Llama 3.1 8B

Overview

Meta's Llama 3.1 8B, the open-weight benchmark of 2024. A 128k context, well-behaved instruction follower with the largest ecosystem in the open-source world.

When to pick this model

• General-purpose chat or assistant deployments on a single consumer GPU
• Long-context RAG up to 128k tokens
• Production workloads needing the most mature open-weight tooling
• Fine-tuning baselines for downstream tasks
• Drop-in replacement for Mistral 7B with longer context

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	6 GB
Q5_K_M	7 GB
Q8_0	10 GB
FP16 (no quantization)	18 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

In practice, Llama 3.1 8B fits an 8 GB consumer card at Q4_K_M (6 GB). Stepping up to Q8_0 raises the footprint to 10 GB, and unquantized FP16 weights take 18 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, Llama 3.1 8B needs roughly 10 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 10 tokens/sec on entry-level GPUs, on the order of 30 tokens/sec on a mid-range card, and up to 80 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches Llama 3.1 8B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

GPU memory	Example cards	Best fit for Llama 3.1 8B
8 GB	RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB	Q5_K_M (7 GB used)
12 GB	RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop	Q8_0 (10 GB used)
16 GB	RTX 5080, RTX 4080 Super, Radeon RX 9070 XT	Q8_0 (10 GB used)
24 GB	RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop	FP16 (18 GB used)
32 GB	RTX 5090	FP16 (18 GB used)

Which GPU should you buy to run Llama 3.1 8B?

To run Llama 3.1 8B locally at Q4, you need ~6 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).

Published benchmark scores

Benchmark	Score
MMLU	73
HumanEval	72.6
GPQA	46.7

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

To put Llama 3.1 8B in context: its MMLU score of 73 ranks #19 of the 34 catalog models with a published MMLU result (catalog median 73.4); its HumanEval score of 72.6 ranks #18 of the 26 catalog models with a published HumanEval result (catalog median 81.1). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.

Strengths

• 128k context window
• Strong instruction following and coding
• Enormous ecosystem of fine-tunes and integrations
• Solid quality-to-size ratio

Limitations

• Beaten by Qwen 3 8B on most 2025 benchmarks
• No vision in this checkpoint
• Llama Community license restricts use above 700M MAU

Typical workloads

In our catalog grid, Llama 3.1 8B is filed under Chat, Document Analysis, RAG — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: everyday chat, drafting and summarization.

The 128k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. It ships under the Llama 3 Community license — commercial use is generally possible but read the specific terms before embedding it in a product.

Architecture & training

Architecture: Dense Transformer · 32 layers · GQA · Llama 3.1 8B

Training: 15T multilingual tokens from Meta. Instruction-following fine-tuning.

Quick start

ollama run llama3.1:8b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

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Frequently asked questions

How much VRAM does Llama 3.1 8B need?

At the recommended Q4_K_M quantization, Llama 3.1 8B needs about 6 GB of VRAM. Q8_0 takes 10 GB, and unquantized FP16 weights take 18 GB.

Can Llama 3.1 8B run without a GPU?

Yes — with roughly 10 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does Llama 3.1 8B support?

Llama 3.1 8B supports a 128k-token context window (131,072 tokens).

Can I use Llama 3.1 8B commercially?

Llama 3.1 8B ships under the Llama 3 Community license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.

How fast is Llama 3.1 8B on consumer hardware?

Our compatibility engine estimates on the order of 30 tokens/sec on a mid-range GPU and up to 80 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of Llama 3.1 8B should I download first?

Start with Q4_K_M (6 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q5_K_M.