Meta Llama 3.1 8B Instruct — Hardware Requirements & GPU Compatibility
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This is an Unsloth repack of Meta's Llama 3.1 8B Instruct, optimized for efficient fine-tuning and inference. Llama 3.1 8B Instruct is one of the most widely used open-weight instruction-tuned models, delivering strong performance across general conversation, reasoning, and multilingual tasks. Unsloth's version provides the full-precision model weights in an optimized layout designed for their training and inference framework. At 8 billion parameters, this model offers a strong balance of capability and efficiency, suitable for users who want to fine-tune or run the model locally without additional quantization.
415.1K downloads
94 likesSpecifications
- Publisher
- Unsloth
- Family
- Llama 3
- Parameters
- 8.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-02-15
- License
- Llama 3.1 Community
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HuggingFace
How Much VRAM Does Meta Llama 3.1 8B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 2.8 GB | 19.7 GB | 2.21 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_M | 2.70 | 3.3 GB | 20.2 GB | 2.71 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 3.7 GB | 20.6 GB | 3.11 GB | Importance-weighted 3-bit |
| IQ3_XS | 3.30 | 3.9 GB | 20.8 GB | 3.31 GB | Importance-weighted 3-bit, extra small |
| 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 |
| IQ3_M | 3.60 | 4.2 GB | 21.1 GB | 3.61 GB | Importance-weighted 3-bit, medium |
| 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 |
| Q3_K_L | 4.10 | 4.7 GB | 21.6 GB | 4.12 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 4.9 GB | 21.8 GB | 4.32 GB | Importance-weighted 4-bit, compact |
| IQ4_NL | 4.50 | 5.1 GB | 22 GB | 4.52 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_S | 4.50 | 5.1 GB | 22 GB | 4.52 GB | 4-bit small quantization |
| Q4_1 | 4.50 | 5.1 GB | 22 GB | 4.52 GB | 4-bit legacy quantization with offset |
| Q4_K_M | 4.80 | 5.4 GB | 22.3 GB | 4.82 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 5.5 GB | 22.4 GB | 4.92 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 6.1 GB | 23 GB | 5.52 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 6.3 GB | 23.2 GB | 5.72 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 6.4 GB | 23.3 GB | 5.82 GB | 5-bit large quantization |
| Q6_K | 6.60 | 7.2 GB | 24.1 GB | 6.62 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 |
Which GPUs Can Run Meta Llama 3.1 8B Instruct?
Q4_K_M · 5.4 GBMeta Llama 3.1 8B Instruct (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. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Meta Llama 3.1 8B Instruct?
Q4_K_M · 5.4 GB33 devices with unified memory can run Meta Llama 3.1 8B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Meta Llama 3.1 8B Instruct need?
Meta Llama 3.1 8B Instruct requires 5.4 GB of VRAM at Q4_K_M, or 8.6 GB at Q8_0. 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 Meta Llama 3.1 8B Instruct?
For Meta Llama 3.1 8B Instruct, 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_XXS at 2.8 GB.
VRAM requirement by quantization
IQ2_XXS2.8 GB~53%Q3_K_S4.1 GB~77%IQ4_XS4.9 GB~87%Q4_K_M ★5.4 GB~89%Q4_K_L5.5 GB~90%Q8_08.6 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Meta Llama 3.1 8B Instruct on a Mac?
Meta Llama 3.1 8B Instruct requires at least 2.8 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 Meta Llama 3.1 8B Instruct locally?
Yes — Meta Llama 3.1 8B Instruct 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 Meta Llama 3.1 8B Instruct?
At Q4_K_M, Meta Llama 3.1 8B Instruct can reach ~541 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 5.4 × 0.55 = ~541 tok/s
Estimated speed at Q4_K_M (5.4 GB)
AMD Instinct MI300X~541 tok/sNVIDIA GeForce RTX 4090~122 tok/sNVIDIA H100 SXM~404 tok/sAMD Instinct MI250X~334 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Meta Llama 3.1 8B Instruct?
At Q4_K_M, the download is about 4.82 GB. The full-precision Q8_0 version is 8.03 GB. The smallest option (IQ2_XXS) is 2.21 GB.