experiments were unfinished.
Uncensored retry Final report, scheduler evidence Matched MTP comparison, near-capacity allocation and measured RAM-to-GPU KV restoration, including original failures and clarified follow-ups.
NVFP4 output/MTP-head experiment Incomplete report 57 CPU tests passed. GPU comparison NOT RUN. The pod was created after the prepared absolute deadline.

Machine-readable selected measurements are a derived summary, not additional experiments or raw statistical samples. Filter by metric, unit, workload and configuration before comparing rows. Full evidence is authoritative.

Performance summary

Single-request decoding: uncensored model

Workload Median decode tokens/s
Three 512-output-token requests, MTP enabled 149.34
Matched three short 128-output-token requests, MTP3 + proposal head 162.48
Same matched workload, no speculative backend 77.15

The matched median ratio was 2.106×. Requests were cold; model artifact, prompt order, seeds, temperature zero and output budgets were controlled. Draft counters confirmed the backend choice. This small comparison does not establish batch/long-context speedups or output-quality equivalence.

Short-prompt concurrency: regular model

About 1,005 input tokens plus 512 output tokens per request; FP8 KV and MTP3.

Concurrent requests Whole-wave aggregate output tokens/s Median first-token latency
1 155.9 0.126 s
2 281.9 0.192 s
4 457.6 0.385 s
8 708.9 0.616 s

These rates include cold prefill and completion of the entire wave; they are not decode-only rates. Sixteen simultaneous submissions to eight lanes completed at 724.6 aggregate tokens/s, with the excess requests queued—not sixteen active lanes. An eight-request workload with 27,844-token prompts achieved 299.1 aggregate output tokens/s including prefill.

Two uncensored four-request cold waves achieved 468.7 and 440.6 aggregate tokens/s, using different, much shorter prompts. An initial different four-way workload achieved 391.32 tokens/s. These are not controlled regular-versus-uncensored comparisons.

KV capacity and long-context behavior

Four-lane shared GPU KV capacity increased from 276,480 FP8 tokens to 495,360 NVFP4 tokens: +79.17%. The per-request ceiling remained 262,144. A matched large wave took 66.61 s with NVFP4 versus 63.35 s with FP8, about 5.1% longer. The demonstrated benefit was capacity, not throughput.

The uncensored retry submitted prompts of 261,557 / 131,644 / 65,839 / 32,958 tokens, generating 512 tokens each. All four marker checks passed. The whole wave took 171.44 s, about 11.95 aggregate output tokens/s including the huge cold prefills.

Allocated KV occupancy reached 494,080 / 495,360 slots (99.74%), with four running requests in the same sample. One was still prefilling and three were decode-ready. No sampled interval proved that all four entire prompts were simultaneously fully prefilled and resident. The earlier wave peaked at only two running requests. Both are retained; changes to submission order and prefix-cache settings were not isolated experimentally.

A separate regular-model 260,621-token prompt passed retrieval with a first-token latency of 101.08 s. Cold long-context prefill dominates latency. Shared capacity is not capacity per request.

RAM-backed history restoration

Uncensored follow-up: four independent histories, deliberately restricted 131,072-token GPU KV capacity, 16 GiB host KV budget, and a non-retained approximately 124K-token eviction request.

Phase Input tokens Reused tokens Four-request wall time
Cold roots 95,960 0 13.261 s
Warm continuations 97,096 96,174 0.967 s
Post-eviction continuations 98,418 97,403 1.188 s

Post-eviction continuations restored 1,771,978,752 bytes (1.6503 GiB) of main-plus-draft KV from host RAM to GPU and reused 98.97% of their input. All memory/tag checks passed in the clarified follow-up. The first scenario also restored KV but failed one peer-tag extraction; its overall failure is retained.

Phase output lengths differed, so cold/continuation timing ratios are not matched speedups. Host RAM preserves inactive histories; it does not enlarge the active GPU pool. Logical history correctness and physical cache reuse are distinct, and previous_response_id does not guarantee a cache hit. IDs and caches are process-local.

Latest head experiment: no GPU performance result

The implementation proposed W4A16 NVFP4 output and shortlist heads, not native W4A4 activation acceleration. Its 510.94 MiB calculated combined head-payload reduction is not measured VRAM savings. No GPU compilation, real-checkpoint conversion, numerical validation, throughput, draft-acceptance or quality comparison completed. Its failure was orchestration-related, not evidence for or against the proposed optimization.

Complete data and integrity

The collection contains captured JSON/JSONL, generated model replies, GPU CSV, reports, scripts/patches, source snapshots, metadata, logs, failure evidence and dated provider/billing snapshots. “All results” means all retained available evidence—not uncaptured/truncated output or unrun tests. Some evidence is duplicated between raw archives and browsable copies; do not count duplicates as independent measurements.

Safety, limitations and reuse

  • No model weights, private keys, account tokens or signed download manifests are included. Automated screening also checks nested archives; it is not an absolute guarantee against every conceivable sensitive datum.
  • Publication of technical metadata was authorized. Files retain historical local paths, cloud resource IDs, endpoint addresses and public host-key information for provenance.
  • All four trial pods were verified absent at their recorded cleanup checks. Old RUNNING fields, URLs and launch scripts are historical, not live-resource instructions. Do not connect to reassigned endpoints or execute deployment/guard scripts without fresh authorization and ownership verification.
  • The collector is a historical script tied to original Linux paths. Do not rerun it on this downloaded snapshot. The verifier only reads files and is suitable for checking the archive.
  • No broad quality, vision, head-quantization-fidelity, exhaustive Unicode or endurance conclusion is established. Retrieval/tag tests are bounded checks, not comprehensive evaluations. Billing values are dated snapshots; some were incomplete estimates.
  • See LICENSE-NOTES.md. Publication does not relicense included third-party code or checkpoint metadata.

To verify a downloaded repository snapshot:

sha256sum --check PUBLICATION-SHA256SUMS
sha256sum --check SHA256SUMS
python3 verify-archive.py

README history 5 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2026-09-22Add isolated RNG-only SM120 precision microbenchmarks6f9b59a10.9 KB
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  2. 2026-09-21Link both model repositories and exact tested revisionsfa8e1da10.9 KB
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  3. 2026-09-21Clarify configured lanes for the serialized uncensored benchmark4e535eb9.8 KB
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  4. 2026-09-21Publish four NInfer RTX 5090 attempts: results, raw evidence and limitationsaabc11c9.8 KB
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  5. 2026-09-21initial commitebda4d60 B
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Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

Community discussions 1 thread · 1 open · 1 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2026-09-22[bot] Conversion to Parquetopen1 💬#1
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ds = load_dataset("dima0000/ninfer-benchmarks")
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