Unsloth Benchmarks: Faster Speeds and Lower VRAM Usage for QLoRA

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Unsloth's documentation shares benchmark metrics for LLM fine-tuning across NVIDIA GPUs. Compared to Hugging Face paired with FlashAttention-2, Unsloth achieves a 2x speedup, over 70% VRAM reduction, and significantly longer context windows on models such as Llama 3.1 and Llama 3.3.

Key points

  1. Evaluations on NVIDIA H100 and Blackwell GPUs demonstrate a 2x speed increase and 70% to 75% VRAM savings for Llama 3.1 (8B) and Llama 3.3 (70B).
  2. Context lengths improve by 12x to 13x thanks to Unsloth's custom gradient checkpointing and Apple's CCE algorithm.
  3. For Llama 3.1 (8B) on an 80 GB GPU, Unsloth supports sequence lengths up to 342,733 tokens, compared to 28,454 tokens with Hugging Face and FlashAttention-2.
  4. On smaller setups like 8 GB VRAM, Unsloth processes 2,972 context tokens with Llama 3.1 (8B), whereas the baseline encounters an Out-of-Memory (OOM) error.
  5. torch.compile requires roughly five minutes or longer to warm up, meaning throughput should be evaluated after initial compilation completes.

Why it matters

Hardware memory limitations often restrict the fine-tuning of large language models. By cutting VRAM requirements and substantially extending sequence length limits, Unsloth makes high-context training accessible on lower-spec infrastructure and reduces GPU operational expenses.

Analysis

Unsloth achieves its performance gains by integrating bespoke gradient checkpointing with Apple's CCE algorithm, addressing memory scaling bottlenecks during QLoRA training. The context length scaling is especially notable: accommodating over 300k tokens on an 80 GB card changes the feasibility of long-context adaptation without multi-node clusters. However, engineering workflows must account for the initial torch.compile warm-up overhead during short training runs.

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Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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