Llama-3.2-3B-Instruct on NVIDIA RTX 4080 Super (16GB VRAM)
In 4-bit Q4_K_M, Llama-3.2-3B-Instruct uses only 3.54 GB of your 16 GB VRAM (22% utilization) at ~234.5 tokens/sec. In full 16-bit precision, it requires 8.14 GB.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
3.54 GB / 16 GB🚀 Ready-to-Run Launch Command
ollama run llama3.2:3b # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-3.2-3B-Instruct-GGUF -c 8192 -ngl 99 -fa
Need 3.54 GB+ VRAM to run Llama-3.2-3B-Instruct?
Recommended cloud setup for 4-bit: RTX 4090 (24GB) (or 8.14 GB for full FP16). Spin up a pre-configured vLLM / Ollama instance in 60 seconds:
| Provider | Best For | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Bonus / Trial | Action |
|---|---|---|---|---|---|---|
| RunPod | 1-Click vLLM / Ollama Pods | $0.44 / hr | $1.39 / hr | $2.39 / hr | Instant PyTorch / vLLM Pods + Serverless GPU | Deploy on RunPod → |
| Vast.ai | Cheapest Spot GPU Market | $0.29 / hr | $0.95 / hr | $1.79 / hr | Save 50–70% on RTX 4090 / 5090 / A100 rentals | Check Spot GPUs → |
| Vultr Cloud GPU | Enterprise SLA & Bare Metal | — | $1.79 / hr | $2.69 / hr | $300 Free Credit for New Users | Claim $300 Credit → |
| DigitalOcean GPU Droplets | Managed H100 Droplets | — | — | $2.99 / hr | $200 Free Trial (60 Days) | Get $200 Free → |
All Quantization Formats: Llama-3.2-3B-Instruct on NVIDIA RTX 4080 Super
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x NVIDIA RTX 4080 Super (16GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 6.61 GB | 0.88 GB | 8.14 GB | Yes (51%) | ~71.1 tok/s | 1x NVIDIA RTX 4080 Super |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 3.51 GB | 0.88 GB | 5.04 GB | Yes (32%) | ~133.8 tok/s | 1x NVIDIA RTX 4080 Super |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 3.51 GB | 0.88 GB | 5.04 GB | Yes (32%) | ~133.8 tok/s | 1x NVIDIA RTX 4080 Super |
| GGUF Q6_K (6-bit) | 6.56 bpw | 2.71 GB | 0.88 GB | 4.24 GB | Yes (27%) | ~173.4 tok/s | 1x NVIDIA RTX 4080 Super |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 2.35 GB | 0.88 GB | 3.88 GB | Yes (24%) | ~199.9 tok/s | 1x NVIDIA RTX 4080 Super |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 2 GB | 0.88 GB | 3.54 GB | Yes (22%) | ~234.5 tok/s | 1x NVIDIA RTX 4080 Super |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 1.76 GB | 0.88 GB | 3.29 GB | Yes (21%) | ~267.6 tok/s | 1x NVIDIA RTX 4080 Super |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 1.62 GB | 0.88 GB | 3.15 GB | Yes (20%) | ~280 tok/s | 1x NVIDIA RTX 4080 Super |
Frequently Asked Questions (Llama-3.2-3B-Instruct + NVIDIA RTX 4080 Super)
Can NVIDIA RTX 4080 Super (16GB) run Llama-3.2-3B-Instruct locally?
Yes — NVIDIA RTX 4080 Super (16GB) easily runs Llama-3.2-3B-Instruct even in full FP16. In 4-bit Q4_K_M, Llama-3.2-3B-Instruct uses only 3.54 GB of your 16 GB VRAM (22% utilization) at ~234.5 tokens/sec. In full 16-bit precision, it requires 8.14 GB.
How is KV Cache calculated for Llama-3.2-3B-Instruct?
Llama-3.2-3B-Instruct uses Dense-GQA with 28 transformer layers, 24 attention heads, and 8 KV heads (head dimension 128). At 8,192 tokens in FP16, KV cache consumes 0.88 GB. Enabling 8-bit KV cache (-ctk q8_0 -ctv q8_0 in llama.cpp or --kv-cache-dtype fp8 in vLLM) cuts this in half to 0.44 GB.