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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 3 new columns ({'machine', 'model', 'size'}) and 7 missing columns ({'reported_by', 'before_or_compared_with', 'mtp', 'model_and_file', 'gpu', 'context', 'cpu_ram_system'}).
This happened while the csv dataset builder was generating data using
hf://datasets/VecSzn/polystrata-benchmarks/readme_speeds.csv (at revision a41d4346f531cd8b506e1c7d201b62eb827a8350), ['hf://datasets/VecSzn/polystrata-benchmarks@a41d4346f531cd8b506e1c7d201b62eb827a8350/community_reports.csv', 'hf://datasets/VecSzn/polystrata-benchmarks@a41d4346f531cd8b506e1c7d201b62eb827a8350/readme_speeds.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 784, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 795, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
model: string
size: string
machine: string
writes_tokens_per_s: string
reads_tokens_per_s: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 884
to
{'reported_by': Value('string'), 'gpu': Value('string'), 'cpu_ram_system': Value('string'), 'model_and_file': Value('string'), 'context': Value('string'), 'mtp': Value('string'), 'writes_tokens_per_s': Value('string'), 'reads_tokens_per_s': Value('string'), 'before_or_compared_with': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
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 1850, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 3 new columns ({'machine', 'model', 'size'}) and 7 missing columns ({'reported_by', 'before_or_compared_with', 'mtp', 'model_and_file', 'gpu', 'context', 'cpu_ram_system'}).
This happened while the csv dataset builder was generating data using
hf://datasets/VecSzn/polystrata-benchmarks/readme_speeds.csv (at revision a41d4346f531cd8b506e1c7d201b62eb827a8350), ['hf://datasets/VecSzn/polystrata-benchmarks@a41d4346f531cd8b506e1c7d201b62eb827a8350/community_reports.csv', 'hf://datasets/VecSzn/polystrata-benchmarks@a41d4346f531cd8b506e1c7d201b62eb827a8350/readme_speeds.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
reported_by string | gpu string | cpu_ram_system string | model_and_file string | context string | mtp string | writes_tokens_per_s string | reads_tokens_per_s string | before_or_compared_with string |
|---|---|---|---|---|---|---|---|---|
u/Present_Resource4022 | RTX 4070 SUPER 12 GB | CPU not stated; 32 GB DDR5-6000; Windows 11 | Qwen3.6-35B-A3B, HauhauCS uncensored Aggressive TTQ Q4_K_M | about 8K / about 32K | on | 140 / 100 | 2,800 to 3,300 | llama.cpp at 8K: about 77 writing and 2,600 reading, settings not stated |
u/hope_it_helps | RTX 4070 Ti 12 GB | not stated | Ornith 1.5 APEX-I-Compact | not stated | not stated | about 90, 60 at the slowest | not stated | usually 40 before, engine not stated |
u/Sensitive_Song4219 | RTX 5060 Laptop 8 GB | Core Ultra 9 275HX; 32 GB DDR5; system not stated | Tiel Coder 35B-A3B, file not stated | not stated | not stated | maybe 20% faster than before, no figure | 1,000 to 1,500; 1,016 at the slowest on a large read, over 1,400 in an auto-compact | 200 to 350 reading before, engine not stated |
iMilnb | RX 7600 8 GB (gfx1102), the AMD beta | Ryzen 5 5600X; 30 GB; Linux, ROCm from AMD's wheels | Qwen3.6-35B-A3B UD-Q2_K_XL, and Ornith 1.5 APEX-I-Mini | 32K; the 24,000-token prompt is 31,834 tokens; --kv-whole | on | Qwen3.6: 74 in a short chat, 53 after the long prompt; Ornith: 40 and 43 | Qwen3.6 481, Ornith 444 | the first run of this engine on an AMD card, through the author's own port (#11) |
biurquezjoselina-design | RTX 5090 D v2 24 GB | Core i7-14700KF, 13 threads; 128 GB DDR5; Windows 10 | GLM 5.3 Flash 3.0BIT (neuralll), 113.6 GB, with the draft block; 1,483 experts in VRAM | 32K, and 256K to 1M set in the run config | on | 26 to 30 after a short prompt, 23 to 30 after 2.5K to 10K | 213 to 395 at 2.5K to 10K; 488 at 112K down to 286 at 557K in one request each, every planted fact found | the first run of GLM on Windows; this project's 32 GB Linux pod reads 29K at 740 to 820 |
null | null | null | null | null | null | 94 | 2,650 | null |
null | null | null | null | null | null | 79 | 2,090 | null |
null | null | null | null | null | null | 62 | 1,750 | null |
null | null | null | null | null | null | 53 | 1,620 | null |
null | null | null | null | null | null | 55 | 2,180 | null |
null | null | null | null | null | null | 60 | 1,160 | null |
null | null | null | null | null | null | 52 | 1,110 | null |
null | null | null | null | null | null | 44 | 1,420 | null |
null | null | null | null | null | null | 59-66 | 820 | null |
null | null | null | null | null | null | 50-52 | 1,060-1,080 | null |
null | null | null | null | null | null | 59-66 | 1,300-1,310 | null |
null | null | null | null | null | null | 94-100 | 1,690 | null |
null | null | null | null | null | null | 123-124 | 1,590 | null |
null | null | null | null | null | null | 82-86 | 1,560-1,570 | null |
null | null | null | null | null | null | 394-395 | 6,350-6,550 | null |
null | null | null | null | null | null | 450 | 6,400-6,590 | null |
null | null | null | null | null | null | 363-401 | 6,030-6,690 | null |
null | null | null | null | null | null | 76-82 | 1,620 | null |
null | null | null | null | null | null | 80-86 | 1,550-1,560 | null |
null | null | null | null | null | null | 59-63 | 1,570-1,580 | null |
null | null | null | null | null | null | 316-319 | 6,830-6,860 | null |
null | null | null | null | null | null | 289-300 | 6,300-6,380 | null |
null | null | null | null | null | null | 289-298 | 6,040-6,390 | null |
null | null | null | null | null | null | 92-93 | 2,240 | null |
null | null | null | null | null | null | 101-104 | 2,150-2,160 | null |
null | null | null | null | null | null | 85 | 2,150 | null |
null | null | null | null | null | null | 353-356 | 7,880-8,230 | null |
null | null | null | null | null | null | 327-340 | 7,380-8,000 | null |
null | null | null | null | null | null | 335-346 | 7,040-8,050 | null |
null | null | null | null | null | null | 83 | 1,700 | null |
null | null | null | null | null | null | 96-97 | 1,610 | null |
null | null | null | null | null | null | 74-79 | 1,660-1,670 | null |
null | null | null | null | null | null | 337-345 | 6,570-6,830 | null |
null | null | null | null | null | null | 339-351 | 6,090-6,520 | null |
null | null | null | null | null | null | 316-322 | 6,750-6,870 | null |
PolyStrata speed measurements
Tokens per second of PolyStrata, an open-source engine that runs mixture-of-experts models on a GPU too small for them by keeping the experts the model uses most on the card and running the rest on the CPU. The numbers are the ones in the project's README at commit b0e3b57a, plus the results people reported on their own cards. How each number is measured is in docs/BACKENDS.md; the benchmark is python polystrata.py benchmark --model <name> --prompt-tokens 24000, and anyone can rerun it.
Files:
readme_speeds.csv: the README's tables. "Writes" is the speed while an answer is written in a short chat; "reads" is the speed at which a 10,000-token prompt is read.community_reports.csv: what people measured on their own machines, with the source of each report.
The README's numbers
| Model | Size | Machine | Writes, tokens/s | Reads, tokens/s |
|---|---|---|---|---|
| Qwen3.8-Flash-Next | Q2_0 | NVIDIA: RTX 5070 (12 GB), Ryzen 5 7600, 64 GB RAM | 94 | 2,650 |
| Qwen3.8-Flash-Next | IQ2_XS | NVIDIA: RTX 5070 (12 GB), Ryzen 5 7600, 64 GB RAM | 79 | 2,090 |
| Qwen3.8-Flash-Next | IQ3_XXS | NVIDIA: RTX 5070 (12 GB), Ryzen 5 7600, 64 GB RAM | 62 | 1,750 |
| Qwen3.8-Flash-Next | IQ3_S | NVIDIA: RTX 5070 (12 GB), Ryzen 5 7600, 64 GB RAM | 53 | 1,620 |
| Qwen3.8-Flash-Next | Coder | NVIDIA: RTX 5070 (12 GB), Ryzen 5 7600, 64 GB RAM | 55 | 2,180 |
| Qwen3.8-Flash-Next | Q2_0 | AMD: RX 9070 XT (16 GB), Ryzen 9 3900X, 47 GB RAM | 60 | 1,160 |
| Qwen3.8-Flash-Next | IQ2_XS | AMD: RX 9070 XT (16 GB), Ryzen 9 3900X, 47 GB RAM | 52 | 1,110 |
| Qwen3.8-Flash-Next | Coder | AMD: RX 9070 XT (16 GB), Ryzen 9 3900X, 47 GB RAM | 44 | 1,420 |
| GLM 5.3 Flash | 3.0BIT | NVIDIA: RTX 5090 (32 GB), Xeon Platinum 8470Q (25 vCPU), 120 GB RAM | 59-66 | 820 |
| GLM 5.3 Flash | UD-IQ2_XXS | NVIDIA: RTX 5090 (32 GB), Xeon Platinum 8470Q (25 vCPU), 120 GB RAM | 50-52 | 1,060-1,080 |
| GLM 5.3 Flash | REAP50-Q3_K_M | NVIDIA: RTX 5090 (32 GB), Xeon Platinum 8470Q (25 vCPU), 120 GB RAM | 59-66 | 1,300-1,310 |
| Qwen3.6-35B-A3B | UD-Q3_K_M | NVIDIA: RTX 4070 Laptop (8 GB), Core i7-14700HX, 32 GB RAM | 94-100 | 1,690 |
| Qwen3.6-35B-A3B | UD-Q2_K_XL | NVIDIA: RTX 4070 Laptop (8 GB), Core i7-14700HX, 32 GB RAM | 123-124 | 1,590 |
| Qwen3.6-35B-A3B | UD-Q4_K_M | NVIDIA: RTX 4070 Laptop (8 GB), Core i7-14700HX, 32 GB RAM | 82-86 | 1,560-1,570 |
| Qwen3.6-35B-A3B | UD-Q3_K_M | NVIDIA: RTX 5090 (32 GB), Xeon Platinum 8470Q (25 vCPU), 120 GB RAM | 394-395 | 6,350-6,550 |
| Qwen3.6-35B-A3B | UD-Q2_K_XL | NVIDIA: RTX 5090 (32 GB), Xeon Platinum 8470Q (25 vCPU), 120 GB RAM | 450 | 6,400-6,590 |
| Qwen3.6-35B-A3B | UD-Q4_K_M | NVIDIA: RTX 5090 (32 GB), Xeon Platinum 8470Q (25 vCPU), 120 GB RAM | 363-401 | 6,030-6,690 |
| Ornith 1.5 | APEX-I-COMPACT | NVIDIA: RTX 4070 Laptop (8 GB), Core i7-14700HX, 32 GB RAM | 76-82 | 1,620 |
| Ornith 1.5 | APEX-I-MINI | NVIDIA: RTX 4070 Laptop (8 GB), Core i7-14700HX, 32 GB RAM | 80-86 | 1,550-1,560 |
| Ornith 1.5 | APEX-I-QUALITY | NVIDIA: RTX 4070 Laptop (8 GB), Core i7-14700HX, 32 GB RAM | 59-63 | 1,570-1,580 |
| Ornith 1.5 | APEX-I-COMPACT | NVIDIA: RTX 5090 (32 GB), Xeon Platinum 8470Q (25 vCPU), 120 GB RAM | 316-319 | 6,830-6,860 |
| Ornith 1.5 | APEX-I-MINI | NVIDIA: RTX 5090 (32 GB), Xeon Platinum 8470Q (25 vCPU), 120 GB RAM | 289-300 | 6,300-6,380 |
| Ornith 1.5 | APEX-I-QUALITY | NVIDIA: RTX 5090 (32 GB), Xeon Platinum 8470Q (25 vCPU), 120 GB RAM | 289-298 | 6,040-6,390 |
| Gemma 4 26B-A4B | UD-Q3_K_M | NVIDIA: RTX 4070 Laptop (8 GB), Core i7-14700HX, 32 GB RAM | 92-93 | 2,240 |
| Gemma 4 26B-A4B | UD-Q2_K_XL | NVIDIA: RTX 4070 Laptop (8 GB), Core i7-14700HX, 32 GB RAM | 101-104 | 2,150-2,160 |
| Gemma 4 26B-A4B | UD-Q4_K_M | NVIDIA: RTX 4070 Laptop (8 GB), Core i7-14700HX, 32 GB RAM | 85 | 2,150 |
| Gemma 4 26B-A4B | UD-Q3_K_M | NVIDIA: RTX 5090 (32 GB), Xeon Platinum 8470Q (25 vCPU), 120 GB RAM | 353-356 | 7,880-8,230 |
| Gemma 4 26B-A4B | UD-Q2_K_XL | NVIDIA: RTX 5090 (32 GB), Xeon Platinum 8470Q (25 vCPU), 120 GB RAM | 327-340 | 7,380-8,000 |
| Gemma 4 26B-A4B | UD-Q4_K_M | NVIDIA: RTX 5090 (32 GB), Xeon Platinum 8470Q (25 vCPU), 120 GB RAM | 335-346 | 7,040-8,050 |
| Tiel Coder 35B-A3B | UD-Q3_K_XL | NVIDIA: RTX 4070 Laptop (8 GB), Core i7-14700HX, 32 GB RAM | 83 | 1,700 |
| Tiel Coder 35B-A3B | UD-IQ3_XXS | NVIDIA: RTX 4070 Laptop (8 GB), Core i7-14700HX, 32 GB RAM | 96-97 | 1,610 |
| Tiel Coder 35B-A3B | UD-Q4_K_XL | NVIDIA: RTX 4070 Laptop (8 GB), Core i7-14700HX, 32 GB RAM | 74-79 | 1,660-1,670 |
| Tiel Coder 35B-A3B | UD-Q3_K_XL | NVIDIA: RTX 5090 (32 GB), Xeon Platinum 8470Q (25 vCPU), 120 GB RAM | 337-345 | 6,570-6,830 |
| Tiel Coder 35B-A3B | UD-IQ3_XXS | NVIDIA: RTX 5090 (32 GB), Xeon Platinum 8470Q (25 vCPU), 120 GB RAM | 339-351 | 6,090-6,520 |
| Tiel Coder 35B-A3B | UD-Q4_K_XL | NVIDIA: RTX 5090 (32 GB), Xeon Platinum 8470Q (25 vCPU), 120 GB RAM | 316-322 | 6,750-6,870 |
Community reports
See community_reports.csv, and the community page for how to add yours.
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