Llama 3.1 Tulu 3 70B — Hardware Requirements & GPU Compatibility
Chat
Llama-3.1-Tulu-3-70B is Allen Institute for AI's 70.6-billion-parameter instruction-following chat model, built by post-training Meta's Llama-3.1-70B base through a fully open pipeline of supervised fine-tuning, direct preference optimization, and a final reinforcement-learning-with-verifiable-rewards stage, with every dataset, script, and recipe published alongside the weights. It targets strong performance on chat as well as math (MATH, GSM8K) and instruction-following (IFEval) benchmarks, and is one entry in a full Tulu 3 family spanning 8B, 70B, and 405B sizes with released SFT, DPO, RLVR, and reward-model checkpoints at each size. At 70.6 billion parameters, it needs a multi-GPU workstation to run in full precision. Context length is 131,072 tokens, inherited from its Llama 3.1 base. It is released under the Llama 3.1 Community License, which requires organizations above 700 million monthly active users to obtain a separate license from Meta. It was published in November 2024.
618 downloads
61 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 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?
Q4_K_M · 43.3 GBLlama 3.1 Tulu 3 70B (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?
Q4_K_M · 43.3 GB27 devices with unified memory can run Llama 3.1 Tulu 3 70B, 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
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 need?
Llama 3.1 Tulu 3 70B 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?
Yes, at IQ2_XS (22.1 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?
For Llama 3.1 Tulu 3 70B, 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 GBQ2_K31.0 GBQ3_K_L37.1 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 on a Mac?
Llama 3.1 Tulu 3 70B 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 locally?
Yes — Llama 3.1 Tulu 3 70B 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?
At Q4_K_M, Llama 3.1 Tulu 3 70B 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?
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?
No single consumer GPU has enough VRAM to run Llama 3.1 Tulu 3 70B at Q4_K_M (43.3 GB). Multi-GPU or professional hardware is required.
- Which devices can run Llama 3.1 Tulu 3 70B?
27 devices with unified memory can run Llama 3.1 Tulu 3 70B 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.