Llama 3.1 Tulu 3 8B — Hardware Requirements & GPU Compatibility
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Llama-3.1-Tulu-3-8B is Allen Institute for AI's fully open instruction-following model, built on Meta's Llama 3.1 8B base through a public pipeline of supervised fine-tuning, direct preference optimization, and a final reinforcement learning stage with verifiable rewards (RLVR). It is tuned for chat as well as harder tasks like math, GSM8K, and instruction-following (IFEval), with all training data, code, and recipes released openly as part of the Tulu 3 project. Its 8B size lets it run on a single consumer GPU. Context length is 131,072 tokens. It is released under the Llama 3.1 Community License Agreement, which restricts use above 700 million monthly active users and imposes Meta's acceptable-use policy. It was published in November 2024, alongside a 70B and later a 405B sibling trained the same way.
8.1K downloads
178 likesSpecifications
- Publisher
- Allen AI
- Family
- Llama 3
- Parameters
- 8.0B
- 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 8B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.0 GB | 20.9 GB | 3.41 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.1 GB | 21.0 GB | 3.51 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.5 GB | 21.4 GB | 3.91 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.6 GB | 21.5 GB | 4.02 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.4 GB | 22.3 GB | 4.82 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.3 GB | 23.2 GB | 5.72 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.2 GB | 24.1 GB | 6.63 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.6 GB | 25.5 GB | 8.03 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 8B?
Q4_K_M · 5.4 GBLlama 3.1 Tulu 3 8B (Q4_K_M) requires 5.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 131K context window can add up to 16.9 GB, bringing total usage to 22.3 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Llama 3.1 Tulu 3 8B?
Q4_K_M · 5.4 GB58 devices with unified memory can run Llama 3.1 Tulu 3 8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download Llama 3.1 Tulu 3 8B
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 8B need?
Llama 3.1 Tulu 3 8B requires 5.4 GB of VRAM at Q4_K_M, or 16.6 GB at BF16. Full 131K context adds up to 16.9 GB (22.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.0B × 4.8 bits ÷ 8 = 4.8 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 17.5 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M5.4 GBQ4_K_M + full context22.3 GB- What's the best quantization for Llama 3.1 Tulu 3 8B?
For Llama 3.1 Tulu 3 8B, Q4_K_M (5.4 GB) offers the best balance of quality and VRAM usage. Q4_K_L (5.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 3.3 GB.
VRAM requirement by quantization
IQ2_M3.3 GBIQ3_M4.2 GBQ4_K_S5.1 GBQ4_K_M ★5.4 GBQ5_K_M6.3 GBBF1616.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 3.1 Tulu 3 8B on a Mac?
Llama 3.1 Tulu 3 8B requires at least 3.3 GB at IQ2_M, 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 8B locally?
Yes — Llama 3.1 Tulu 3 8B can run locally on consumer hardware. At Q4_K_M quantization it needs 5.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3.1 Tulu 3 8B?
At Q4_K_M, Llama 3.1 Tulu 3 8B can reach ~891 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~122 tok/s. 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 ÷ 5.4 × 0.65 = ~965 tok/s
Estimated speed at Q4_K_M (5.4 GB)
~965 tok/s~122 tok/s~965 tok/s~891 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 8B?
At Q4_K_M, the download is about 4.82 GB. The full-precision BF16 version is 16.06 GB. The smallest option (IQ2_M) is 2.71 GB.
- Which GPUs can run Llama 3.1 Tulu 3 8B?
52 consumer GPUs can run Llama 3.1 Tulu 3 8B at Q4_K_M (5.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Llama 3.1 Tulu 3 8B?
59 devices with unified memory can run Llama 3.1 Tulu 3 8B at Q4_K_M (5.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.