Llama 3.1 8B Instruct — Hardware Requirements & GPU Compatibility
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Meta Llama 3.1 8B Instruct is an 8-billion parameter instruction-tuned language model from Meta. Part of the Llama 3.1 release, it supports a 128K token context window and is fine-tuned for conversational use, tool calling, and general assistant tasks. Its compact size makes it well-suited for local deployment on modern consumer GPUs with 8GB or more of VRAM. Llama 3.1 8B Instruct delivers strong performance for its parameter class across benchmarks in reasoning, coding, and multilingual understanding. It is released under the Llama 3.1 Community License and is widely supported by inference frameworks such as llama.cpp, vLLM, and Ollama.
9.9M downloads
6.1K likesSpecifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 8.0B
- Context Length
- 131,072 tokens
- Release Date
- 2024-07-18
- License
- Llama 3.1 Community
Get Started
HuggingFace
How Much VRAM Does Llama 3.1 8B Instruct 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_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 |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Llama 3.1 8B Instruct?
Q4_K_M · 5.3 GBLlama 3.1 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. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Llama 3.1 8B Instruct?
Q4_K_M · 5.3 GB58 devices with unified memory can run Llama 3.1 8B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Apple iPhone 17 Pro.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Llama 3.1 8B Instruct
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Llama 3.1 8B Instruct need?
Llama 3.1 8B Instruct requires 5.3 GB of VRAM at Q4_K_M, or 17.7 GB at BF16.
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 Llama 3.1 8B Instruct?
For Llama 3.1 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 IQ3_XS at 3.6 GB.
VRAM requirement by quantization
IQ3_XS3.6 GBIQ3_M4.0 GBIQ4_XS4.8 GBQ4_K_M ★5.3 GBQ5_K_M6.3 GBBF1617.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 3.1 8B Instruct on a Mac?
Llama 3.1 8B Instruct requires at least 3.6 GB at IQ3_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 Llama 3.1 8B Instruct locally?
Yes — Llama 3.1 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 Llama 3.1 8B Instruct?
At Q4_K_M, Llama 3.1 8B Instruct can reach ~830 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 5.3 × 0.65 = ~981 tok/s
Estimated speed at Q4_K_M (5.3 GB)
~981 tok/s~124 tok/s~981 tok/s~830 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama 3.1 8B Instruct?
At Q4_K_M, the download is about 4.82 GB. The full-precision BF16 version is 16.06 GB. The smallest option (IQ3_XS) is 3.31 GB.
- Which GPUs can run Llama 3.1 8B Instruct?
50 consumer GPUs can run Llama 3.1 8B Instruct at Q4_K_M (5.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Llama 3.1 8B Instruct?
59 devices with unified memory can run Llama 3.1 8B Instruct at Q4_K_M (5.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.