Guide · 2026-08-30

Which LLM Should You Run on an RTX 4080 or 4080 Super?

Last updated 2026-08-30

Both 16GB cards handle 9B-14B models easily and stretch to 20B at Q4 - here's what to run, and whether the Super is worth the extra cost.

By Mohamed Meguedmi · 8 min read

RTX 4080 and 4080 Super: what you're actually buying in 2026

Both cards are built on the same Ada Lovelace AD103 die, manufactured on TSMC's 4N process, and both top out at 16GB of GDDR6X. That shared ceiling is the number that matters most for local LLM work, because it - not the core count - decides which model sizes and quantization levels you can load without spilling into system RAM. The Super's advantage on paper is real but small: about 5% more CUDA cores (10,240 vs 9,728) and roughly 3% more memory bandwidth (736 GB/s vs 716 GB/s), driven by faster 23 Gbps GDDR6X versus the standard card's 22.4 Gbps. On inference workloads, that gap shows up as roughly 3-5% more tokens per second - a difference you'd need a stopwatch to notice, let alone feel in daily use.

Memory bandwidth matters more than raw CUDA core count for LLM inference because token generation is largely memory-bound: the GPU has to stream the entire model's weights through memory for every token it produces. That's why a card with modest compute but generous bandwidth can still hold its own on tokens/sec, and why the 4080 and 4080 Super perform so similarly to each other despite the core-count gap - neither card is meaningfully bandwidth-starved relative to the other. Both cards carry a 320W TDP, and we'd recommend at least an 850W PSU once you account for a modern CPU and headroom for sustained boost clocks under load.

Spec	RTX 4080	RTX 4080 Super
Architecture	Ada Lovelace AD103 (TSMC 4N)	Ada Lovelace AD103 (TSMC 4N)
VRAM	16GB GDDR6X @ 22.4 Gbps	16GB GDDR6X @ 23 Gbps
Memory bandwidth	716 GB/s	736 GB/s
CUDA cores	9,728	10,240
TDP	320W	320W
Recommended PSU	850W	850W
Typical 2026 price	$1000-1200 used, good condition	~$1300 new, increasingly scarce

Our rule of thumb: don't pay more than a $100 premium for a Super over a standard 4080 on the used market. The performance delta doesn't come close to justifying a bigger gap, and past that point you're better off putting the difference toward a newer card entirely - more on that below.

Which LLMs actually fit in 16GB of VRAM

16GB is a genuinely useful tier in 2026 - it's the line where "runs great" turns into "runs, with compromises," and knowing exactly where that line falls saves a lot of trial and error with failed model loads. As a working guide for what fits on either card:

• 9B-14B dense models at Q8 - the sweet spot. Full context windows, minimal quality loss versus full precision, and enough VRAM headroom left over for a long context window or a small embedding model running alongside.
• 14B models at Q4 - still comfortable, and the quantization loss is minor enough that most people won't notice it in day-to-day use like coding assistance, summarization, or chat.
• 20B-class mixture-of-experts models at Q4 - models like gpt-oss-20B fit, but you'll want to keep context length modest and close other GPU-heavy applications first, since MoE models carry more total parameters in memory even though only a fraction activate per token.
• 30B+ dense models - technically loadable at aggressive Q3/Q4 quantization, but you're trading real output quality for the privilege, and context length gets squeezed hard. This is the point where a second GPU or a 24GB card, like a used RTX 3090, starts to make more sense.

One thing that trips up a lot of people moving from cloud APIs to local inference: your context window eats into the same VRAM budget as the model weights. A 14B model at Q4 might load in comfortably, but push the context to 32K tokens and the KV cache can claw back several gigabytes, forcing you back down to a smaller quant or a shorter context. Check a model's actual VRAM footprint at your target context length in our full model catalog before you download a multi-gigabyte GGUF file that won't load.

Getting the 4080 running well for inference

Neither card needs anything exotic to run local models well, but a few settings matter more than people expect:

• Drivers - keep NVIDIA's Studio or Game Ready driver current. CUDA and cuDNN performance for inference backends like llama.cpp, Ollama, and vLLM improves meaningfully release over release, and flash-attention kernels in particular have seen steady gains on Ada Lovelace hardware.
• GPU offload layers - with a 16GB card, you'll often be one or two layers away from a clean full-VRAM load. Most backends let you tune the number of offloaded layers manually; trimming context length by a few thousand tokens is usually a better trade than partial CPU offload, which tanks throughput.
• Power connectors - the 4080 and 4080 Super use a 16-pin 12VHPWR connector. If you're buying used, inspect it closely under bright light; this connector has a well-documented history of melting under sustained load when it isn't fully seated.
• Front end - Ollama and LM Studio are the fastest path to a working setup if you'd rather not compile llama.cpp yourself; text-generation-webui and vLLM give you more control over batching and sampling once you're comfortable with the basics.
• PSU headroom - 850W is our floor recommendation, not a ceiling. If you're pairing the card with a power-hungry CPU or plan to add a second GPU down the line, size up now rather than swapping the PSU later.

4080 vs 4080 Super vs the RTX 50-series: the 2026 upgrade math

Here's where the math gets interesting. A new RTX 5070 Ti, still carrying 16GB of VRAM, now runs around $950 with a full manufacturer warranty. Against a used 4080 Super, it delivers roughly 10% faster LLM throughput, plus support for FP4 quantization formats that NVIDIA's Blackwell architecture handles natively - a format that's picking up more model support as 2026 goes on, which matters if you want your hardware to stay useful for the next couple of model generations rather than just the current one.

That changes the calculus for buying used. A used 4080 only makes sense once it's priced at least $200 below a new 5070 Ti; otherwise you're paying nearly new-GPU money for older silicon, a shorter remaining lifespan, and someone else's warranty history. There's also a resale angle worth considering: Ada Lovelace cards will only get harder to move as Blackwell inventory fills out the used market over the next year, so a 4080 you buy today is a depreciating asset faster than it was twelve months ago.

Scenario	Better choice
Used 4080 at $900 or below	4080 - solid value, still capable at 16GB
Used 4080 Super at $1050 or below	4080 Super - small speed edge for a small premium
Used 4080/Super priced above those thresholds	New RTX 5070 Ti (~$950, warrantied) - more consistent value
Need FP4 support or planning to resell your 4080	Upgrade to 5080/5070 Ti now

Run the numbers on a specific listing against current new-card pricing with our GPU comparison tool before you commit - it's the fastest way to confirm whether a given used price actually clears the bar versus buying new.

The verdict: keep, buy, or upgrade?

• Keep your 4080 if: it's already running your workload well. 16GB is still a legitimately useful VRAM tier in 2026, and this isn't a priority upgrade.
• Buy a used 4080/Super if: you can land one at $900 or less for a standard 4080, or $1050 or less for a Super. Above those numbers, a new 5070 Ti is the better buy on price-to-performance alone.
• Upgrade to a 5080 or 5070 Ti if: you specifically need FP4 support for upcoming model formats, or you can resell your current 4080 for a good price and pocket most of the difference.

None of these paths are wrong - they're just tuned to different priorities. If you're optimizing for lowest total cost and your current setup works, the smartest move is often to do nothing. If you're buying your first serious local-inference GPU today, the new-card warranty and FP4 headroom on a 5070 Ti make it hard to pass up at its current price point.

Buying a used RTX 4080 in the US market

If you're shopping secondhand, eBay's sold listings and r/hardwareswap give you the most reliable read on real transaction prices; asking prices on Facebook Marketplace tend to run high and are worth discounting mentally before you factor them into a decision. Newegg and Best Buy occasionally carry open-box or manufacturer-refurbished 4080 Supers with a short warranty window, which is worth the modest premium over a private-party sale if you're not comfortable inspecting a card's VRM and 12VHPWR connector yourself. Whatever the source, ask the seller for a recent GPU-Z screenshot, insist on local pickup with a live stress test when possible, and use PayPal Goods & Services rather than a bank transfer or gift card for any remote purchase.

Once you've settled on a card, plug your full budget into our build configurator to see a complete, VRAM-matched parts list built around it. The same underlying spec and quantization data behind this guide is also available through our public API (CC BY 4.0) and open-source MCP server, if you'd rather query VRAM and fit data programmatically than read it off a page.