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.

VRAM16 GB GDDR6X
Memory bandwidth736 GB/s (Super) / 717 GB/s (4080)
ArchitectureAda Lovelace (RTX 40 series)
Local AI tierComfortable 7B–8B Q8 / 13B–14B Q4_K_M
NVLinkNot supported
Best runtimesOllama · LM Studio · llama.cpp

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedJune 2026SourcesNVIDIA GPU specifications, GGUF quantization documentation (llama.cpp), Hugging Face model cards, and OpenSourcesAI 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 best single-GPU option when 7B FP16 or 13B Q8 is the quality target — the 16GB tier's two real unlocks over 12GB — but not a 30B card.

  • 7BFull FP16 with ~2 GB headroom — impossible on 12GB cards
  • 13B–14BQ8 near-lossless (Q4-only on 12GB cards)
  • 30B–32BDoes not fit — CPU offload only at 1–5 t/s
  • 4080 vs SuperSame 16GB ceiling; under 5% speed difference

Good fit for

  • High-quality daily chat and coding at 7B FP16, 30–50 tokens per second
  • Users stepping up from 12GB specifically for 13B/14B at Q8
  • Fast full-precision 4B models for tools and agents

Wrong fit for

  • 30B+ models — Q4 needs 18–20 GB, so the 24GB tier is the right move
  • Long (16K+) context on 14B Q8 — KV cache pushes past 16GB

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.

Check 16GB model fit in the checker →

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 sizeBest quantizationVRAM usedVerdictNotes
1B–4BFP162–8 GBComfortableFull precision on all small models.
7BFP16~14 GBComfortableKey upgrade vs 12GB. FP16 fits with ~2GB headroom. Full quality, no quantization loss.
8BQ8~8.7 GBComfortable8B FP16 (~16 GB + overhead) does not fit. Q8 is the max quality tier — very close to FP16.
13BQ8~14 GBComfortableKey upgrade vs 12GB cards where 13B is Q4 only. Q8 fits with ~2GB headroom.
14BQ8~15 GBComfortable (tight)Fits with ~1GB headroom. Keep context windows under 16K to avoid pressure.
30B–32BQ4~18–20 GBCPU offload onlyExceeds 16GB. Possible at 1–5 t/s via RAM offload. Requires 24GB+ for usable speed.
70BQ4~38–42 GBNot recommendedFar 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.

Getting started: first setup on the RTX 4080

# Install Ollama (Linux/macOS)
curl -fsSL https://ollama.com/install.sh | sh

# The key 16GB unlock: 7B at full FP16 precision
ollama pull qwen3:7b-fp16
ollama run qwen3:7b-fp16

# Or 13B at Q8 — near-lossless quality impossible on 12GB
ollama pull qwen2.5:14b-q8_0
ollama run qwen2.5:14b-q8_0

# Fast 4B FP16 for agents and autocomplete
ollama pull phi4-mini
ollama run phi4-mini

Recommended runtimes

  • Ollama — Easiest start. Use :fp16 and :q8_0 tag 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 GPU fallback

The RTX 4080 handles 30B models at Q4 and most daily local AI workflows. Cloud GPU is useful for 70B+ models, 8B at FP16 experiments, fine-tuning jobs, and multi-GPU workloads.

Best cloud GPU for on-demand inference and spot rentals

RunPod

RunPod provides on-demand and spot GPU rentals with RTX 4090 (24 GB), A100 (80 GB), and H100 instances. Good fit for 70B model experiments, FP16 fine-tuning runs, and full-stack AI app backends beyond the RTX 4080's 16GB ceiling.

Pros

  • RTX 4090 and A100 instances for larger VRAM requirements
  • Spot pricing makes large-model experiments affordable
  • Full-stack GPU pods with persistent storage and networking

Cons

  • Spot instances can be interrupted mid-run
  • Requires Docker familiarity for custom environments

Partner link: OpenSourcesAI may earn a commission if you sign up.

Visit RunPod

Best cloud GPU for ML training and large-model fine-tuning

Lambda

Lambda Labs offers A100 and H100 GPU cloud instances built for serious ML workloads. Ideal when you need to fine-tune a 70B model, run 8B at FP16 precision, or scale batch jobs beyond what the RTX 4080 can handle locally.

Pros

  • A100 and H100 instances for serious ML workloads
  • Clean pricing, no hidden fees, ML-focused infrastructure
  • Strong fit for fine-tuning and large batch inference jobs

Cons

  • Less flexible than RunPod for short spot experiments
  • GPU availability can vary by region and instance type
Visit Lambda

Related hardware

RTX 4070 Ti 12GB — the tier belowRTX 4090 24GB — the tier aboveRTX 3090 24GB — used-market 24GB alternativeWhat can 16GB VRAM run? — full tier guideCheck 16GB model fit in the checker

FAQ

What does 16GB VRAM unlock that 12GB cannot do?

Three meaningful upgrades from 12GB to 16GB: 7B-8B models at Q8 move from a tight fit to a comfortable one, 13B/14B models at Q4_K_M gain real headroom for context, and a 24B model like Mistral Small 3.1 at Q4_K_M newly fits (tightly). What 16GB does not unlock: 7B-8B at FP16 (~16 GB of measured weights plus runtime overhead) and 13B/14B at Q8 (~15-15.7 GB of weights, needing ~17 GB total) — both start at 24GB. The 30B+ ceiling does not change either — you still cannot run 30B in VRAM 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.

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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.

Last verified: June 2026

Related guides

OllamaRun open models locally with a simple CLI, model library, desktop app, and local API.LM StudioDesktop app for discovering, downloading, chatting with, and serving local LLMs.llama.cppCore C/C++ inference project behind many local GGUF model workflows.Qwen3 8B8B open-weight model. Good for: coding, chat.Phi-414B open-weight model. Good for: coding, reasoning.Qwen3 14B14B open-weight model. Good for: coding, chat.Local Coding Assistant StackOpen-source AI stack recipePrivate Local Chatbot StackOpen-source AI stack recipe

Check which models fit your RTX 4080

Check model fitVRAM reference guide