Meta Llama 3 8B — Hardware Requirements & GPU Compatibility
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Meta Llama 3 8B is an 8-billion parameter base (pretrained) language model from Meta's Llama 3 release. As a base model, it is not fine-tuned for chat or instructions and is intended for further fine-tuning, research, or as a foundation for custom applications. It uses grouped-query attention and was trained on over 15 trillion tokens. Llama 3 8B supports an 8K token context window and delivers strong benchmark performance across language understanding, reasoning, and coding tasks for its size. It is released under the Meta Llama 3 Community License and runs efficiently on consumer GPUs with 8GB or more of VRAM.
3.1M downloads
6.5K likesSpecifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 8.0B
- Release Date
- 2024-09-27
- License
- Llama 3 Community
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HuggingFace
How Much VRAM Does Meta Llama 3 8B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.8 GB | — | 3.41 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.9 GB | — | 3.51 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.3 GB | — | 3.91 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.4 GB | — | 4.02 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.3 GB | — | 4.82 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.3 GB | — | 5.72 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.3 GB | — | 6.62 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.8 GB | — | 8.03 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Meta Llama 3 8B?
Q4_K_M · 5.3 GBMeta Llama 3 8B (Q4_K_M) requires 5.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Meta Llama 3 8B?
Q4_K_M · 5.3 GB33 devices with unified memory can run Meta Llama 3 8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (4)
Frequently Asked Questions
- How much VRAM does Meta Llama 3 8B need?
Meta Llama 3 8B requires 5.3 GB of VRAM at Q4_K_M, or 8.8 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 8.0B × 4.8 bits ÷ 8 = 4.8 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M5.3 GB- What's the best quantization for Meta Llama 3 8B?
For Meta Llama 3 8B, Q4_K_M (5.3 GB) offers the best balance of quality and VRAM usage. Q5_0 (5.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.8 GB.
VRAM requirement by quantization
Q2_K3.8 GBQ4_04.4 GBQ4_K_M ★5.3 GBQ5_05.5 GBQ5_K_S6.1 GBQ8_08.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Meta Llama 3 8B on a Mac?
Meta Llama 3 8B requires at least 3.8 GB at Q2_K, 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 8B locally?
Yes — Meta Llama 3 8B can run locally on consumer hardware. At Q4_K_M quantization it needs 5.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Meta Llama 3 8B?
At Q4_K_M, Meta Llama 3 8B can reach ~550 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~124 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 ÷ 5.3 × 0.55 = ~550 tok/s
Estimated speed at Q4_K_M (5.3 GB)
~550 tok/s~124 tok/s~411 tok/s~340 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 8B?
At Q4_K_M, the download is about 4.82 GB. The full-precision Q8_0 version is 8.03 GB. The smallest option (Q2_K) is 3.41 GB.
- Which GPUs can run Meta Llama 3 8B?
35 consumer GPUs can run Meta Llama 3 8B at Q4_K_M (5.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Meta Llama 3 8B?
33 devices with unified memory can run Meta Llama 3 8B at Q4_K_M (5.3 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.