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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallYuri Pocepaev reports cutting GLiClass’s batch-1 median latency from 59.23 ms with an initial native FP8 adapter to 16.10 ms after adding Triton fusion and CUDA Graphs on an RTX 4050 Laptop GPU. The improvement came from reducing auxiliary GPU work and dispatch overhead—not from changing the FP8 weights alone. In the same measurements, BF16 with CUDA Graphs took 23.79 ms, so the optimized FP8 path was 1.48× faster on this particular request. These are the author’s results from one setup, not an independently reproduced benchmark or a general guarantee for FP8.
What the 59 ms and 16 ms figures compare
The 3.68× reduction is a comparison between two versions of Pocepaev’s FP8 adapter: the initial native implementation and the later Triton-plus-CUDA-Graphs implementation. It is not a controlled comparison between the original model and FP8. For a BF16 control using the same graph wrapper, the report gives a 23.79 ms median; the optimized FP8 version’s 16.10 ms median is 1.48× faster than that control.
| Runtime | Median latency | p95 latency | Peak allocated tensor memory |
|---|---|---|---|
| BF16, eager | 24.18 ms | 29.67 ms | 3.219 GiB |
| BF16 + CUDA Graphs | 23.79 ms | 24.45 ms | 3.238 GiB |
| Initial native FP8 adapter | 59.23 ms | 66.36 ms | 2.145 GiB |
| FP8 + Triton | 37.97 ms | 46.62 ms | 2.144 GiB |
| FP8 + Triton + CUDA Graphs | 16.10 ms | 16.44 ms | 2.166 GiB |
All figures in the table are reported by Pocepaev in 2026. They are medians and p95 values from his test, not confidence intervals or results aggregated across machines. The request used one fixed AG News example, four candidate labels, and batch size 1. After warmup and quality evaluation, he timed 50 more requests, synchronizing CUDA before and after each timing. Timing included tokenization and postprocessing, but excluded model loading, Triton compilation, and graph preparation. Laptop clocks and thermals were not locked.
Why the first FP8 version was slower
The derivative checkpoint uses FP8 E4M3 weights with per-output-channel scales and dynamically quantized activations for 168 matrices across 24 mT5 encoder blocks. This is W8A8 for selected projections, not for every tensor: embeddings, normalization layers, and classification components remain BF16. The reported weight files are 2,259,902,516 bytes for the derivative checkpoint and 3,416,522,340 bytes for BF16, a 33.85% reduction.
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That smaller representation did not make the first adapter faster. It used torch._scaled_mm and cuBLAS for the FP8 matrix multiplications, but a profiled request still involved 3,269 GPU kernel executions. Separate operations found activation maxima, calculated scales, converted types, padded rows, and processed outputs. On this request, those additional steps outweighed the benefit of using FP8 GEMMs.
What Triton fusion and CUDA Graphs changed
Triton fused work around the matrix multiplications
Pocepaev moved activation quantization into a Triton kernel and fused output scaling into a second Triton kernel. This reduced the separate auxiliary work around the matrix operations. The reported median fell from 59.23 ms to 37.97 ms, while peak allocated tensor memory stayed close to the initial adapter’s figure.
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CUDA Graphs reduced encoder dispatch overhead
He then captured encoder work in CUDA Graphs. The adapter uses four sequence-length buckets—64, 128, 192, and 256 tokens—pads each input to a bucket, and masks the added positions. One compatibility adjustment was to construct the attention mask outside the captured region. With Triton fusion and graph capture together, the profiled request involved 1,425 GPU kernel executions and retained all 168 native FP8 GEMMs; the median was 16.10 ms.
The staged results show why the optimization was not simply “turn on FP8”: Triton fusion helped, and graph capture produced a further large change on this batch-1 workload. The measured peak allocated tensor memory for the final path was 2.166 GiB, but that is not a complete VRAM or model-loading requirement. The report says the loader temporarily reconstructs weights in BF16 before replacing projections, so peak allocation during the timed run should not be treated as the total memory needed to load the model or as equivalent to an nvidia-smi reading.
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What the quality check showed—and did not show
Pocepaev compared BF16 and optimized FP8 on 664 examples: a seeded subset of 256 AG News test examples plus the full English and Russian SIB-200 test splits, with 204 examples per language. SIB-200 candidate labels were in English for both language splits.
| Evaluation set | BF16 macro-F1 | Optimized FP8 macro-F1 | Difference |
|---|---|---|---|
| AG News | 79.08% | 79.49% | +0.41 percentage points |
| SIB-200 English | 84.57% | 84.04% | −0.53 percentage points |
| SIB-200 Russian | 84.09% | 83.42% | −0.67 percentage points |
Top-1 predictions agreed between BF16 and optimized FP8 on 99.25% of the 664 examples. Agreement is not accuracy, and the small positive AG News difference is not evidence that FP8 improved model quality. The results include declines on both SIB-200 splits; this three-set evaluation does not establish quality preservation across other languages or production tasks. The report also did not retain full score distributions, so it makes no claim about all-logit changes or calibration.
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How to interpret or reproduce the result
This is evidence that one implementation stack can make this GLiClass workload faster on one RTX 4050 Laptop setup. It does not establish expected performance on a different GPU, operating system, batch size, sequence-length distribution, or sustained-throughput workload. Larger batches and sustained throughput were not measured.
For a meaningful comparison with another implementation, match or record the conditions that affect both the model work and the timing:
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- GPU model and laptop power or thermal conditions.
- Model and checkpoint revision, along with software versions.
- Batch size, sequence length, and number of candidate labels.
- Warmup and synchronization method, and whether tokenization and postprocessing are timed.
- Whether model loading, compilation, and graph preparation are included or excluded.
- Quality results on the same evaluation examples, rather than latency alone.
Pocepaev says his model, source code, report, and a Hugging Face repository containing weights, tokenizer, dependencies, adapter, and an inference.py entry point are available under Apache-2.0. Those are claims in his 2026 article; without a verified current artifact URL or revision, check the repository’s present contents and license before relying on them.
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