Meta Llama 3.1 8B Instruct AWQ INT4 — Hardware Requirements & GPU Compatibility
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This is an AWQ INT4-quantized version of Meta's Llama 3.1 8B Instruct, produced by Hugging Face's Hugging Quants project. Llama 3.1 8B Instruct is a widely adopted open-weight model known for its strong instruction-following, reasoning, and multilingual abilities. The AWQ INT4 quantization from Hugging Face aggressively compresses the model to 4-bit integer precision using activation-aware weight quantization, significantly reducing VRAM requirements while retaining most of the original model's quality. This format is optimized for GPU inference with frameworks like vLLM, AutoAWQ, and Transformers, making it a practical choice for users who want fast, memory-efficient local inference on consumer GPUs.
169.6K downloads
88 likesSpecifications
- Publisher
- Hugging Face
- Family
- Llama 3
- Parameters
- 8B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-08-07
- License
- Llama 3.1 Community
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How Much VRAM Does Meta Llama 3.1 8B Instruct AWQ INT4 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 | 2.20 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_M | 2.70 | 3.3 GB | 20.2 GB | 2.70 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 3.7 GB | 20.6 GB | 3.10 GB | Importance-weighted 3-bit |
| IQ3_XS | 3.30 | 3.9 GB | 20.8 GB | 3.30 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 4.0 GB | 20.9 GB | 3.40 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.1 GB | 21.0 GB | 3.50 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 4.2 GB | 21.1 GB | 3.60 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 4.5 GB | 21.4 GB | 3.90 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.6 GB | 21.5 GB | 4.00 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 4.7 GB | 21.6 GB | 4.10 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 4.9 GB | 21.8 GB | 4.30 GB | Importance-weighted 4-bit, compact |
| IQ4_NL | 4.50 | 5.1 GB | 22.0 GB | 4.50 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_S | 4.50 | 5.1 GB | 22.0 GB | 4.50 GB | 4-bit small quantization |
| Q4_1 | 4.50 | 5.1 GB | 22.0 GB | 4.50 GB | 4-bit legacy quantization with offset |
| Q4_K_M | 4.80 | 5.4 GB | 22.3 GB | 4.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 5.5 GB | 22.4 GB | 4.90 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 6.1 GB | 23.0 GB | 5.50 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 6.3 GB | 23.2 GB | 5.70 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 6.4 GB | 23.3 GB | 5.80 GB | 5-bit large quantization |
| Q6_K | 6.60 | 7.2 GB | 24.1 GB | 6.60 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.6 GB | 25.5 GB | 8.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Meta Llama 3.1 8B Instruct AWQ INT4?
Q4_K_M · 5.4 GBMeta Llama 3.1 8B Instruct AWQ INT4 (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, 7+ 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 AWQ INT4?
Q4_K_M · 5.4 GB33 devices with unified memory can run Meta Llama 3.1 8B Instruct AWQ INT4, 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
Frequently Asked Questions
- How much VRAM does Meta Llama 3.1 8B Instruct AWQ INT4 need?
Meta Llama 3.1 8B Instruct AWQ INT4 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 = 8B × 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 AWQ INT4?
For Meta Llama 3.1 8B Instruct AWQ INT4, 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 AWQ INT4 on a Mac?
Meta Llama 3.1 8B Instruct AWQ INT4 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 AWQ INT4 locally?
Yes — Meta Llama 3.1 8B Instruct AWQ INT4 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 AWQ INT4?
At Q4_K_M, Meta Llama 3.1 8B Instruct AWQ INT4 can reach ~543 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 = ~543 tok/s
Estimated speed at Q4_K_M (5.4 GB)
AMD Instinct MI300X~543 tok/sNVIDIA GeForce RTX 4090~122 tok/sNVIDIA H100 SXM~406 tok/sAMD Instinct MI250X~336 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 AWQ INT4?
At Q4_K_M, the download is about 4.80 GB. The full-precision Q8_0 version is 8.00 GB. The smallest option (IQ2_XXS) is 2.20 GB.