Unsloth vs Axolotl vs TRL vs LLaMA-Factory: A Fine-Tuning Framework Comparison on Speed, VRAM, and Multi-GPU

Four open source projects dominate LLM fine-tuning today. Unsloth, Axolotl, TRL, and LLaMA-Factory all wrap the same underlying PyTorch and Hugging Face stack. They diverge on where they spend engineering effort. Unsloth rewrites kernels. Axolotl composes parallelism strategies. TRL defines the trainer APIs the others build on. LLaMA-Factory optimizes for breadth of model coverage and zero-code operation. This comparison covers three axes engineers actually hit: training throughput, peak VRAM, and multi-GPU scaling. Understand each framework TRL is the reference implementation layer. It ships SFTTrainer, DPOTrainer, GRPOTrainer, KTOTrainer, RewardTrainer, and RLOOTrainer. Axolotl and LLaMA-Factory both call into it. The current stable release line is v1.8.0. Unsloth replaces parts of the modeling code with hand-written Triton kernels. Backpropagation steps are manually derived rather than autograd-generated. Hugging Face’s own writeup notes accuracy degradation is 0% versus standard QLoRA, because no approximations are introduced. Axolotl is a YAML-driven wrapper over Transformers, PEFT, TRL, Accelerate, and DeepSpeed. Its differentiator is composability of parallelism strategies, not kernel work. LLaMA-Factory is an ACL 2024 system demonstration paper with a Gradio web UI called LlamaBoard. The repository covers 100+ LLMs and VLMs. (function(){ var f=document.getElementById('mtp-ft-frame'); if(!f) return; window.addEventListener('message',function(e){ if(!e.data||typeof e.data!=='object') return; var h=e.data.mtpFtHeight; if(typeof h==='number'&&h>200&&h Speed Unsloth: kernel-level gains on a single GPU Unsloth's published benchmarks show 2x training speed for Llama 3.1 8B and Llama 3.3 70B. The setup used the Alpaca dataset, batch size 2, and gradient accumulation 4. QLoRA ran at rank 32 on all linear layers. The MoE results are larger. Unsloth fine-tuned unsloth/gpt-oss-20b-BF16 on an NVIDIA B200. It reports 712.33 ms per step at 8K context, versus 5,226.86 ms for Transformers v5. That is a 7.3x gap. At 4K the gap is 4.82x, and at 1K it is only 1.37x. The trend direction is model-dependent, and Unsloth's docs scope this claim to gpt-oss. There, the speedup grows with sequence length, credited to Flex Attention and the MoE kernels. Qwen3-30B-A3B on B200 runs the other way. Its reported speedup falls from 1.7x at 1K to 1.1x at 16K. Memory savings move the opposite direction, rising from about 2% to 15%. Qwen3-30B-A3B on H100 reaches up to 1.77x. GLM-4.7-Flash on RTX PRO 6000 reaches 2.1x. A collaboration with AMD measured Llama-3.1-8B LoRA SFT at 2.07 s/step. TRL plus FlashAttention-2 took 2.87 s/step, a 1.39x gap with matching loss curves. Axolotl: kernels borrowed, parallelism native Axolotl added custom Triton kernels and autograd functions for LoRA in February 2025, explicitly citing Unsloth as inspiration. They are opt-in through lora_mlp_kernel, lora_qkv_kernel, and lora_o_kernel. Recent release notes add SonicMoE LoRA support. It delivers up to 1.45x speedup and 30% memory reduction over a grouped_mm baseline. That figure is for Qwen3.5-35B-A3B 8-bit LoRA on a single H100 SXM. Axolotl also ships FlashAttention 2/3/4, xFormers, Flex Attention, SageAttention, Liger Kernel, Cut Cross Entropy, and ScatterMoE. TRL: the baseline everyone measures against TRL is usually the reference point rather than the winner on raw single-GPU throughput. It compensates with breadth of memory and speed levers documented in Reducing Memory Usage and Speeding Up Training. Those levers include packing, padding-free batching, truncation, Liger Kernel, and vLLM sleep mode for GRPO. TRL also has a first-party Unsloth integration, so the two are not mutually exclusive. LLaMA-Factory: speed by delegation LLaMA-Factory does not write its own kernels. It exposes other people's work through config flags. Setting use_unsloth: true activates