Llama 3.1 8B Instruct
Llama · Llama 3.1 Community License
Best-in-class 8B model. General capabilities with capable coding support. The go-to small model for local inference: fast, accurate, and well-supported across all inference engines.
- Parameters
- 8.03B
- Architecture
- Dense
- Context
- 131,072 tokens
- Released
- 2024-07-23
- Engines
- llama.cpp, ollama, vLLM, TGI
- Builder Tools
- Cursor, Continue, Aider, Open WebUI, LM Studio
Parameters
8.03B
VRAM
6.7 GB
Context
128K
Formats
5
GPUs
41
Llama 3.1 8B Instruct (8.03B) requires 6.7 GB VRAM at recommended quality (Q6_K). At efficient quality (Q4_K_M), it fits in 4.9 GB VRAM, making it compatible with the NVIDIA RTX 4060 Laptop (40-60W). On NVIDIA Grace Blackwell Ultra GB300, expect approximately 350 tok/s at Q8_0. For the best experience, Starter AI Desktop ($582) is recommended.
Source: OwnRig methodology
6.7 GB
Q6_K
5.8 GB
128K tokens
Chat
VRAM Requirements
| Quality | Quantization | VRAM | File Size |
|---|---|---|---|
| full | Q8_0 | 8.9 GB | 8 GB |
| recommended | Q6_K | 6.7 GB | 5.8 GB |
| recommended | Q5_K_M | 5.8 GB | 4.8 GB |
| efficient | Q4_K_M | 4.9 GB | 4 GB |
| compressed | Q3_K_M | 4 GB | 3.1 GB |
Context Length Impact
KV cache VRAM at Q6_K quality. Longer context = more memory.
| Context | KV Cache | Total VRAM |
|---|---|---|
| 2K | 102 MB | 6.8 GB |
| 4K | 307 MB | 7 GB |
| 8K | 512 MB | 7.2 GB |
| 16K | 1 GB | 7.7 GB |
| 32K | 2 GB | 8.7 GB |
| 64K | 4.1 GB | 10.8 GB |
| 128K | 8.2 GB | 14.9 GB |
Compatible GPUs
41 devicesShowing 41 of 41 entries
Builder Context
Llama 3.1 8B Instruct is commonly used with Cursor, Continue, Aider, Open WebUI, LM Studio. For an AI coding workflow, pair it with an embedding model like nomic-embed-text for local RAG.
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Frequently Asked Questions
- How much VRAM does Llama 3.1 8B Instruct need?
- Llama 3.1 8B Instruct requires 6.7 GB VRAM at recommended quality (Q6_K). At lower quality settings, it can fit in as little as 4 GB.
- What is the best GPU for Llama 3.1 8B Instruct?
- The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for Llama 3.1 8B Instruct, achieving 350 tok/s at Q8_0 with an excellent rating.
- Can I run Llama 3.1 8B Instruct on an RTX 4060 Ti?
- Yes. On the NVIDIA GeForce RTX 4060 Ti 16GB, Llama 3.1 8B Instruct runs at 55 tok/s (Q8_0, excellent).
- What quantization should I use for Llama 3.1 8B Instruct?
- For the best quality, use Q6_K (6.7 GB VRAM). If your GPU has limited VRAM, Q3_K_M (4 GB) is the most efficient option with acceptable quality.
- Is Llama 3.1 8B Instruct good for coding?
- Yes. Llama 3.1 8B Instruct is used with Cursor, Continue, Aider, Open WebUI, LM Studio for local AI coding. For the best coding experience, pair it with an embedding model for local RAG.
Related Guides
Tutorial
The Complete Guide to Running LLMs Locally
Run large language models locally: hardware needs, Ollama and llama.cpp, model picks by use case, and quantization.
Explainer
VRAM: The Only Spec That Matters for AI
VRAM for local AI: what it is, why models need it, how quantization cuts requirements, and a VRAM table for major models.
Data confidence: verified. Source
VRAM requirements are calculated from model parameters and may vary by inference engine, context length, and batch size. Performance estimates are based on community benchmarks and should be verified for your specific configuration.Llama is a trademark of its respective owner. OwnRig is not affiliated with or endorsed by the model creator.