Meta
Llama 3.1 8B: Local Deployment Guide
Short answer: Llama 3.1 8B runs locally with ollama run llama3.1:8b. That pulls the Q4_K_M build — about 4.7 GB of weights, so an 8 GB GPU is the practical floor and 12 GB is comfortable. The 128K context window is real, but filling it costs roughly 16 GB of KV cache on top of the weights, which is why almost nobody actually runs it at 128K on a consumer card.
Meta's Llama 3.1 8B is the smallest model in the July 2024 refresh, and it keeps the 128K context window, tool calling, and multilingual support of its larger siblings. At Q4_K_M the weights are roughly 4.7 GB, which is what makes it one of the most accessible open-weight models to run locally. It is one of the most accessible LLMs you can run locally; if you are still choosing a card, start with the AI hardware guide.
Specifications
- Model family
- llama-3-1
- Version
- 3.1
- Parameters
- 8B
- Context window
- 128K tokens
- Modalities
- text
- Languages
- English, German, French, Italian, Portuguese, Hindi, Spanish, Thai
- License
- Llama 3.1 Community License
Benchmark scores published by Meta
Every figure below is copied from the Meta-Llama-3.1-8B-Instruct model card, with the shot setting Meta used. Nothing here is our own evaluation.
- MMLU: 69.4 % — Meta model card, 5-shot macro average (73.0 with 0-shot CoT)
- GSM8K: 84.5 % — Meta model card, 8-shot chain-of-thought
- HumanEval: 72.6 % — Meta model card, 0-shot pass@1
- ARC-Challenge: 83.4 % — Meta model card, 0-shot accuracy
- MATH: 51.9 % — Meta model card, 0-shot CoT final exact match
Benchmark performance
How much VRAM does Llama 3.1 8B need?
About 4.7 GB for the weights at Q4_K_M, which is what ollama run llama3.1:8b downloads by default. You can work the rest out yourself with one rule of thumb:
weights (GB) ≈ parameters (billions) × bytes per weight
Q4_K_M ≈ 0.6 GB per billion parameters → 8 × 0.6 ≈ 4.8 GB
Q4_K_M is not exactly 4 bits per weight — the k-quant format keeps some tensors at higher precision, which is why the practical constant is ~0.6 GB/B rather than 0.5. The published Ollama file size, 4.7 GB, lands where the arithmetic says it should.
| Quantization | Bytes/weight | Weights, 8B model | Smallest card that fits |
|---|---|---|---|
| Q3_K_M | ~0.5 | ~4.0 GB | 6 GB (quality drops noticeably) |
| Q4_K_M (default) | ~0.6 | ~4.7 GB | 8 GB |
| Q5_K_M | ~0.7 | ~5.7 GB | 8 GB |
| Q8_0 | ~1.1 | ~8.5 GB | 12 GB |
| FP16 (unquantized) | 2.0 | ~16 GB | 24 GB |
Storage is the same number — you need the file on disk before it reaches the GPU. On CPU-only machines the weights live in system RAM instead, so 16 GB of RAM is the equivalent floor. For the wider ladder across model sizes, see the 8 GB RAM model guide.
How fast will Llama 3.1 8B run on my hardware?
Token generation on a local LLM is bandwidth-bound: to emit one token the runtime has to stream every weight it is using out of memory. That gives you a hard ceiling you can compute before you buy anything:
ceiling (tokens/sec) = memory bandwidth (GB/s) ÷ model size in memory (GB)
This is an arithmetic upper bound, not a measurement. Real output lands well below it: the formula ignores attention over the KV cache, prompt processing, sampling, and the fact that no runtime achieves 100% of a vendor’s peak bandwidth figure. Treat it as “this hardware definitely cannot beat X”, never as a prediction.
| Hardware | Memory bandwidth (spec) | Ceiling at Q4_K_M |
|---|---|---|
| RTX 4090 | 1,008 GB/s | under ~214 tok/s |
| RTX 4070 | 504 GB/s | under ~107 tok/s |
| RTX 3060 12GB | 360 GB/s | under ~77 tok/s |
| Apple M2 (base) | 100 GB/s | under ~21 tok/s |
| CPU, dual-channel DDR5-5600 | ~90 GB/s | under ~19 tok/s |
Two things follow. First, a bigger GPU only helps until the model already fits — an RTX 4090 and an RTX 3060 both hold this model comfortably, so the difference between them is bandwidth, not capacity. Second, if the model does not fit and layers spill to system RAM, the slow number in that table becomes your effective ceiling for the spilled portion. Fitting the model is worth more than raw GPU speed.
What does the 128K context window actually cost?
This is the number missing from most Llama 3.1 8B write-ups. The weights are only half the memory bill; the KV cache grows linearly with how many tokens you have in the window, and for this model it is expensive:
KV bytes/token = 2 (K and V) × layers × KV heads × head dim × bytes per value
= 2 × 32 × 8 × 128 × 2 (fp16) = 131,072 bytes ≈ 128 KB per token
Layer count, KV-head count and head dimension come from the published Llama 3.1 8B configuration (32 layers, 8 key/value heads under grouped-query attention, head dimension 128).
| Context in use | KV cache (fp16) | Total with 4.7 GB of weights |
|---|---|---|
| 2K tokens | ~0.25 GB | ~5 GB |
| 8K tokens (Ollama’s common default window) | ~1 GB | ~5.7 GB |
| 32K tokens | ~4 GB | ~8.7 GB |
| 128K tokens (full window) | ~16 GB | ~21 GB |
So the honest version of “128K context on an 8 GB card” is: the model supports it, your GPU does not. Around 20K–32K tokens is where a 12 GB card runs out of room. Quantizing the KV cache to 8-bit roughly halves those figures, at some cost to long-context recall.
How is Llama 3.1 8B different from Llama 3 8B?
Meta’s release notes list four changes that matter for local use:
- Context window 8K → 128K. The headline change, with the memory bill described above.
- Tool calling. The instruct model is trained for function calling, which is what makes agent frameworks work without a wrapper prompt.
- Broader multilingual support. Eight languages are officially supported rather than English alone.
- Refreshed training mix, with a knowledge cutoff of December 2023.
Same parameter count, same architecture family, same VRAM footprint at a given quantization — so upgrading from Llama 3 8B costs you nothing but the download.
How do I install Llama 3.1 8B?
The one-line path is Ollama, which pulls the Q4_K_M build and starts a chat session:
ollama run llama3.1:8bTo raise the context window past the default, pass it explicitly (and re-check the KV cache table above before you do): /set parameter num_ctx 32768. LM Studio and llama.cpp read the same GGUF files if you prefer a GUI or a raw binary. The alternative is to take the weights straight from the vendor:
- Download the latest weights from Download Llama 3.1 8B.
- Verify your hardware can accommodate the 8B parameter checkpoint and 128K tokens context window.
- Follow the vendor documentation Hugging Face model card for runtime setup and inference examples.
📚 Research & Documentation
Meta Research
💡 Sourcing note: Every specification and benchmark figure on this page comes from Meta’s model card and release notes, linked above. The memory and throughput tables are arithmetic derived from published parameter counts, the model configuration, and manufacturers’ bandwidth specifications — the formulas are shown so you can check them. We do not publish first-party hardware benchmarks.
What is Llama 3.1 8B good and bad at?
Where an 8B model earns its place
- • Summarising and rewriting documents you already have — the task is bounded, and the model is not being asked to know anything.
- • Structured extraction: pulling JSON out of messy text, with the schema in the prompt.
- • Drafting and boilerplate code, checked by you before it runs.
- • Tool-calling agents where the model routes to a real API instead of answering from memory.
- • Anything privacy-bound, where the constraint is “this text must not leave the machine” rather than raw capability.
Where it will disappoint you
- • Factual recall on niche topics. An 8B model has limited room for world knowledge, and the cutoff is December 2023 — pair it with retrieval rather than trusting it.
- • Long multi-step reasoning. Meta’s own MATH figure (51.9) is the honest signal: it is a competent assistant, not a solver.
- • Very long contexts in practice. Attention quality at 128K is a different question from whether the window is advertised, and the memory bill above usually settles it first.
- • Tasks where a 70B model is the right answer. If quality matters more than latency and you have the VRAM, compare against Llama 3.1 70B.
Licensing, in one paragraph
Llama 3.1 ships under the Llama 3.1 Community License, not a standard open-source licence. Commercial use is permitted, attribution requirements apply, and services with more than 700 million monthly active users need a separate licence from Meta. Read the licence text yourself before you build a product on it — a summary on a blog, including this one, is not legal advice.
Llama 3.1 8B Architecture and Capabilities
Llama 3.1 8B's architecture: 32 transformer layers with grouped-query attention, a 128K context window, and native tool calling
Related Models & Resources
Larger Llama Models
- Llama 3.1 70B - Enterprise-grade performance
- Llama 3.1 405B - State-of-the-art capabilities
Setup & Optimization Guides
- Quantization Explained - What Q4_K_M trades away for that 4.7 GB
- Hardware Requirements Guide - Optimal setup configurations
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Related Guides
Continue your local AI journey with these comprehensive guides
Llama 3.1 70B: Enhanced Performance
Technical analysis of the 70B parameter variant for enterprise applications.
Quantization Explained
What Q4_K_M actually does to the weights, and where quality starts to fall off.
Best Local AI Models for 8GB RAM
The wider model ladder when memory, not preference, is the constraint.
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Last verified on October 1, 2024 by Localaimaster Team
Sources (Click to expand)
- ai.meta.comcontextWindowFetched October 1, 2024https://ai.meta.com/blog/meta-llama-3-1/
- ai.meta.comlanguagesFetched October 1, 2024https://ai.meta.com/blog/meta-llama-3-1/
- ai.meta.comlicenseFetched October 1, 2024https://ai.meta.com/blog/meta-llama-3-1/
- ai.meta.commodalitiesFetched October 1, 2024https://ai.meta.com/blog/meta-llama-3-1/
- ai.meta.comparametersFetched October 1, 2024https://ai.meta.com/blog/meta-llama-3-1/
- ai.meta.comreleaseDateFetched October 1, 2024https://ai.meta.com/blog/meta-llama-3-1/
- ai.meta.comvendorFetched October 1, 2024https://ai.meta.com/blog/meta-llama-3-1/
- ai.meta.comvendorUrlFetched October 1, 2024https://ai.meta.com/blog/meta-llama-3-1/
- huggingface.comodelCardUrlFetched October 1, 2024https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct
- huggingface.coresourcesFetched October 1, 2024https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct
All data aggregated from official model cards, papers, and vendor documentation. Errors may exist; please report corrections via admin@localaimaster.com.
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