Hardware · NVIDIA Ada Lovelace

Reviewed October 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 buys comfort and reach rather than full precision: 8B models at Q8 and 14B models at Q4_K_M run with real headroom, and 12B at Q8 and 24B at Q4 newly fit, tightly. FP16 on current 7B and 8B models, 14B at Q8, and dense 30B models still need 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 byCJ ZwartLast updatedOctober 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 16GB card you buy for comfort and reach: roomy 8B at Q8 and 14B at Q4_K_M, plus a tight 12B at Q8 or 24B at Q4. Full-precision 7B and 8B, 14B at Q8 and dense 30B models still belong to 24GB.

  • 7B–8BQ8 with room for long context (8.89 GB measured for Qwen3 8B); FP16 does not fit
  • 14BQ4_K_M with real headroom; Q8 needs about 17 GB and does not fit
  • 12B–24BNewly reachable: 12B at Q8 and 22B–24B at Q4, both tight
  • 30B–32BDoes not fit. CPU offload only: dense models at 1–5 t/s
  • 4080 vs SuperSame 16GB ceiling; under 5% speed difference

Good fit for

  • Daily chat and coding on 8B models at Q8, with room for long context
  • 14B models at Q4_K_M with headroom that 10GB and 12GB cards lack
  • Trying 24B-class models such as Mistral Small 3.1 at Q4, tightly

Wrong fit for

  • Full-precision 7B or 8B, or 14B at Q8: both need about 17 GB, so plan for 24GB
  • Dense 30B+ models: Q4 needs 18–20 GB, so the 24GB tier is the right move
  • Long (16K+) context on the tight fits: the KV cache pushes past 16GB

The RTX 4080 and 4080 Super are the fastest 16GB cards of NVIDIA's Ada generation, and what 16GB buys over 12GB is room, not a new precision tier. 8B models at Q8 stop being a near-limit fit, 14B models at Q4_K_M gain real context headroom, and a few models that do not fit on 12GB at all now load: 12B at Q8 and 22B–24B at Q4, both with little room to spare.

What 16GB does not open is full precision or the 30B class. Current 7B and 8B models are about 16 GB of weights at FP16 and 14B models are 15–16 GB at Q8, so both need about 17 GB once runtime overhead is added. A 30B model at Q4 needs 18–20GB. If any of those is your target, the jump to 24GB (RTX 3090 or RTX 4090) is the right move. If fast 8B to 14B models with room for context are your target, the 4080 tier is the right card.

Check 16GB model fit in the checker →

What this hardware can run

The 16GB budget gives 8B at Q8 and 14B at Q4_K_M real headroom and newly fits 12B at Q8 and 22B–24B at Q4, tightly. Sizes are the checker's weight figures; the checker adds 1.5 GB of runtime overhead and calls a fit comfortable with 2 GB to spare.

Model sizeBest quantizationVRAM usedVerdictNotes
1B–4BFP162–8 GBComfortableFull precision on all small models.
7B–8BQ88.89 GB (Qwen3 8B, measured on an RTX 3080)ComfortableRoom for long context, where 12GB cards are tight. FP16 on current 7B and 8B models (~16 GB of weights plus overhead) does not fit; Q8 is very close to FP16 quality.
9BQ8~11 GBComfortableQwen 3.5 9B. Too large for 12GB cards at Q8; fits here with room for context.
12BQ8~13 GBFits (tight)Gemma 3 12B. Does not fit at Q8 on 12GB cards. Keep context modest.
14BQ4_K_M~9 GBComfortableQwen3 14B, Phi-4, Qwen 2.5 Coder 14B. Real context headroom, where 12GB cards are tight. Q8 (15–16 GB of weights) does not fit.
20B (mixture-of-experts)Q4~12.5 GBComfortablegpt-oss 20B. Does not fit on 12GB cards.
22B–24BQ4_K_M~14–14.4 GBFits (tight)Mistral Small 3.1, Devstral, Codestral. Little room left for context.
30B–32BQ4~18–20 GBCPU offload onlyExceeds 16GB. Possible via RAM offload; a dense model runs at 1–5 t/s and needs 24GB+ for usable speed. Mixture-of-experts models are the exception: measured on an RTX 3080 10GB, they stayed usable with more than half of the model in system RAM.
70BQ4~38–42 GBNot recommendedFar exceeds 16GB. Needs 48GB+ VRAM or cloud.

Best model sizes for this card

  • 8B at Q8: The daily driver at this tier. Q8 is very close to FP16 quality, and at 8.89 GB (Qwen3 8B, measured on an RTX 3080) it leaves room for long chat and coding sessions. Full FP16 on current 8B models does not fit in 16GB.
  • 14B at Q4_K_M: More depth for coding and reasoning, with the context headroom 12GB cards lack. Qwen3 14B, Phi-4 and Qwen 2.5 Coder 14B are about 9 GB of weights at Q4_K_M. Q8 on these models needs about 17 GB and does not fit.
  • 12B at Q8 or 24B at Q4: The reach the extra 4GB buys. Gemma 3 12B at Q8 (~13 GB) and Mistral Small 3.1 24B at Q4_K_M (~14.4 GB) both load, with little room for long context.
  • 4B at FP16: Very fast full-precision inference. Good for tools and agents where speed matters more than depth.

Recommended models

  • Qwen3 8B at Q8: ollama pull qwen3:8b-q8_0. Near-FP16 quality with room for context. The recommended daily driver at the 16GB tier.
  • Qwen3 14B: ollama pull qwen3:14b (Q4_K_M, about 9.3 GB). Strong coding and reasoning at 14B depth, with real headroom on 16GB.
  • Phi-4 Mini: small and fast, about 2.6 GB at Q4 or 7.5 GB at FP16, and both fit here with room to spare. Good for fast iteration alongside a larger model.
  • Mistral Small 3.1 24B: ollama pull mistral-small3.1:24b (Q4_K_M, about 14.4 GB). The largest class that fits. Tight, so keep context modest.

Getting started: first setup on the RTX 4080

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

# The 16GB daily driver: 8B at Q8, near-FP16 quality with room for context
ollama pull qwen3:8b-q8_0
ollama run qwen3:8b-q8_0

# More depth: 14B at Q4_K_M, with headroom 12GB cards lack
ollama pull qwen3:14b
ollama run qwen3:14b

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

Recommended runtimes

  • Ollama: easiest start. Use a tag suffix such as :q8_0 to pick a quantization.
  • LM Studio: desktop GUI with a built-in model browser. Good for comparing Q4 and 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: 8B at Q8 for fast, near-full-precision responses with room for long conversations.
  • Coding assistance: 14B at Q4_K_M, such as Qwen 2.5 Coder 14B, for stronger code generation with context headroom. Devstral 24B at Q4 fits too, tightly.
  • Local RAG: 14B at Q4_K_M with a vector database. The headroom matters here, because retrieved passages fill the context window.
  • Agent workflows: 8B at Q8 or a 4B model for fast tool-call loops. Speed matters in agentic chains, and the 4080's bandwidth keeps latency per step low.

What this hardware cannot do well

  • Dense 30B+ models: Q4 on 30B needs 18–20GB. Exceeds 16GB. CPU offload works, but a dense model runs at 1–5 t/s. The jump to 24GB is required. Mixture-of-experts models are the exception: measured on an RTX 3080 10GB, they stayed usable with more than half of the model in system RAM. See the runs and result files.
  • 7B or 8B at FP16: Current 7B and 8B models are about 16 GB of weights at FP16, about 17.5 GB with runtime overhead, which exceeds 16GB. Only a few older, slightly smaller 7B models (about 13.5 to 14.5 GB) fit, tightly. Q8 is the practical quality ceiling for 8B models on this card.
  • 14B at Q8: 15 to 16 GB of weights, about 17 GB with overhead. Q4_K_M is the practical quantization for 14B models at 16GB.
  • Long context on the tight fits: 12B at Q8 and 22B–24B at Q4 leave little room, and KV cache growth at long context (16K+) can push past 16GB.

Upgrade path

  • RTX 3090 (24GB, used): The natural next step. 24GB opens 30B at Q4, 8B at FP16 and 14B at Q8. 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 most daily local AI workflows up to the 14B class. 30B models at Q4 do not fit in its 16 GB, so cloud GPU is useful for 30B and 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 and 8B models at Q8 move from a tight fit to a comfortable one, 14B models at Q4_K_M gain real headroom for context, and models that do not fit on 12GB at all now load, such as Gemma 3 12B at Q8 and Mistral Small 3.1 24B at Q4_K_M (both tightly). What 16GB does not unlock: 7B and 8B at FP16 (about 16 GB of weights plus runtime overhead) and 14B at Q8 (15 to 16 GB of weights, about 17 GB in total). Both start at 24GB. The 30B ceiling does not change either: a dense 30B model still does not fit 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 for a dense model it reduces generation to 1–5 tokens per second. Mixture-of-experts models are the exception: measured on an RTX 3080 10GB, they stayed usable with more than half of the model in system RAM. For dense 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 needs about 16 GB for weights plus about 1.5 GB of runtime overhead, roughly 17.5 GB in total, which exceeds 16GB. Current 7B models such as Qwen 2.5 7B are the same size at FP16 and do not fit either. The best you can do for 8B models on a 16GB card is Q8 (8.89 GB for Qwen3 8B, measured on an RTX 3080), which is very close to FP16 quality in practice. A few older, slightly smaller 7B models, about 13.5 to 14.5 GB at FP16, fit with little room to spare.

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: October 2026

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Check which models fit your RTX 4080

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