NVIDIA GeForce RTX 4080 Super

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.