Llama-3.1-8B-Instruct on NVIDIA RTX 5080 (16GB VRAM)
Full FP16 (18.2 GB) exceeds 16 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 6.67 GB (5.01 GB weights + 1 GB KV cache at 8K context), delivering ~122.3 tokens/sec.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
6.67 GB / 16 GB🚀 Ready-to-Run Launch Command
ollama run llama3.1:8b # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-3.1-8B-Instruct-GGUF -c 8192 -ngl 99 -fa
Need 6.67 GB+ VRAM to run Llama-3.1-8B-Instruct?
Recommended cloud setup for 4-bit: RTX 4090 (24GB) (or 18.2 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-3.1-8B-Instruct on NVIDIA RTX 5080
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x NVIDIA RTX 5080 (16GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 16.54 GB | 1 GB | 18.2 GB | No (Over by 2.2GB) | ~4.8 tok/s | 2x NVIDIA RTX 5080 |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 8.79 GB | 1 GB | 10.45 GB | Yes (65%) | ~69.8 tok/s | 1x NVIDIA RTX 5080 |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 8.79 GB | 1 GB | 10.45 GB | Yes (65%) | ~69.8 tok/s | 1x NVIDIA RTX 5080 |
| GGUF Q6_K (6-bit) | 6.56 bpw | 6.78 GB | 1 GB | 8.44 GB | Yes (53%) | ~90.4 tok/s | 1x NVIDIA RTX 5080 |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 5.88 GB | 1 GB | 7.54 GB | Yes (47%) | ~104.2 tok/s | 1x NVIDIA RTX 5080 |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 5.01 GB | 1 GB | 6.67 GB | Yes (42%) | ~122.3 tok/s | 1x NVIDIA RTX 5080 |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 4.39 GB | 1 GB | 6.05 GB | Yes (38%) | ~139.5 tok/s | 1x NVIDIA RTX 5080 |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 4.04 GB | 1 GB | 5.7 GB | Yes (36%) | ~151.7 tok/s | 1x NVIDIA RTX 5080 |
Frequently Asked Questions (Llama-3.1-8B-Instruct + NVIDIA RTX 5080)
Can NVIDIA RTX 5080 (16GB) run Llama-3.1-8B-Instruct locally?
Yes — NVIDIA RTX 5080 (16GB) can run Llama-3.1-8B-Instruct using FP8 (W8A8 / E4M3). Full FP16 (18.2 GB) exceeds 16 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 6.67 GB (5.01 GB weights + 1 GB KV cache at 8K context), delivering ~122.3 tokens/sec.
How is KV Cache calculated for Llama-3.1-8B-Instruct?
Llama-3.1-8B-Instruct uses Dense-GQA with 32 transformer layers, 32 attention heads, and 8 KV heads (head dimension 128). At 8,192 tokens in FP16, KV cache consumes 1 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 0.50 GB.