Llama 3.1 Tulu 3 70B DPO — Hardware Requirements & GPU Compatibility
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Llama-3.1-Tulu-3-70B-DPO is Allen Institute for AI's (Ai2's) 70.6-billion-parameter instruction-following model, fine-tuned from Meta's Llama 3.1 70B base through Ai2's fully open Tulu 3 post-training recipe. This checkpoint is the direct preference optimization (DPO) stage, trained on Ai2's published preference mixture on top of the Tulu-3-70B-SFT model; a further RLVR stage produces the final Tulu-3-70B release. Tulu 3 targets strong performance not just on chat but on tasks like MATH, GSM8K, and IFEval, with all training data, code, and recipes released openly as a reference post-training pipeline. At 70.6 billion parameters, it needs a multi-GPU workstation or server to run, even once quantized. Context length is 131,072 tokens, inherited from the Llama 3.1 base. It is released under Meta's Llama 3.1 Community License, a custom license that is free for most commercial and research use but requires organizations with more than 700 million monthly active users to request separate permission from Meta. It was published in November 2024.
447 downloads
10 likesSpecifications
- Publisher
- Allen AI
- Family
- Llama 3
- Parameters
- 70.6B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,264
- Release Date
- 2024-11-20
- License
- Llama 3.1 Community
Get Started
HuggingFace
How Much VRAM Does Llama 3.1 Tulu 3 70B DPO Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 31.0 GB | 73.2 GB | 29.99 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 31.8 GB | 74.1 GB | 30.87 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 35.4 GB | 77.6 GB | 34.39 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 36.3 GB | 78.5 GB | 35.28 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 43.3 GB | 85.6 GB | 42.33 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 51.2 GB | 93.5 GB | 50.27 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 59.2 GB | 101.5 GB | 58.21 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 71.5 GB | 113.8 GB | 70.55 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Llama 3.1 Tulu 3 70B DPO?
Q4_K_M · 43.3 GBLlama 3.1 Tulu 3 70B DPO (Q4_K_M) requires 43.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 57+ GB is recommended. Using the full 131K context window can add up to 42.3 GB, bringing total usage to 85.6 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Llama 3.1 Tulu 3 70B DPO?
Q4_K_M · 43.3 GB27 devices with unified memory can run Llama 3.1 Tulu 3 70B DPO, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Llama 3.1 Tulu 3 70B DPO
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Llama 3.1 Tulu 3 70B DPO need?
Llama 3.1 Tulu 3 70B DPO requires 43.3 GB of VRAM at Q4_K_M, or 142.1 GB at BF16. Full 131K context adds up to 42.3 GB (85.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 70.6B × 4.8 bits ÷ 8 = 42.3 GB
KV Cache + Overhead ≈ 1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 43.3 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M43.3 GBQ4_K_M + full context85.6 GB- Can NVIDIA GeForce RTX 4090 run Llama 3.1 Tulu 3 70B DPO?
Yes, at IQ2_S (23.0 GB) or lower. Higher quantizations like IQ2_M (24.8 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Llama 3.1 Tulu 3 70B DPO?
For Llama 3.1 Tulu 3 70B DPO, Q4_K_M (43.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (49.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 20.4 GB.
VRAM requirement by quantization
IQ2_XXS20.4 GBIQ3_XS30.1 GBQ3_K_M35.4 GBQ4_K_M ★43.3 GBQ5_K_S49.5 GBBF16142.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 3.1 Tulu 3 70B DPO on a Mac?
Llama 3.1 Tulu 3 70B DPO requires at least 20.4 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Llama 3.1 Tulu 3 70B DPO locally?
Yes — Llama 3.1 Tulu 3 70B DPO can run locally on consumer hardware. At Q4_K_M quantization it needs 43.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3.1 Tulu 3 70B DPO?
At Q4_K_M, Llama 3.1 Tulu 3 70B DPO can reach ~111 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 43.3 × 0.65 = ~120 tok/s
Estimated speed at Q4_K_M (43.3 GB)
~120 tok/s~120 tok/s~111 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama 3.1 Tulu 3 70B DPO?
At Q4_K_M, the download is about 42.33 GB. The full-precision BF16 version is 141.11 GB. The smallest option (IQ2_XXS) is 19.40 GB.
- Which GPUs can run Llama 3.1 Tulu 3 70B DPO?
No single consumer GPU has enough VRAM to run Llama 3.1 Tulu 3 70B DPO at Q4_K_M (43.3 GB). Multi-GPU or professional hardware is required.
- Which devices can run Llama 3.1 Tulu 3 70B DPO?
27 devices with unified memory can run Llama 3.1 Tulu 3 70B DPO at Q4_K_M (43.3 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.