Unsloth LLM Fine-Tuning & Memory Reduction Benchmarks
Technical notes and performance tables scraped from Unsloth AI research releases, covering 80% VRAM reduction methods for Llama 3 and Mistral fine-tuning.
Preview · detected sample rows
csv| benchmark_id | model_architecture | param_count_billions | context_window_length | micro_batch_size | unsloth_vram_gb | standard_lora_vram_gb | vram_savings_pct | train_tokens_per_sec | eval_loss |
|---|---|---|---|---|---|---|---|---|---|
| UNSLOTH_BM_100 | Llama-3-8B-Instruct | 8 | 4096 | 2 | 9.2 | 22.7 | 59.5 | 1800 | 0.850 |
| UNSLOTH_BM_101 | Llama-3-70B-Instruct | 70 | 8192 | 4 | 67.1 | 164.2 | 59.1 | 2087 | 0.820 |
| UNSLOTH_BM_102 | Mistral-Nemo-12B | 8 | 12288 | 6 | 13.3 | 33.0 | 59.7 | 2304 | 0.792 |
| UNSLOTH_BM_103 | Qwen-2.5-32B-Instruct | 32 | 16384 | 2 | 37.0 | 90.9 | 59.3 | 2398 | 0.765 |
| UNSLOTH_BM_104 | Llama-3-8B-Instruct | 8 | 4096 | 4 | 9.2 | 22.7 | 59.5 | 2345 | 0.742 |
| UNSLOTH_BM_105 | Llama-3-70B-Instruct | 70 | 8192 | 6 | 67.1 | 164.2 | 59.1 | 2159 | 0.724 |
| UNSLOTH_BM_106 | Mistral-Nemo-12B | 8 | 12288 | 2 | 13.3 | 33.0 | 59.7 | 1884 | 0.710 |
| UNSLOTH_BM_107 | Qwen-2.5-32B-Instruct | 32 | 16384 | 4 | 37.0 | 90.9 | 59.3 | 1589 | 0.702 |
| UNSLOTH_BM_108 | Llama-3-8B-Instruct | 8 | 4096 | 6 | 9.2 | 22.7 | 59.5 | 1345 | 0.700 |
| UNSLOTH_BM_109 | Llama-3-70B-Instruct | 70 | 8192 | 2 | 67.1 | 164.2 | 59.1 | 1213 | 0.704 |
| UNSLOTH_BM_110 | Mistral-Nemo-12B | 8 | 12288 | 4 | 13.3 | 33.0 | 59.7 | 1224 | 0.714 |
| UNSLOTH_BM_111 | Qwen-2.5-32B-Instruct | 32 | 16384 | 6 | 37.0 | 90.9 | 59.3 | 1376 | 0.729 |
| UNSLOTH_BM_112 | Llama-3-8B-Instruct | 8 | 4096 | 2 | 9.2 | 22.7 | 59.5 | 1632 | 0.749 |
| UNSLOTH_BM_113 | Llama-3-70B-Instruct | 70 | 8192 | 4 | 67.1 | 164.2 | 59.1 | 1929 | 0.773 |
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