The RTX 5080 is the fastest 16GB card for local AI. At 94 tokens per second for 8B models, it outperforms even the RTX 3090 in raw speed while costing less. The best choice for users who want top speed with 14B models.
16GB GDDR7 at 960 GB/s makes the RTX 5080 the bandwidth champion of 16GB cards. It loads 14B models at Q4 with ~5GB headroom and pushes tokens at 94 tok/s. For context: the RTX 3090 achieves 87 tok/s with 24GB VRAM. The 5080 is faster despite 8GB less memory. If your models fit in 16GB, this card maximizes speed. For models that need 20GB+, you will need to step up to the RTX 4090 or 5090.
| GPU | VRAM | Speed | Bandwidth | Price |
|---|---|---|---|---|
| RTX 5070 Ti | 16 GB | 87 tok/s | 896 GB/s | $749 |
| RTX 5080 | 16 GB | 94 tok/s | 960 GB/s | $999 |
| RTX 4080 SUPER | 16 GB | 79 tok/s | 736 GB/s | $1,597 |
| RTX 5090 | 32 GB | 145 tok/s | 1792 GB/s | $2,499 |
Llama / 8B / Q4_K_M / ~6.5 GB
Best for: Chat, Coding·Pop: 94/100
Perf: ~94.0 tok/s · first token ~0.4s
Fits in 16 GB VRAM with room to spare. Best for chat, coding on RTX 5080.
ollama run llama3.1:8b-instruct-q4_K_M
Qwen / 9B / Q4_K_M / ~7 GB
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: ~85.0 tok/s · first token ~0.4s
Fits in 16 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 5080.
ollama run qwen3.5:9b-instruct-q4_K_M
Qwen / 8B / Q4_K_M / ~6.5 GB
Best for: Chat, Coding·Pop: 88/100
Perf: ~94.0 tok/s · first token ~0.4s
Fits in 16 GB VRAM with room to spare. Best for chat, coding on RTX 5080.
ollama run qwen3:8b-q4_K_M
Mistral / 7B / Q4_K_M / ~5.5 GB
Best for: Chat, Coding·Pop: 90/100
Perf: ~105.3 tok/s · first token ~0.3s
Fits in 16 GB VRAM with room to spare. Best for chat, coding on RTX 5080.
ollama run mistral:7b-instruct-q4_K_M
Qwen / 7B / Q4_K_M / ~5.5 GB
Best for: Coding·Pop: 85/100
Perf: ~105.3 tok/s · first token ~0.3s
Fits in 16 GB VRAM with room to spare. Best for coding on RTX 5080.
ollama run qwen2.5-coder:7b-q4_K_M
Qwen / 7B / Q4_K_M / ~5.5 GB
Best for: Chat, Coding·Pop: 86/100
Perf: ~105.3 tok/s · first token ~0.3s
Fits in 16 GB VRAM with room to spare. Best for chat, coding on RTX 5080.
ollama run qwen2.5:7b-instruct-q4_K_M
LFM2 / 8B / Q4_K_M / ~6 GB
Best for: Local agents, tool calling, fast chat·Pop: 75/100
Perf: ~94.0 tok/s · first token ~0.4s
Fits in 16 GB VRAM with room to spare. Best for local agents, tool calling, fast chat on RTX 5080.
ollama run liquidai/lfm2:8b-a1b-instruct-q4_K_M
DeepSeek / 7B / Q4_K_M / ~5.5 GB
Best for: Reasoning, Coding·Pop: 77/100
Perf: ~105.3 tok/s · first token ~0.3s
Fits in 16 GB VRAM with room to spare. Best for reasoning, coding on RTX 5080.
ollama run deepseek-r1-distill:qwen-7b-q4_K_M
Llama / 8B / Q5_K_M / ~8 GB
Best for: Chat, Coding·Pop: 82/100
Perf: ~80.8 tok/s · first token ~0.4s
Fits in 16 GB VRAM with room to spare. Best for chat, coding on RTX 5080.
ollama run llama3.1:8b-instruct-q5_K_M
Gemma / 9B / Q4_K_M / ~7 GB
Best for: Chat, Coding·Pop: 81/100
Perf: ~85.0 tok/s · first token ~0.4s
Fits in 16 GB VRAM with room to spare. Best for chat, coding on RTX 5080.
ollama run gemma2:9b-instruct-q4_K_M
Alibaba Cloud — Widest size range (0.5B to 235B)
LlamaMeta — Most popular open-weight model family
DeepSeekDeepSeek AI — Best-in-class reasoning with R1 models
MistralMistral AI — Excellent performance-per-parameter ratio
GemmaGoogle DeepMind — Excellent quality at small sizes (1B-9B)
PhiMicrosoft — Best quality-per-parameter in small sizes
The RTX 5080 has 16GB GDDR7 VRAM with 960 GB/s bandwidth — the highest of any 16GB consumer card. About 15.5GB is usable for models. Perfect for 14B models at Q4 with room for generous context windows.
Up to 14B parameter models at Q4 quantization. The 5080 runs them at 94 tok/s, faster than any other 16GB card. For 32B models, you need 24GB+ VRAM — consider the RTX 4090 or 5090 instead.
The RTX 5080 is the best 16GB card for AI speed in 2026. At 94 tok/s, it beats the RTX 3090 while costing less. The only downside is that 16GB limits you to 14B models — the 5090 (32GB) unlocks 70B models.
The RTX 5090 (32GB) has double the VRAM and 54% more speed (145 vs 94 tok/s), but costs 2.5x more ($2,499 vs $999). Get the 5080 if 14B models are enough. Get the 5090 only if you need 32B-70B models.
Both have 16GB VRAM. The 5080 is 8% faster (94 vs 87 tok/s) but costs $250 more. For most AI workloads, the 5070 Ti is the better value. The 5080 is for users who want maximum speed from 16GB.
Use our interactive wizard to compare models across Apple Silicon and NVIDIA GPUs.