TCompress Model Benchmark Report
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TCompress Model Benchmark Report
Quantization Type: QAT (Quantization Aware Training)
Precision: BF16
Evaluation Dataset: Salesforce/wikitext (wikitext-2-raw-v1)
Performance Metrics
| Metric | Result |
|---|
| Total Tokens Evaluated | 1,000,000 |
| Latency (Mean) | 58.12 ms |
| Throughput | 17,206.3 tok/s |
| Peak GPU Memory | 1,174.4 MB |
Model Accuracy
| Variant | Agreement vs Base | Flipped Tokens |
|---|
| TCompress (bf16) | 94.92% | 50,829 |
Storage & Packaging
| Asset | Size |
|---|
| Model Weight File | 298.0 MB |
Implementation Details
This model has been optimized via Training-Aware Quantization to maintain high fidelity (94.92% agreement) with the base FP32 architecture while significantly reducing memory footprint and maximizing throughput on CUDA-enabled hardware.
