Meta Llama 3 8B Instruct — Hardware Requirements & GPU Compatibility
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Meta Llama 3 8B Instruct is the instruction-tuned version of Meta's Llama 3 8B base model, with 8 billion parameters. It is fine-tuned for dialogue and chat use cases using supervised fine-tuning and RLHF, making it ready for conversational applications out of the box. The model supports an 8K token context window and performs well across coding, reasoning, and general knowledge tasks. Its efficient size makes it one of the most popular models for local inference on consumer hardware. Released under the Meta Llama 3 Community License.
1.4M downloads
4.4K likesSpecifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 8.0B
- Release Date
- 2025-06-18
- License
- Llama 3 Community
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HuggingFace
How Much VRAM Does Meta Llama 3 8B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XS | 2.40 | 2.6 GB | — | 2.41 GB | Importance-weighted 2-bit, extra small |
| IQ3_XS | 3.30 | 3.6 GB | — | 3.31 GB | Importance-weighted 3-bit, extra small |
| 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 |
| Q3_K_L | 4.10 | 4.5 GB | — | 4.12 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 4.8 GB | — | 4.32 GB | Importance-weighted 4-bit, compact |
| 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_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 Instruct?
Q4_K_M · 5.3 GBMeta Llama 3 8B Instruct (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 Instruct?
Q4_K_M · 5.3 GB33 devices with unified memory can run Meta Llama 3 8B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (7)
Frequently Asked Questions
- How much VRAM does Meta Llama 3 8B Instruct need?
Meta Llama 3 8B Instruct 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 Instruct?
For Meta Llama 3 8B Instruct, Q4_K_M (5.3 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 2.6 GB.
VRAM requirement by quantization
IQ2_XS2.6 GB~57%Q3_K_S3.9 GB~77%IQ4_XS4.8 GB~87%Q4_K_M ★5.3 GB~89%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 Instruct on a Mac?
Meta Llama 3 8B Instruct requires at least 2.6 GB at IQ2_XS, 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 Instruct locally?
Yes — Meta Llama 3 8B Instruct 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 Instruct?
At Q4_K_M, Meta Llama 3 8B Instruct 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 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_XS) is 2.41 GB.