Llama 3.3 8B Instruct
Meta's Llama 3.3 at 8B. Drop-in upgrade from Llama 3.1 8B; same hardware envelope, better instruction following.
Positioning
Llama 3.3 8B Instruct is a dense 8-billion-parameter model released by Meta under the Llama Community License. It is positioned as a drop-in upgrade from Llama 3.1 8B, offering improved instruction following within the same hardware envelope. With a context length of 131,072 tokens, it supports long-form reasoning and document-level tasks. The model is designed for consumer-tier deployment, making it accessible for single-GPU setups.
Strengths
- Drop-in upgrade from Llama 3.1 8B: Operators already running Llama 3.1 8B can replace it with this model without changing hardware or infrastructure.
- Large 128K context window: Supports processing of long documents, multi-turn conversations, and extended reasoning tasks.
- Permissive Llama Community License: Allows commercial use, redistribution, and fine-tuning, making it suitable for business applications.
- Consumer-friendly quant sizes: At Q4_K_M, the model occupies ~4.5 GB on disk, fitting comfortably on most consumer GPUs with 8–12 GB VRAM.
Limitations
- No community benchmarks yet: We do not have independent measurements for this model. Published vendor metrics should be treated as best-case.
- Dense architecture at 8B: While efficient, the model may lag behind larger or MoE models on complex reasoning tasks.
- KV cache overhead at full context: At 131K tokens, the KV cache can exceed the model weights, requiring significant VRAM for long-context use.
- License restrictions: The Llama Community License imposes acceptable use policies and may not be compatible with all commercial workflows.
What it takes to run this locally
At FP16, the model requires ~16 GB on disk. Quantized versions reduce this significantly: Q8_0 ~9 GB, Q6_K ~6.6 GB, Q5_K_M ~5.7 GB, Q4_K_M ~4.5 GB, Q3_K_M ~3.9 GB, Q2_K ~2.6 GB. For inference, add ~30–50% for KV cache and framework overhead at typical context lengths. This model fits within the consumer deployment class, running on a single 12–24 GB GPU.
Should you run this locally?
Yes if you need a reliable, permissively licensed 8B chat model for consumer hardware and want the latest instruction-following improvements from Meta. No if your tasks require frontier-level reasoning or you cannot accommodate the KV cache overhead for very long contexts.
Catalog cross-links
- Llama 3.1 8B Instruct
- Llama 3 8B Instruct
- Consumer GPU Guide
Overview
Meta's Llama 3.3 at 8B. Drop-in upgrade from Llama 3.1 8B; same hardware envelope, better instruction following.
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
- Drop-in upgrade from 3.1 8B
- Better instruction polish
Weaknesses
- Llama Community License unchanged
Prompting kit
Tested patterns for getting the most out of Llama 3.3 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
- •Small Llama 3.3 sibling for entry-tier rigs (fits comfortably in 8GB VRAM at Q4_K_M). Per Meta's release notes, it's the recommended drop-in for Llama 3.1 8B with no migration changes required.
- •Same multilingual support as the 70B — 8 languages: English, German, French, Italian, Portuguese, Hindi, Spanish, Thai.
- •128K context window per the model card. Same as the 70B; quality drops faster past 32K on the smaller model.
- •Native tool calling — same JSON function call format as Llama 3.3 70B. Per the model card, tool-call reliability is lower than the 70B; constrain output schema strictly.
- •Per Meta's responsible-use guide, anchor the system prompt to a specific persona to suppress generic disclaimers (more important for the 8B than the 70B because the smaller model is more refusal-prone).
Chat template
Same Llama 3 template as the 70B — <|begin_of_text|>, <|start_header_id|>{role}<|end_header_id|>, <|eot_id|>.
Tool calling
Native function calling per the model card. Schema reliability drops vs the 70B — use a strict JSON schema validator on the runtime side and re-prompt on parse failures.
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.
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 | 7 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of Llama 3.3 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 3.3 8B Instruct?
Can I use Llama 3.3 8B Instruct commercially?
What's the context length of Llama 3.3 8B Instruct?
Source: huggingface.co/meta-llama/Llama-3.3-8B-Instruct
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Llama 3.3 8B Instruct runs on your specific hardware before committing money.