Can I run Llama-3.1-8B-Instruct on an RTX 5080 (16GB)?
Computed with the open FitLLM engine — accurate per-layer KV-cache modeling, not a naive estimate. Updated 2026-10-19.
Conditions: Q4_K_M · KV cache F16 · 32K context · Windows + display reserve (2 GB).
Memory breakdown (Q4_K_M, F16 KV, 32K context)
| Model weights | 4.6 GB |
| KV cache | 4.0 GB |
| Runtime overhead + reserve | 4.1 GB |
| Total used | 12.7 / 16 GB |
| Free | 3.3 GB |
Context at Q4_K_M: recommended max ~48K tokens (keeps 5% headroom) · hard limit ~52K tokens · with Q8 KV cache → hard limit ~89K tokens.
Every quantization on the RTX 5080
| Weight quant | Weights | Recommended max ctx | Hard limit | Used @32K |
|---|---|---|---|---|
| Q4_K_M | 4.6 GB | ~48K | ~52K | ✅ 12.7 / 16.0 GB |
| Q5_K_M | 5.3 GB | ~43K | ~47K | ✅ 13.5 / 16.0 GB |
| Q6_K | 6.1 GB | ~37K | ~42K | ✅ 14.4 / 16.0 GB |
| Q8_0 | 7.9 GB | ~25K | ~30K | ❌ 16.5 / 16.0 GB |
| FP16 | 14.9 GB | won't fit | ❌ 24.3 / 16.0 GB | |
Recommended max = the longest context that still keeps the 5% headroom · hard limit = the longest that fits at all (KV cache F16).
Lower weight quants free memory at some output-quality cost — Q4 is the common sweet spot; below that quality drops faster.
KV cache is F16 here (llama.cpp default). Drop it to Q8/Q4 (-ctk/-ctv) for more context.
Embed this verdict
Live badge for your README or model card — recomputed by the engine at exactly these conditions, never stale:
[](https://fitllm.run/can-i-run/llama-3-1-8b-instruct-on-rtx-5080)
Or from your terminal (exit 0/1 — works as a pre-download guard):
npx fitllm "Llama-3.1-8B-Instruct" --gpu "RTX 5080" --quant Q4_K_M --ctx 32768 --kv 16Why most VRAM calculators get this wrong
Llama-3.1-8B-Instruct's KV cache is computed per layer with its real head_dim and grouped-query head count — not the uniform "all layers × full context" shortcut most calculators use.
Other options
same GPU Models that fit on the RTX 5080: Gemma 4 e2b, Gemma 4 e4b, Gemma 4 12b, Llama-3.2-3B-Instruct, Llama-3.1-8B-Instruct, MiniCPM5-1B, Qwen3-0.6B, Qwen3-1.7B, Llama-3.2-1B-Instruct, Gemma-3-1B-it, Spark-X2.5-4B, MiniCPM5-2B.
same model GPUs that run Llama-3.1-8B-Instruct: RTX 5090 (32GB), RTX 5080 (16GB), RTX 5070 Ti (16GB), RTX 5070 (12GB), RTX 4090 (24GB), RTX 4080 SUPER (16GB), RTX 4070 Ti SUPER (16GB), RTX 4070 (12GB), RTX 4060 Ti 16GB (16GB), RTX 3090 (24GB), RTX 3090 Ti (24GB), RTX 3080 10GB (10GB), RTX 3080 12GB (12GB), RTX 3060 12GB (12GB), RTX 5060 Ti 16GB (16GB), RTX 4070 Ti (12GB), RTX 4080 (16GB), RTX 2080 Ti (11GB), RTX 6000 Ada (48GB), RTX PRO 6000 Blackwell (96GB), RX 7900 XTX (24GB), RX 7900 XT (20GB), RX 7800 XT (16GB), RX 9070 XT (16GB), RX 9070 (16GB), Radeon PRO W7900 (48GB), 2× RTX 3090 (48GB), 2× RTX 4090 (48GB), 4× RTX 3090 (96GB), A100 40GB (40GB), A100 80GB (80GB), H100 80GB (80GB), H200 141GB (141GB), B200 (180GB), RTX PRO 5000 Blackwell 48GB (48GB), RTX PRO 5000 Blackwell 72GB (72GB), RTX PRO 4500 Blackwell (32GB), RTX PRO 4000 Blackwell (24GB), RTX PRO 2000 Blackwell (16GB), DGX Spark 128GB (128GB), 2× DGX Spark 128GB (256GB), 4× DGX Spark 128GB (512GB).
Reproduce it
Llama-3.1-8B-Instruct = 8B, 32 layers. The RTX 5080 has 16GB / 960GB/s. Same math, open source: fitllm-engine. GGUF bpw from llama.cpp.
All numbers are computed by the open-source fitllm-engine (MIT) from official model config.json values — reproduce or audit them yourself. Estimates; real usage varies with runtime (llama.cpp / MLX / Ollama), driver and display. Found a mismatch? Report it. · FitLLM home