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Models / Llama 3.2 3B
Llama 3.2 3B VRAM Requirements & GPU Pairing
Llama 3.2 3B is Meta's small gun for edge devices and entry-level GPUs: 3.21B dense parameters, 28 layers, standard GQA with 8 KV heads, and a native 128K context. Its value is not benchmark leadership but ubiquity — the Q4_K_M total at 8K is only ~4.4GB, so even aging 8GB cards run it with room to spare. On an RTX 3060 12GB it theoretically reaches ~137 tok/s, and a Mac mini M4 Pro manages ~104 tok/s.
The trade-off is reasoning depth: multi-step logic and long-chain reasoning fail noticeably more often than on 8B models. It fits classification, summarization, structured extraction, and lightweight agent routing. If your GPU has 12GB or more, there is rarely a reason to pick it over Llama 3.1 8B or Qwen3 8B; but under tight VRAM or latency pressure it remains the safest starting point. Q4_K_M is the sweet spot, and Q8_0 at ~5.9GB is trivial for 12GB cards. Note the llama3.2-community license has extra terms for very large-scale commercial use.
Architecture Specs
| Total parameters | 3.21B |
| Active parameters | 3.21B |
| Layers | 28 |
| KV heads | 8 |
| Head dim | 128 |
| Native context | 128K |
| License | llama3.2-community |
| KV bytes per token (fp16) | 112.0 KB |
VRAM by Quant Tier (@8K context, fp16 KV)
| Quant | Weights | Total (incl. 1.5GB runtime overhead) |
|---|---|---|
| Q8_0 | 3.4 GB | 5.9 GB |
| Q6_K | 2.6 GB | 5.1 GB |
| Q4_K_M | 2.0 GB | 4.4 GB |
| Q3_K_M | 1.6 GB | 4.0 GB |
| Q2_K | 1.3 GB | 3.7 GB |
KV cache and runtime overhead do not change across quants; longer contexts grow the KV part linearly — adjust context length in the GPU checker tool.
GPU Verdict Matrix (Q4_K_M @8K)
| GPU | Usable VRAM | Verdict | Recommended quant | Theoretical speed | |
|---|---|---|---|---|---|
| RTX 3060 12GB | 12.0 GB | Comfortable | FP16 | ≈42 tok/s | Try it → |
| RTX 3090 | 24.0 GB | Comfortable | FP16 | ≈109 tok/s | Try it → |
| RTX 4070 Ti Super | 16.0 GB | Comfortable | FP16 | ≈79 tok/s | Try it → |
| RTX 4090 | 24.0 GB | Comfortable | FP16 | ≈118 tok/s | Try it → |
| RTX 5090 | 32.0 GB | Comfortable | FP16 | ≈209 tok/s | Try it → |
| RTX A6000 | 48.0 GB | Comfortable | FP16 | ≈90 tok/s | Try it → |
| A100 80GB | 80.0 GB | Comfortable | FP16 | ≈238 tok/s | Try it → |
| H100 80GB | 80.0 GB | Comfortable | FP16 | ≈391 tok/s | Try it → |
| RX 7900 XTX | 24.0 GB | Comfortable | FP16 | ≈112 tok/s | Try it → |
| Mac mini M4 Pro (48GB) | 36.0 GB | Comfortable | FP16 | ≈32 tok/s | Try it → |
| Mac Studio M4 Max (64GB) | 48.0 GB | Comfortable | FP16 | ≈64 tok/s | Try it → |
| Mac Studio M3 Ultra (96GB) | 72.0 GB | Comfortable | FP16 | ≈96 tok/s | Try it → |
Theoretical speed = bandwidth × 0.75 ÷ per-token weight bytes (active params for MoE); real-world results vary with framework/driver/CPU, ±30%.
FAQ
- Can an 8GB GPU run Llama 3.2 3B?
- Yes. Q4_K_M @8K totals ~4.4GB, and even Q8_0 is only ~5.9GB, so 8GB is comfortable at 8K context. At the full 128K context the KV cache alone grows to ~15GB — reduce context or quantize KV.
- Llama 3.2 3B vs Llama 3.1 8B?
- With 12GB+ VRAM always pick the 8B — the quality jump is worth 3GB. Choose 3B only for sub-8GB cards or high-throughput lightweight tasks.
- What is Llama 3.2 3B good for?
- Latency-sensitive lightweight work: classification, summarization, keyword extraction, simple Q&A, and routing nodes in agent pipelines. Complex reasoning, code, and math are not its strengths.
Related guides
- Quantization Explained: Q4 vs Q8 and What You Actually Lose
- Run GGUF Models Locally: llama.cpp and Ollama Walkthrough
- How Much VRAM per Billion Parameters? (2026 Cheat Sheet)
Try Llama 3.2 3B in the GPU compatibility checker →
Data verified 2026-09-01