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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowNotImplementedError
Message: Cannot write struct type 'geometry' with no child field to Parquet. Consider adding a dummy child field.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1868, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 803, in finalize
self.write_rows_on_file()
~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
self._write_table(table)
~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 790, in _write_table
self._build_writer(inferred_schema=pa_table.schema)
~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 834, in _build_writer
self.pa_writer = pq.ParquetWriter(
~~~~~~~~~~~~~~~~^
self.stream,
^^^^^^^^^^^^
...<9 lines>...
},
^^
)
^
File "/usr/local/lib/python3.14/site-packages/pyarrow/parquet/core.py", line 1115, in __init__
self.writer = _parquet.ParquetWriter(
~~~~~~~~~~~~~~~~~~~~~~^
sink, schema,
^^^^^^^^^^^^^
...<20 lines>...
max_rows_per_page=max_rows_per_page,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
**options)
^^^^^^^^^^
File "pyarrow/_parquet.pyx", line 2430, in pyarrow._parquet.ParquetWriter.__cinit__
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'geometry' with no child field to Parquet. Consider adding a dummy child field.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1879, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
id string | status string | title string | published timestamp[s] | summary string | hardware dict | software dict | model dict | slo dict | workload dict | power dict | accuracy dict | runs int64 | variance_band_pct float64 | fleet dict | configs list | not_covered list | reproduce string | spec dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
h100-qwen3-30b-a3b-fp8-vllm-0-31 | published | Qwen3-30B-A3B FP8 on 1× H100 SXM with vLLM 0.31.0 | 2026-10-06T00:00:00 | Full-mode measurement on a Lambda Cloud 1× H100 SXM5 instance: 10-minute windows, 5 repeats at the SLO boundary, GSM8K 5-shot on 200 items × 3 seeds. Default vLLM configuration with a BF16 KV cache. Energy is GPU board power. | {
"vendor": "NVIDIA",
"accelerator": "NVIDIA H100 80GB HBM3",
"count": 1,
"rated_bw_tbs_per_gpu": 3.35,
"driver": "580.105.08",
"host": "Lambda Cloud instance"
} | {
"engine": "vLLM",
"engine_version": "0.31.0",
"kernels": "vllm/vllm-openai:latest"
} | {
"name": "Qwen3-30B-A3B-FP8",
"shape": "MoE",
"quantization": "FP8 weights, BF16 KV cache",
"parallelism": "1 replica × TP1"
} | {
"ttft_p99_ms": 2000,
"tpot_p99_ms": 50
} | {
"trace": "chat-mix-v1",
"input_tokens": "median 1,024 · p90 4,096",
"output_tokens": "median 256 · p90 1,024",
"arrival": "Poisson, rate swept to SLO boundary"
} | {
"source": "GPU board power sum (NVIDIA)",
"kind": "board",
"sampling_hz": 1,
"window": "600 s steady state"
} | {
"suite": "gsm8k-5shot",
"threshold_pts": 1
} | 5 | 2.2 | {
"cards": 64
} | [
{
"key": "baseline",
"label": "vllm-default",
"engine": "vLLM 0.31.0 · KV BF16",
"mbu_pct": 58.1,
"node_power_kw": 0.601,
"goodput_tok_s": 3995.9,
"ttft_p99_ms": 327,
"tpot_p99_ms": 37.3,
"slo_pass_count": 5,
"repeats": 5,
"throttle_counts": {
"sw_power_cap": 1870
... | [
"Whole-node power (no BMC access on this host)",
"Context lengths beyond the chat-mix-v1 trace"
] | tokenwatt run --spec lambda-qwen3-30b-a3b-fp8-tp1-dp1.spec.yaml --label 'vllm-default' --role baseline --out runs/vllm-default.json
tokenwatt report runs/vllm-default.json --title 'Qwen3-30B-A3B FP8 on 1× H100 SXM with vLLM 0.31.0' | {
"name": "lambda-qwen3-30b-a3b-fp8-tp1-dp1",
"hardware": {
"adapter": "nvidia",
"rated_bw_tbs_per_gpu": null,
"gpus": null,
"host": "Lambda Cloud instance"
},
"engine": {
"endpoint": "http://127.0.0.1:8000/v1",
"name": "vLLM",
"version": "0.31.0",
"kernels": "vllm/vllm-openai:la... |
lab-h100-qwen3-30b-a3b-kv-cache | draft | Qwen3-30B-A3B FP8 on 1× H100: BF16 vs FP8 KV cache | 2026-10-06T00:00:00 | Validation run on a Lambda Cloud 1× H100 SXM5 instance with vLLM 0.31.0. Quick mode: 120 s windows, 2 repeats, 50 GSM8K items with one seed. FP8 KV cache raised SLO goodput and tokens per joule but failed the accuracy gate on this small suite. | {
"vendor": "NVIDIA",
"accelerator": "NVIDIA H100 80GB HBM3",
"count": 1,
"rated_bw_tbs_per_gpu": 3.35,
"driver": "580.105.08",
"host": "Lambda Cloud instance"
} | {
"engine": "vLLM",
"engine_version": "0.31.0",
"kernels": "vllm/vllm-openai:latest"
} | {
"name": "Qwen3-30B-A3B-FP8",
"shape": "MoE",
"quantization": "FP8 weights; KV cache varies by configuration",
"parallelism": "1 replica × TP1"
} | {
"ttft_p99_ms": 2000,
"tpot_p99_ms": 50
} | {
"trace": "chat-mix-v1",
"input_tokens": "median 1,024 · p90 4,096",
"output_tokens": "median 256 · p90 1,024",
"arrival": "Poisson, rate swept to SLO boundary"
} | {
"source": "GPU board power sum (NVIDIA)",
"kind": "board",
"sampling_hz": 1,
"window": "120 s steady state"
} | {
"suite": "gsm8k-5shot",
"threshold_pts": 1
} | 2 | 1 | {
"cards": 64
} | [
{
"key": "baseline",
"label": "vllm-default",
"engine": "vLLM 0.31.0 · KV BF16",
"mbu_pct": 56.6,
"node_power_kw": 0.637,
"goodput_tok_s": 4572.3,
"ttft_p99_ms": 340,
"tpot_p99_ms": 44.6,
"slo_pass_count": 2,
"repeats": 2,
"throttle_counts": {
"sw_power_cap": 199
... | [
"Whole-node power (cloud VM: no BMC access)",
"Context lengths beyond the chat-mix-v1 trace",
"Quick mode: short windows, few repeats — validation only, not for publishing",
"Whole-node power: energy figures are GPU board power only (no CPUs, memory, NICs, fans or PSU losses)"
] | tokenwatt run --spec lambda-qwen3-30b-a3b-fp8-tp1-dp1.spec.yaml --label 'vllm-default' --role baseline --out runs/vllm-default.json
tokenwatt run --spec lambda-qwen3-30b-a3b-fp8-tp1-dp1.spec.yaml --label 'vllm-fp8kv' --role tuned --out runs/vllm-fp8kv.json
tokenwatt report runs/vllm-default.json runs/vllm-fp8kv.json -... | {
"name": "lambda-qwen3-30b-a3b-fp8-tp1-dp1",
"hardware": {
"adapter": "nvidia",
"rated_bw_tbs_per_gpu": null,
"gpus": null,
"host": "Lambda Cloud instance"
},
"engine": {
"endpoint": "http://127.0.0.1:8000/v1",
"name": "vLLM",
"version": "0.31.0",
"kernels": "vllm/vllm-openai:la... |
TokenWatt inference reports
Published TokenWatt Bench reports, as shown on tokenwatt.io/reports. Each report gives SLO goodput (the highest request rate meeting p99 TTFT and TPOT targets), tokens per joule, memory-bandwidth utilization and an accuracy gate for each configuration measured. Method: methodology.
h100-qwen3-30b-a3b-fp8-vllm-0-31.json— H100 SXM, Qwen3-30B-A3B-FP8, vLLM 0.31.0, full mode: 3,996 output tok/s at 9.5 req/s within SLOlab-h100-qwen3-30b-a3b-kv-cache.json— BF16 vs FP8 KV cache on the same setup (Lab notes #1)
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