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
Get Started
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 |
| Q3_K_L | 4.10 | 4.5 GB | — | 4.12 GB | 3-bit large quantization |
| Q4_1 | 4.50 | 5.0 GB | — | 4.52 GB | 4-bit legacy quantization with offset |
| Q4_K_S | 4.50 | 5.0 GB | — | 4.52 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 5.3 GB | — | 4.82 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 5.5 GB | — | 5.02 GB | 5-bit legacy quantization |
| Q5_1 | 5.50 | 6.1 GB | — | 5.52 GB | 5-bit legacy quantization with offset |
| Q5_K_S | 5.50 | 6.1 GB | — | 5.52 GB | 5-bit small quantization |
| 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 GB~75%Q4_04.4 GB~85%Q4_K_M ★5.3 GB~89%Q5_05.5 GB~90%Q5_K_S6.1 GB~92%Q8_08.8 GB~99%★ 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)
AMD Instinct MI300X~550 tok/sNVIDIA GeForce RTX 4090~124 tok/sNVIDIA H100 SXM~411 tok/sAMD Instinct MI250X~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.