ChatCodingAI CodingReasoning3.21B
Llama 3.2 3B Instruct
Llama · Llama 3.2 Community License
Ultra-lightweight model that runs on virtually any GPU. Surprisingly capable for its size — good at summarization, simple coding tasks, and quick chat. The default choice when speed matters more than depth.
- Parameters
- 3.21B
- Context
- 131,072 tokens
- Released
- 2024-09-25
- Engines
- llama.cpp, ollama, vLLM, TGI
- Builder Tools
- Cursor, Continue, Ollama, LM Studio, Open WebUI
Llama 3.2 3B Instruct (3.21B) requires 2.8 GB VRAM at recommended quality (Q6_K). At efficient quality (Q4_K_M), it fits in 2.1 GB VRAM, making it compatible with the NVIDIA GeForce RTX 3060 12GB. On NVIDIA GeForce RTX 5090, expect approximately 200 tok/s at Q8_0. For the best experience, Starter AI Desktop ($582) is recommended.
— OwnRig methodology, data updated 2026-03-15
VRAM Requirements
| Quality | Quantization | VRAM | File Size |
|---|---|---|---|
| full | Q8_0 | 3.7 GB | 3.2 GB |
| recommended | Q6_K | 2.8 GB | 2.4 GB |
| recommended | Q5_K_M | 2.5 GB | 2.1 GB |
| efficient | Q4_K_M | 2.1 GB | 1.8 GB |
| compressed | Q3_K_M | 1.7 GB | 1.4 GB |
Context Length Impact
KV cache VRAM at Q6_K quality. Longer context = more memory.
| Context | KV Cache | Total VRAM |
|---|---|---|
| 2K | 102 MB | 2.9 GB |
| 4K | 102 MB | 2.9 GB |
| 8K | 307 MB | 3.1 GB |
| 16K | 512 MB | 3.3 GB |
| 32K | 1 GB | 3.8 GB |
| 64K | 2 GB | 4.8 GB |
| 128K | 4.1 GB | 6.9 GB |
Compatible GPUs
Performance data for Llama 3.2 3B Instruct across different hardware.
| Device | Quantization | Speed | Rating | Fits in VRAM |
|---|---|---|---|---|
| NVIDIA GeForce RTX 3060 12GB | Q8_0 | 90 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 4060 Ti 16GB | Q8_0 | 75 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 4070 Ti Super | Q8_0 | 130 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 4070 Super | Q8_0 | 110 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 4080 Super | Q8_0 | 140 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 4090 | Q8_0 | 170 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 3090 | Q8_0 | 150 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 5080 | Q8_0 | 160 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 5090 | Q8_0 | 200 tok/s | Excellent | ✓ |
| Apple M4 Pro (24GB Unified) | Q8_0 | 60 tok/s | Excellent | ✓ |
| Apple M4 Pro (48GB) | Q8_0 | 60 tok/s | Excellent | ✓ |
| Apple M4 Max (36GB Unified) | Q8_0 | 100 tok/s | Excellent | ✓ |
| Apple M4 Max (64GB Unified) | Q8_0 | 100 tok/s | Excellent | ✓ |
| Apple M4 Max (128GB Unified) | Q8_0 | 100 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 4060 8GB | Q8_0 | 65 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 4070 Ti 12GB | Q8_0 | 95 tok/s | Excellent | ✓ |
| NVIDIA GeForce RTX 3080 10GB | Q8_0 | 140 tok/s | Excellent | ✓ |
| Apple M3 Pro (18GB Unified) | Q8_0 | 35 tok/s | Good | ✓ |
Builder Context
Llama 3.2 3B Instruct is commonly used with Cursor, Continue, Ollama, LM Studio, Open WebUI. For an AI coding workflow, pair it with an embedding model like nomic-embed-text for local RAG.
Recommended Builds
Complete PC builds that can run Llama 3.2 3B Instruct.
Frequently Asked Questions
- How much VRAM does Llama 3.2 3B Instruct need?
- Llama 3.2 3B Instruct requires 2.8 GB VRAM at recommended quality (Q6_K). At lower quality settings, it can fit in as little as 1.7 GB.
- What is the best GPU for Llama 3.2 3B Instruct?
- The NVIDIA GeForce RTX 5090 delivers the best performance for Llama 3.2 3B Instruct, achieving 200 tok/s at Q8_0 with an excellent rating.
- Can I run Llama 3.2 3B Instruct on an RTX 4060 Ti?
- Yes. On the NVIDIA GeForce RTX 4060 Ti 16GB, Llama 3.2 3B Instruct runs at 75 tok/s (Q8_0, excellent).
- What quantization should I use for Llama 3.2 3B Instruct?
- For the best quality, use Q6_K (2.8 GB VRAM). If your GPU has limited VRAM, Q3_K_M (1.7 GB) is the most efficient option with acceptable quality.
- Is Llama 3.2 3B Instruct good for coding?
- Yes. Llama 3.2 3B Instruct is used with Cursor, Continue, Ollama, LM Studio, Open WebUI for local AI coding. For the best coding experience, pair it with an embedding model for local RAG.
Data confidence: estimated. Last updated: 2026-03-15. Source