Meta Llama 3.1 8B Instruct GGUF — Hardware Requirements & GPU Compatibility
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This is a GGUF-quantized version of Meta's Llama 3.1 8B Instruct, repackaged by Bartowski. Llama 3.1 8B Instruct is one of the most popular open-weight models available, offering strong general-purpose instruction following, reasoning, and multilingual capabilities in a highly efficient 8-billion-parameter package. Bartowski's GGUF conversion makes this model ready to use with llama.cpp and compatible frontends like Ollama, LM Studio, and KoboldCpp. At 8B parameters, it strikes an excellent balance between quality and hardware requirements, running well on modern consumer GPUs with 8GB or more of VRAM, and even on CPU for users willing to trade speed for accessibility.
292.6K downloads
329 likesSpecifications
- Publisher
- Bartowski
- Family
- Llama 3
- Parameters
- 8B
- Release Date
- 2024-12-01
- License
- Llama 3.1 Community
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HuggingFace
How Much VRAM Does Meta Llama 3.1 8B Instruct GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_M | 2.70 | 3.0 GB | — | 2.70 GB | Importance-weighted 2-bit, medium |
| IQ3_XS | 3.30 | 3.6 GB | — | 3.30 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 3.7 GB | — | 3.40 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.9 GB | — | 3.50 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 4.0 GB | — | 3.60 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 4.3 GB | — | 3.90 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.4 GB | — | 4.00 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 4.5 GB | — | 4.10 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 4.7 GB | — | 4.30 GB | Importance-weighted 4-bit, compact |
| IQ4_NL | 4.50 | 5.0 GB | — | 4.50 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_S | 4.50 | 5.0 GB | — | 4.50 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 5.3 GB | — | 4.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 5.4 GB | — | 4.90 GB | 4-bit large quantization |
| Q5_K_S | 5.50 | 6.0 GB | — | 5.50 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 6.3 GB | — | 5.70 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 6.4 GB | — | 5.80 GB | 5-bit large quantization |
| Q6_K | 6.60 | 7.3 GB | — | 6.60 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.8 GB | — | 8.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Meta Llama 3.1 8B Instruct GGUF?
Q4_K_M · 5.3 GBMeta Llama 3.1 8B Instruct GGUF (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 3.1 8B Instruct GGUF?
Q4_K_M · 5.3 GB33 devices with unified memory can run Meta Llama 3.1 8B Instruct GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Meta Llama 3.1 8B Instruct GGUF need?
Meta Llama 3.1 8B Instruct GGUF requires 5.3 GB of VRAM at Q4_K_M, or 8.8 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 8B × 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 3.1 8B Instruct GGUF?
For Meta Llama 3.1 8B Instruct GGUF, Q4_K_M (5.3 GB) offers the best balance of quality and VRAM usage. Q4_K_L (5.4 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 3.0 GB.
VRAM requirement by quantization
IQ2_M3.0 GB~62%IQ3_M4.0 GB~78%IQ4_NL5.0 GB~88%Q4_K_M ★5.3 GB~89%Q5_K_S6.0 GB~92%Q8_08.8 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Meta Llama 3.1 8B Instruct GGUF on a Mac?
Meta Llama 3.1 8B Instruct GGUF requires at least 3.0 GB at IQ2_M, 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 GGUF locally?
Yes — Meta Llama 3.1 8B Instruct GGUF 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 3.1 8B Instruct GGUF?
At Q4_K_M, Meta Llama 3.1 8B Instruct GGUF can reach ~552 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 = ~552 tok/s
Estimated speed at Q4_K_M (5.3 GB)
AMD Instinct MI300X~552 tok/sNVIDIA GeForce RTX 4090~124 tok/sNVIDIA H100 SXM~413 tok/sAMD Instinct MI250X~341 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 GGUF?
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_M) is 2.70 GB.