Llama 3.1 8B Instruct
Meta's small flagship. Strong general reasoning, 128K context, broad multilingual. The default first try for most local-AI use cases on consumer hardware.
Positioning
The default 8B-class model for anyone who wants a permissive, English-strong, runs-everywhere chat assistant. If you have an RTX 3060 12 GB or anything stronger, this is the model you start with — it's the one the entire local-LLM tutorial ecosystem is calibrated against.
Strengths
- Fits everything: Q4_K_M is 4.6 GB. Runs on a 6 GB card with reduced context, comfortably on 8 GB+, and at full 128K context on a 12 GB+ card with KV cache trimming.
- Instruction following is excellent: handles multi-turn, system prompts, JSON-mode-via-prompt, and tool-call-style outputs without the brittleness Mistral 7B shows.
- Genuinely permissive license: the Llama 3.1 Community License allows commercial use up to 700M MAUs — which is everyone reading this.
Limitations
- Math and code are average, not strong. For coding work, Qwen 2.5 Coder 7B is meaningfully better.
- 128K context is nominal, not real — quality starts degrading past ~32K tokens, and effective recall over very long inputs is weaker than the spec suggests.
- Alignment refusals are noticeable in technical domains (security research, pen-testing tutorials). Hermes-3-8B is a good uncensored alternative on the same base.
Real-world performance on RTX 4090
- Q4_K_M (4.6 GB): 95–115 tok/s decode, TTFT under 80 ms on a 1K prompt
- Q5_K_M (5.6 GB): 88–100 tok/s
- Q8_0 (8.5 GB): 70–82 tok/s — the quality bump over Q5 is small; rarely worth the speed loss
Should you run this locally?
Yes, for general assistant work, summarization, drafting, RAG pipelines, and as the chat model behind tooling/agents that need a fast, predictable backbone. No, for serious code generation (use Qwen 2.5 Coder), heavy reasoning (use QwQ 32B or DeepSeek R1 Distill), or non-English tasks where Qwen 2.5 7B is consistently stronger.
How it compares
- vs Qwen 2.5 7B → Qwen wins on knowledge breadth and multilingual tasks; Llama wins on instruction reliability and ecosystem maturity. Coin flip with the edge to Qwen if you're comfortable using it.
- vs Mistral 7B v0.3 → Llama wins decisively on instruction following and long-context behavior. Mistral 7B is the previous default; there's no reason to start there now.
- vs Phi-3.5 Mini (3.8B) → Llama is far more capable; Phi is the right pick only when VRAM is genuinely tight (sub-6 GB cards).
- vs Llama 3.2 3B → Llama 3.1 8B is materially better at almost everything but uses ~2× the VRAM. The 3B is for VRAM-constrained edge devices.
Run this yourself
ollama pull llama3.1:8b-instruct-q4_K_M
ollama run llama3.1:8b-instruct-q4_K_M
›Why this rating
8.7/10 — the boring, correct answer for almost every "I have an 8 GB GPU and want a chat model" question. Loses points only because Qwen 2.5 7B has overtaken it on raw capability per parameter.
Overview
Meta's small flagship. Strong general reasoning, 128K context, broad multilingual. The default first try for most local-AI use cases on consumer hardware.
Featured in this workflow
Full-system workflows that include this model as part of their service ledger — with the one-line operator note for each.
- Workflow · System·homelab·Role: General-purpose chat modelPrivate job-search assistant
Strong English instruction-following at the 8B size, fits 12 GB at Q5_K_M with 8K context, runs on Apple Silicon via MLX. Mature license, well-understood failure modes.
Family & lineage
How this model relates to others in its lineage. Family members share architecture and training-data roots; parent / children edges record direct distillation or fine-tune relationships.
Strengths
- 128K context
- Excellent instruction following
- Strong tool/function calling
Weaknesses
- Refusals on edge use cases
- Slower than 3B siblings
- No vision
Prompting kit
Tested patterns for getting the most out of Llama 3.1 8B Instruct locally. Local models are pickier about prompt structure than cloud models — what works on Claude or GPT-5 often fails here.
Recommended system prompt
You are a helpful, honest, and concise assistant. Answer the user's question directly. If you don't know something, say so rather than guessing.
Quirks to know
- •Predecessor to Llama 3.3 8B. Per Meta's release notes, Llama 3.3 8B is a drop-in upgrade — no migration changes needed — but Llama 3.1 8B is still widely deployed in production fine-tunes.
- •128K context window per the model card. Same context limit as 3.3.
- •Multilingual: 8 languages — English, German, French, Italian, Portuguese, Hindi, Spanish, Thai.
- •Native tool calling per the model card, but Meta's release notes flag 3.1's tool-call reliability as materially lower than 3.3's. If tool calling matters, prefer Llama 3.3 8B.
- •Per Meta's responsible-use guide, the 8B is more refusal-prone than the 70B — anchor system prompts to a specific persona to suppress generic disclaimers.
Chat template
Llama 3 format with <|begin_of_text|>, <|start_header_id|>{role}<|end_header_id|>, <|eot_id|> — same template as Llama 3.3 and Llama 3.2.
Tool calling
Per the model card, JSON function call format. Reliability is lower than Llama 3.3 8B — re-prompt on parse failures or migrate to 3.3 8B.
Sampler settings
- temperature
- 0.6
- top_p
- 0.9
Meta's evaluation harness defaults. Drop to 0.1-0.3 for tool calling and structured output.
Reviewed quality benchmarks
First-party rows were run by RunLocalAI; reviewed community rows are labeled in the data. Every row links to the raw test-run log.
| Benchmark | Quant | Runtime / Hardware | Score | Raw log |
|---|---|---|---|---|
HumanEval+ tested 2026-05-28 | Q4_K_M | ollama-0.24 rtx-3080-16gb-mobile | 56.1/100 | Gist → |
MBPP+ tested 2026-05-29 | Q4_K_M | ollama-0.24 rtx-3080-16gb-mobile | 39.2/100 | Gist → |
Q4_K_M note:First-party HumanEval+ on RTX 3080 Laptop 16GB via Ollama 0.24. Windows-safe scoring via scripts/evalplus_score_windows.py.
Q4_K_M note:First-party MBPP+ on RTX 3080 Laptop 16GB via Ollama 0.24. Windows-safe scoring via scripts/evalplus_score_windows.py.
Want to verify? Every row links to its Gist with full stdout and stderr of the run. The runner script is in the public repo (scripts/run-humaneval-plus.ts) — reproducible end-to-end. Browse all coding scores at /benchmarks/coding.
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 4.9 GB | 6 GB |
| Q5_K_M | 5.7 GB | 7 GB |
| Q8_0 | 8.5 GB | 10 GB |
| FP16 | 16.1 GB | 18 GB |
Get the model
Ollama
One-line install
ollama run llama3.1:8bRead our Ollama review →HuggingFace
Original weights
Source repository — direct quantization required.
Benchmarks
Real measurements on real hardware. Numbers ship with the runner version, quant, and date.
| Hardware | Provenance | Quant | Ctx | Tokens / sec | TTFT | Date |
|---|---|---|---|---|---|---|
| NVIDIA GeForce RTX 5080 | EditorialM | Q4_K_M | 4K | 135.6tok/s | 130 ms | May 28, 26 |
What to do next
Got this model running on real hardware? Share what you measured — the form arrives with the model pre-selected.
Hardware that runs this
Cards with enough VRAM for at least one quantization of Llama 3.1 8B Instruct.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run Llama 3.1 8B Instruct?
Can I use Llama 3.1 8B Instruct commercially?
What's the context length of Llama 3.1 8B Instruct?
How do I install Llama 3.1 8B Instruct with Ollama?
Compare against other models
Curated head-to-head decisions where Llama 3.1 8B Instruct is one of the contenders. For arbitrary pairings use /model-battle.
Source: huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Llama 3.1 8B Instruct runs on your specific hardware before committing money.