NVIDIA GeForce RTX 4080 Super
Refreshed 4080 with 16GB GDDR6X. Slightly behind 5080 but well-supported.
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Sub-scores sum to 618 / 1000. Headline = 618 × 0.70 (Estimated-confidence discount) = 433. This is an algorithmic performance-tier score — distinct from, and often lower than, the editorial “Our verdict” below, which weighs value and real-world fit (especially for hardware we haven’t measured yet). How scoring works →
Extrapolated from 736 GB/s bandwidth — 88.3 tok/s estimated. No measured benchmarks yet.
Plain-English: Comfortable at 14B and below — snappy enough for a coding agent; vision models supported.
Verdicts extrapolated from catalog VRAM + bandwidth + ecosystem flags. Hover any chip for the rationale. Want measured numbers? Submit your own run with runlocalai-bench --submit.
What it does well
The RTX 4080 Super delivers 14B-class models at top-tier speeds. Full GPU offload of Qwen 3 14B / Phi-4 14B / Qwen 2.5 14B with 32K context, 60–80 tok/s. CUDA universal support. Memory bandwidth at 736 GB/s is more than enough for the model class it can fit.
Where it breaks
- 16 GB VRAM is the hard ceiling — 32B-class models partial-offload at Q4 (19+ GB), making the 4090 dramatically more useful for "serious local AI."
- Beaten by used RTX 3090 on $/VRAM by a wide margin if you can find a clean unit.
- Awkward price tier — the gap to a new 4090 isn't large enough to justify the VRAM cap for most local-AI buyers.
Ideal model range
- Sweet spot: Qwen 3 14B / Phi-4 14B / Qwen 2.5 14B at Q4 — full GPU, 60–80 tok/s, 32K context.
- Stretch: 24B-class (Mistral Small 3 24B) at Q4 — fits with 16K context.
- Comfortable: 7–8B at full 128K context, or as a fast routing model in agent stacks.
Bad use cases
- 32B-class anything — you'll partial-offload, losing the speed advantage that justified buying NVIDIA.
- Long-context 14B workloads — 32K context with KV cache eats into your VRAM budget.
- Coder workflows wanting Qwen 2.5 Coder 32B — partial-offload kills autocomplete latency.
Verdict
Buy this if 14B-class models cover your work, you specifically want CUDA + driver maturity, and the price difference vs RTX 4090 is meaningful in your budget. Skip this if you can stretch to a 4090, find a used 3090 (same 24 GB VRAM, cheaper), or want to wait for RTX 5080 (16 GB, but newer architecture).
How it compares
- vs RTX 4090 → 4090 has 50% more VRAM, opens 32B-class. Worth the premium for serious local AI.
- vs RTX 3090 (used) → 3090 has the same 24 GB at materially lower used pricing — 4080 Super loses on $/VRAM badly.
- vs RTX 5080 → 5080 is the architectural successor at similar 16 GB VRAM; pick 5080 if available.
- vs RX 7900 XTX (24 GB) → AMD has more VRAM at lower price, NVIDIA has better software. 4080 Super's 16 GB cap is the deciding factor against AMD here.
›Why this rating
7.2/10 — solid mid-flagship for local AI but the 16 GB VRAM caps you at 14B-class full-GPU, and the price gap to a 4090 (or used 3090) often doesn't justify the position. Loses points specifically on VRAM-per-dollar.
Overview
Refreshed 4080 with 16GB GDDR6X. Slightly behind 5080 but well-supported.
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Specs
| VRAM | 16 GB |
| Power draw (peak) | 320 W |
| Released | 2024 |
| MSRP | $999 |
| Backends | CUDA Vulkan |
Models that fit
Open-weight models small enough to run on NVIDIA GeForce RTX 4080 Super with usable context.
Frequently asked
What models can NVIDIA GeForce RTX 4080 Super run?
Does NVIDIA GeForce RTX 4080 Super support CUDA?
How much does NVIDIA GeForce RTX 4080 Super cost?
Where next?
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify hardware specifications.