diff --git a/README.md b/README.md
index 7c84c5ef94..dc2136f6fa 100644
--- a/README.md
+++ b/README.md
@@ -82,23 +82,17 @@ All notebooks are **beginner friendly**! Add your dataset, click "Run All", and
## 🥇 Performance Benchmarking
-- For the full list of **reproducible** benchmarking tables, [go to our website](https://unsloth.ai/blog/mistral-benchmark#Benchmark%20tables)
+- For our most detailed benchmarks, read our [Llama 3.3 Blog](https://unsloth.ai/blog/llama3-3).
+- Benchmarking of Unsloth was also conducted by [🤗Hugging Face](https://huggingface.co/blog/unsloth-trl).
-| 1 A100 40GB | 🤗Hugging Face | Flash Attention | 🦥Unsloth Open Source | 🦥[Unsloth Pro](https://unsloth.ai/pricing) |
-|--------------|--------------|-----------------|---------------------|-----------------|
-| Alpaca | 1x | 1.04x | 1.98x | **15.64x** |
-| LAION Chip2 | 1x | 0.92x | 1.61x | **20.73x** |
-| OASST | 1x | 1.19x | 2.17x | **14.83x** |
-| Slim Orca | 1x | 1.18x | 2.22x | **14.82x** |
+We tested using the Alpaca Dataset, a batch size of 2, gradient accumulation steps of 4, rank = 32, and applied QLoRA on all linear layers (q, k, v, o, gate, up, down):
+
+| Model | VRAM | 🦥 Unsloth speed | 🦥 VRAM reduction | 🦥 Longer context | 😊 Hugging Face + FA2 |
+|----------------|-------|-----------------|----------------|----------------|--------------------|
+| Llama 3.3 (70B)| 80GB | 2x | >75% | 13x longer | 1x |
+| Llama 3.1 (8B) | 80GB | 2x | >70% | 12x longer | 1x |
-- Benchmarking table below was conducted by [🤗Hugging Face](https://huggingface.co/blog/unsloth-trl).
-
-| Free Colab T4 | Dataset | 🤗Hugging Face | Pytorch 2.1.1 | 🦥Unsloth | 🦥 VRAM reduction |
-| --- | --- | --- | --- | --- | --- |
-| Llama-2 7b | OASST | 1x | 1.19x | 1.95x | -43.3% |
-| Mistral 7b | Alpaca | 1x | 1.07x | 1.56x | -13.7% |
-| Tiny Llama 1.1b | Alpaca | 1x | 2.06x | 3.87x | -73.8% |
-| DPO with Zephyr | Ultra Chat | 1x | 1.09x | 1.55x | -18.6% |
+
@@ -359,119 +353,28 @@ dpo_trainer.train()
```
## 🥇 Detailed Benchmarking Tables
-- Click "Code" for fully reproducible examples
-- "Unsloth Equal" is a preview of our PRO version, with code stripped out. All settings and the loss curve remains identical.
-- For the full list of benchmarking tables, [go to our website](https://unsloth.ai/blog/mistral-benchmark#Benchmark%20tables)
-
-| 1 A100 40GB | 🤗Hugging Face | Flash Attention 2 | 🦥Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
-|--------------|-------------|-------------|-----------------|--------------|---------------|-------------|
-| Alpaca | 1x | 1.04x | 1.98x | 2.48x | 5.32x | **15.64x** |
-| code | [Code](https://colab.research.google.com/drive/1u4dBeM-0vGNVmmO6X7cScAut-Hyt4KDF?usp=sharing) | [Code](https://colab.research.google.com/drive/1fgTOxpMbVjloQBvZyz4lF4BacKSZOB2A?usp=sharing) | [Code](https://colab.research.google.com/drive/1YIPY_18xm-K0iJDgvNkRoJsgkPMPAO3G?usp=sharing) | [Code](https://colab.research.google.com/drive/1ANW8EFL3LVyTD7Gq4TkheC1Z7Rxw-rHp?usp=sharing) | | |
-| seconds| 1040 | 1001 | 525 | 419 | 196 | 67 |
-| memory MB| 18235 | 15365 | 9631 | 8525 | | |
-| % saved| | 15.74 | 47.18 | 53.25 | | | |
+### Context length benchmarks
+#### Llama 3.1 (8B) max. context length
+We tested Llama 3.1 (8B) Instruct and did 4bit QLoRA on all linear layers (Q, K, V, O, gate, up and down) with rank = 32 with a batch size of 1. We padded all sequences to a certain maximum sequence length to mimic long context finetuning workloads.
+| GPU VRAM | 🦥Unsloth context length | Hugging Face + FA2 |
+|----------|-----------------------|-----------------|
+| 8 GB | 2,972 | OOM |
+| 12 GB | 21,848 | 932 |
+| 16 GB | 40,724 | 2,551 |
+| 24 GB | 78,475 | 5,789 |
+| 40 GB | 153,977 | 12,264 |
+| 48 GB | 191,728 | 15,502 |
+| 80 GB | 342,733 | 28,454 |
-### Llama-Factory 3rd party benchmarking
-- [Link to performance table.](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-Comparison) TGS: tokens per GPU per second. Model: LLaMA2-7B. GPU: NVIDIA A100 * 1. Batch size: 4. Gradient accumulation: 2. LoRA rank: 8. Max length: 1024.
+#### Llama 3.3 (70B) max. context length
+We tested Llama 3.3 (70B) Instruct on a 80GB A100 and did 4bit QLoRA on all linear layers (Q, K, V, O, gate, up and down) with rank = 32 with a batch size of 1. We padded all sequences to a certain maximum sequence length to mimic long context finetuning workloads.
-| Method | Bits | TGS | GRAM | Speed |
-| --- | --- | --- | --- | --- |
-| HF | 16 | 2392 | 18GB | 100% |
-| HF+FA2 | 16 | 2954 | 17GB | 123% |
-| Unsloth+FA2 | 16 | 4007 | 16GB | **168%** |
-| HF | 4 | 2415 | 9GB | 101% |
-| Unsloth+FA2 | 4 | 3726 | 7GB | **160%** |
+| GPU VRAM | 🦥Unsloth context length | Hugging Face + FA2 |
+|----------|------------------------|------------------|
+| 48 GB | 12,106 | OOM |
+| 80 GB | 89,389 | 6,916 |
-### Performance comparisons between popular models
-
- Click for specific model benchmarking tables (Mistral 7b, CodeLlama 34b etc.)
-
-### Mistral 7b
-| 1 A100 40GB | Hugging Face | Flash Attention 2 | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
-|--------------|-------------|-------------|-----------------|--------------|---------------|-------------|
-| Mistral 7B Slim Orca | 1x | 1.15x | 2.15x | 2.53x | 4.61x | **13.69x** |
-| code | [Code](https://colab.research.google.com/drive/1mePk3KzwTD81hr5mcNcs_AX3Kbg_Ha0x?usp=sharing) | [Code](https://colab.research.google.com/drive/1dgHxjvTmX6hb0bPcLp26RXSE6_n9DKj7?usp=sharing) | [Code](https://colab.research.google.com/drive/1SKrKGV-BZoU4kv5q3g0jtE_OhRgPtrrQ?usp=sharing) | [Code](https://colab.research.google.com/drive/18yOiyX0T81mTwZqOALFSCX_tSAqju6aD?usp=sharing) | |
-| seconds | 1813 | 1571 | 842 | 718 | 393 | 132 |
-| memory MB | 32853 | 19385 | 12465 | 10271 | | |
-| % saved| | 40.99 | 62.06 | 68.74 | | |
-
-### CodeLlama 34b
-| 1 A100 40GB | Hugging Face | Flash Attention 2 | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
-|--------------|-------------|-------------|-----------------|--------------|---------------|-------------|
-| Code Llama 34B | OOM ❌ | 0.99x | 1.87x | 2.61x | 4.27x | 12.82x |
-| code | [▶️ Code](https://colab.research.google.com/drive/1ykfz3BqrtC_AUFegCzUQjjfUNlxp6Otc?usp=sharing) | [Code](https://colab.research.google.com/drive/12ZypxQh7OC6kBXvWZI-5d05I4m-B_hoR?usp=sharing) | [Code](https://colab.research.google.com/drive/1gdHyAx8XJsz2yNV-DHvbHjR1iCef5Qmh?usp=sharing) | [Code](https://colab.research.google.com/drive/1fm7wqx9MJ0kRrwKOfmLkK1Rmw-pySahB?usp=sharing) | |
-| seconds | 1953 | 1982 | 1043 | 748 | 458 | 152 |
-| memory MB | 40000 | 33217 | 27413 | 22161 | | |
-| % saved| | 16.96| 31.47 | 44.60 | | | |
-
-### 1 Tesla T4
-
-| 1 T4 16GB | Hugging Face | Flash Attention | Unsloth Open | Unsloth Pro Equal | Unsloth Pro | Unsloth Max |
-|--------------|-------------|-----------------|-----------------|---------------|---------------|-------------|
-| Alpaca | 1x | 1.09x | 1.69x | 1.79x | 2.93x | **8.3x** |
-| code | [▶️ Code](https://colab.research.google.com/drive/1XpLIV4s8Bj5uryB-X2gqM88oRGHEGdaB?usp=sharing) | [Code](https://colab.research.google.com/drive/1LyXu6CjuymQg6ddHX8g1dpUvrMa1nn4L?usp=sharing) | [Code](https://colab.research.google.com/drive/1gsv4LpY7C32otl1rgRo5wXTk4HIitXoM?usp=sharing) | [Code](https://colab.research.google.com/drive/1VtULwRQwhEnVdNryjm27zXfdSM1tNfFK?usp=sharing) | | |
-| seconds | 1599 | 1468 | 942 | 894 | 545 | 193 |
-| memory MB | 7199 | 7059 | 6459 | 5443 | | |
-| % saved | | 1.94 | 10.28 | 24.39 | | |
-
-### 2 Tesla T4s via DDP
-
- | 2 T4 DDP | Hugging Face | Flash Attention | Unsloth Open | Unsloth Equal | Unsloth Pro | Unsloth Max |
-|--------------|----------|-------------|-----------------|--------------|---------------|-------------|
-| Alpaca | 1x | 0.99x | 4.95x | 4.44x | 7.28x | **20.61x** |
-| code | [▶️ Code](https://www.kaggle.com/danielhanchen/hf-original-alpaca-t4-ddp) | [Code](https://www.kaggle.com/danielhanchen/hf-sdpa-alpaca-t4-ddp) | [Code](https://www.kaggle.com/danielhanchen/unsloth-alpaca-t4-ddp) | | |
-| seconds | 9882 | 9946 | 1996 | 2227 | 1357 | 480 |
-| memory MB| 9176 | 9128 | 6904 | 6782 | | |
-| % saved | | 0.52 | 24.76 | 26.09 | | | |
-
-
-### Performance comparisons on 1 Tesla T4 GPU:
-
- Click for Time taken for 1 epoch
-
-One Tesla T4 on Google Colab
-`bsz = 2, ga = 4, max_grad_norm = 0.3, num_train_epochs = 1, seed = 3047, lr = 2e-4, wd = 0.01, optim = "adamw_8bit", schedule = "linear", schedule_steps = 10`
-
-| System | GPU | Alpaca (52K) | LAION OIG (210K) | Open Assistant (10K) | SlimOrca (518K) |
-| --- | --- | --- | --- | --- | --- |
-| Huggingface | 1 T4 | 23h 15m | 56h 28m | 8h 38m | 391h 41m |
-| Unsloth Open | 1 T4 | 13h 7m (1.8x) | 31h 47m (1.8x) | 4h 27m (1.9x) | 240h 4m (1.6x) |
-| Unsloth Pro | 1 T4 | 3h 6m (7.5x) | 5h 17m (10.7x) | 1h 7m (7.7x) | 59h 53m (6.5x) |
-| Unsloth Max | 1 T4 | 2h 39m (8.8x) | 4h 31m (12.5x) | 0h 58m (8.9x) | 51h 30m (7.6x) |
-
-**Peak Memory Usage**
-
-| System | GPU | Alpaca (52K) | LAION OIG (210K) | Open Assistant (10K) | SlimOrca (518K) |
-| --- | --- | --- | --- | --- | --- |
-| Huggingface | 1 T4 | 7.3GB | 5.9GB | 14.0GB | 13.3GB |
-| Unsloth Open | 1 T4 | 6.8GB | 5.7GB | 7.8GB | 7.7GB |
-| Unsloth Pro | 1 T4 | 6.4GB | 6.4GB | 6.4GB | 6.4GB |
-| Unsloth Max | 1 T4 | 11.4GB | 12.4GB | 11.9GB | 14.4GB |
-
-
-
- Click for Performance Comparisons on 2 Tesla T4 GPUs via DDP:
-**Time taken for 1 epoch**
-
-Two Tesla T4s on Kaggle
-`bsz = 2, ga = 4, max_grad_norm = 0.3, num_train_epochs = 1, seed = 3047, lr = 2e-4, wd = 0.01, optim = "adamw_8bit", schedule = "linear", schedule_steps = 10`
-
-| System | GPU | Alpaca (52K) | LAION OIG (210K) | Open Assistant (10K) | SlimOrca (518K) * |
-| --- | --- | --- | --- | --- | --- |
-| Huggingface | 2 T4 | 84h 47m | 163h 48m | 30h 51m | 1301h 24m * |
-| Unsloth Pro | 2 T4 | 3h 20m (25.4x) | 5h 43m (28.7x) | 1h 12m (25.7x) | 71h 40m (18.1x) * |
-| Unsloth Max | 2 T4 | 3h 4m (27.6x) | 5h 14m (31.3x) | 1h 6m (28.1x) | 54h 20m (23.9x) * |
-
-**Peak Memory Usage on a Multi GPU System (2 GPUs)**
-
-| System | GPU | Alpaca (52K) | LAION OIG (210K) | Open Assistant (10K) | SlimOrca (518K) * |
-| --- | --- | --- | --- | --- | --- |
-| Huggingface | 2 T4 | 8.4GB \| 6GB | 7.2GB \| 5.3GB | 14.3GB \| 6.6GB | 10.9GB \| 5.9GB * |
-| Unsloth Pro | 2 T4 | 7.7GB \| 4.9GB | 7.5GB \| 4.9GB | 8.5GB \| 4.9GB | 6.2GB \| 4.7GB * |
-| Unsloth Max | 2 T4 | 10.5GB \| 5GB | 10.6GB \| 5GB | 10.6GB \| 5GB | 10.5GB \| 5GB * |
-
-* Slim Orca `bsz=1` for all benchmarks since `bsz=2` OOMs. We can handle `bsz=2`, but we benchmark it with `bsz=1` for consistency.
-
+
