Hardware · NVIDIA Ada Lovelace · Reviewed June 2026
RTX 4080 for Local LLMs: The 16GB VRAM Tier
The RTX 4080 and RTX 4080 Super sit at the 16GB tier — one step above the 12GB cards (RTX 3060, RTX 4070 Ti) and one step below the 24GB tier (RTX 3090, RTX 4090). The extra 4GB over 12GB cards unlocks two meaningful upgrades for local AI: 7B models at full FP16 precision, and 13B/14B models at Q8 instead of Q4. The ceiling (30B+ models) does not open until 24GB.
Editorial review
This page covers both the RTX 4080 (716 GB/s) and RTX 4080 Super (736 GB/s). Both have 16GB VRAM — the model ceiling is identical. Bandwidth difference translates to under 5% difference in tokens per second.
Quick verdict
The RTX 4080 and 4080 Super are the best single-GPU option if 7B FP16 or 13B Q8 is your quality target and you do not need 30B models. The 16GB VRAM tier unlocks the two upgrades most local AI users actually want over 12GB: full-precision 7B inference and near-lossless quality on 13B models.
What it does not unlock is 30B models. A 30B model at Q4 needs 18–20GB, which exceeds 16GB. If 30B+ models are your primary use case, the jump to 24GB (RTX 3090 or RTX 4090) is the right move. If 7B–14B at the highest available quality is your target, the 4080 tier is the right card.
What this hardware can run
The 16GB budget opens FP16 for 7B models and Q8 for 13B/14B models — both impossible on 12GB cards.
| Model size | Best quantization | VRAM used | Verdict | Notes |
|---|---|---|---|---|
| 1B–4B | FP16 | 2–8 GB | Comfortable | Full precision on all small models. |
| 7B | FP16 | ~14 GB | Comfortable | Key upgrade vs 12GB. FP16 fits with ~2GB headroom. Full quality, no quantization loss. |
| 8B | Q8 | ~8.7 GB | Comfortable | 8B FP16 (~16 GB + overhead) does not fit. Q8 is the max quality tier — very close to FP16. |
| 13B | Q8 | ~14 GB | Comfortable | Key upgrade vs 12GB cards where 13B is Q4 only. Q8 fits with ~2GB headroom. |
| 14B | Q8 | ~15 GB | Comfortable (tight) | Fits with ~1GB headroom. Keep context windows under 16K to avoid pressure. |
| 30B–32B | Q4 | ~18–20 GB | CPU offload only | Exceeds 16GB. Possible at 1–5 t/s via RAM offload. Requires 24GB+ for usable speed. |
| 70B | Q4 | ~38–42 GB | Not recommended | Far exceeds 16GB. Needs 48GB+ VRAM or cloud. |
Best model sizes for this card
- 7B at FP16: The headline upgrade over 12GB cards. Full precision on a 7B model — no quantization quality loss — at ~14GB of VRAM. Runs at 30–50 tokens per second at 736 GB/s bandwidth. The right daily driver for high-quality chat and coding.
- 13B and 14B at Q8: The second meaningful upgrade. Q8 on 13B or 14B models is very close to FP16 quality and fits within 16GB. On 12GB cards, these models require Q4 quantization (lower quality). Qwen 2.5 14B and Phi-4 at Q8 are the recommended options.
- 4B at FP16: Very fast full-precision inference. Good for tools and agents where speed matters more than depth.
Recommended models
- Qwen 3 7B FP16:
ollama pull qwen3:7b-fp16(or 8B equivalent). Full precision. The recommended daily driver at the 16GB tier. - Qwen 2.5 14B Q8:
ollama pull qwen2.5:14b-q8_0. Near-FP16 quality on a 14B model. Fits at ~15GB with ~1GB headroom. Strong instruction following. - Phi-4 Mini FP16 — Small, fast, full precision. Under 4GB VRAM. Good for fast iteration alongside a larger model.
- Qwen 3 14B Q8:
ollama pull qwen3:14b-q8_0. Strong coding and reasoning at Q8 quality on 14B depth. ~15GB VRAM — fits with tight headroom.
Recommended runtimes
- Ollama — Easiest start. Use
:fp16and:q8_0tag suffixes to target specific quantizations. - LM Studio — Desktop GUI with built-in model browser. Good for comparing FP16 vs Q8 quality side by side.
- Open WebUI — Browser chat over Ollama. Run via Docker.
- llama.cpp: Direct inference engine for precise control over quantization and context window settings.
Best local AI workflows
- High-quality chat: 7B FP16 for fast, full-precision interactive responses. The primary use case advantage over 12GB cards.
- Coding assistance: 13B or 14B at Q8 for strong code generation and explanation. Meaningful quality improvement over Q4 at the same model size.
- Local RAG: 14B Q8 with a vector database. Good for document Q&A where quality matters.
- Agent workflows: 7B FP16 for fast tool-call loops. Speed is important for agentic chains — the 4080's bandwidth keeps latency per step low.
What this hardware cannot do well
- 30B+ models: Q4 on 30B needs 18–20GB. Exceeds 16GB. CPU offload works but is 1–5 t/s. The jump to 24GB is required.
- 8B at FP16: 8B FP16 weights + overhead (~17.5GB) exceeds 16GB. Q8 (8.7GB) is the max quality for 8B models on this card.
- Long context on 14B: At 14B Q8 (~15GB) and long context (16K+), KV cache growth can push past 16GB. Keep context windows under 12K for comfortable operation.
Upgrade path
- RTX 3090 (24GB, used): The natural next step. 24GB opens 30B at Q4, 8B at FP16, and 13B FP16 (just over). Often available at lower cost than a new 4090.
- RTX 4090 (24GB): 24GB at maximum consumer Ada Lovelace bandwidth. If 30B models and maximum speed are the goal, the 4090 is the right card.
Cloud fallback
- RunPod: RTX 4090 (24GB) and A100 instances for 30B+ models on demand.
- Lambda: A100 and H100 cloud GPU instances.
- Vast.ai: Marketplace GPU compute — often cheapest for short experimental runs.
Related hardware
FAQ
What does 16GB VRAM unlock that 12GB cannot do?
The two meaningful upgrades from 12GB to 16GB are: 7B models at FP16 (full precision, no quality loss from quantization) and 13B/14B models at Q8 instead of Q4. On a 12GB card, 7B FP16 needs 14GB and does not fit. On a 16GB card, 7B FP16 fits with ~2GB headroom. Similarly, 13B at Q8 needs approximately 14GB — comfortable on 16GB but exceeds 12GB. The model ceiling (30B+) does not change — you still cannot run 30B comfortably at 16GB.
Can the RTX 4080 run 30B models?
Not in VRAM. A 30B model at Q4_K_M needs approximately 18–20 GB of VRAM, which exceeds the 16 GB limit. CPU offload is possible but reduces generation to 1–5 tokens per second. For 30B models at usable speed, a 24GB card (RTX 3090 or RTX 4090) is required.
What is the difference between the RTX 4080 and RTX 4080 Super for local AI?
Both have 16GB of GDDR6X VRAM, so the model ceiling is identical. The RTX 4080 Super has slightly higher memory bandwidth (736 GB/s vs 716 GB/s) and a wider CUDA core count. For local AI inference, the practical difference in tokens per second is small — typically under 5%. The 4080 Super is the better value if prices are comparable, but the original 4080 is not meaningfully worse for local AI.
Can the RTX 4080 run 8B models at FP16?
Not cleanly. An 8B model at FP16 requires approximately 16GB of memory for weights, plus ~1.5GB of runtime overhead — totaling roughly 17.5GB. This exceeds the 16GB VRAM limit. The best you can do for 8B models on a 16GB card is Q8 (~8.7GB), which is very close to FP16 quality in practice. 7B at FP16 (14GB) fits comfortably.
Is the RTX 4080 worth it over the RTX 4070 Ti Super (16GB) for local AI?
Both have 16GB of VRAM — same model ceiling. The RTX 4080 has higher memory bandwidth (716–736 GB/s vs 672 GB/s on the 4070 Ti Super), which translates to approximately 6–10% more tokens per second at equivalent model sizes. If budget is the concern, the 4070 Ti Super is a strong alternative. If you want the fastest 16GB Ada Lovelace card for local AI, the 4080 Super edges it out.
Disclosure
OpenSourcesAI may earn a commission or referral fee from links to hardware retailers, cloud GPU providers, or partner tools on this page. Editorial assessments are produced independently. Hardware specs are sourced from NVIDIA documentation. Model VRAM estimates are derived from GGUF quantization formulas and may vary across runtime versions. Verify before purchasing.
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