Walmart just dropped the RTX 4080 Super by $482. A card that was sitting at $1,501 last week is now $1,019 — specifically the PNY GeForce RTX 4080 Super, confirmed as of March 19, 2026.
This matters a lot if you've been sitting on the fence about building a local LLM rig. Not because the 4080 Super is the fastest GPU you can buy. It isn't. But because $1,019 for 16GB of GDDR6X is genuinely hard to beat right now, especially when the Blackwell alternatives are either unavailable, overpriced, or — and this is the part most reviews are getting wrong — actually worse for LLM work.
See also: RX 9070 XT vs RTX 4080 Super for local LLMs →
VRAM Is the Only Number That Matters
If you're running LLMs locally with Ollama or LM Studio, there is one spec that determines whether your setup is usable or frustrating: how many gigabytes of 16GB VRAM you have. Not clock speed. Not CUDA cores. Not generation number.
The reason is simple. When a model fits entirely in your GPU's VRAM, it runs fast — we're talking 70–140 tokens per second on a 16GB card with the right models. When it doesn't fit, your GPU starts offloading layers to system RAM and CPU, and speeds crater. Real-world benchmarks on RTX 4080 16GB hardware show that the GPT-OSS 20B model (which fits fully in VRAM) runs at 139.93 tokens/sec. A model that needs CPU offloading? 12.64 tokens/sec. That's an 11x difference on the same machine, just from running out of VRAM.
Note
The VRAM math for 16GB: A 7B model at Q4_K_M quantization needs roughly 4–5GB. A 13–14B model runs comfortably at 10–13GB. A 20B model can squeeze in at 14GB with some to spare. Once you push past that, you're hitting CPU territory and things slow down fast.
So when someone says "just get the RTX 5070, it's newer," the correct response is: the RTX 5070 has 12GB of VRAM. That's 4GB less than the 4080 Super — and that gap determines whether a 14B model fits in VRAM or gets partially offloaded. For gaming, sure, maybe that's fine. For local LLM inference? It's a real limitation.
What You Can Actually Run on 16GB
The 4080 Super's 16GB gives you a genuinely solid model library. Recent benchmarks on the hardware running Ollama in early 2026 show:
- Qwen3.5 9B — 90.89 tok/s, fits entirely in VRAM at 9.3GB
- Ministral 14B — 70.13 tok/s, uses about 13GB, stays 100% on GPU
- Qwen 3 14B — 61.85 tok/s, similar footprint
- GPT-OSS 20B — 139.93 tok/s when it fits in VRAM at 14GB (this one is surprisingly fast — the bandwidth advantage of fitting fully on-GPU is massive)
- Llama 3.1 8B — around 79 tok/s, snappy for coding and chat
That covers basically every use case where you'd want a local model. Coding assistant, document summarization, agentic workflows, private chat — all of it runs at a pace that doesn't feel like watching paint dry. 60+ tokens per second on a 14B model is genuinely usable. It feels like ChatGPT, not like waiting for a 2019 phone to load a website.
The 4080 Super hits around 736 GB/s of memory bandwidth. Not as fast as the 960 GB/s on the RTX 5080, but the 5080 costs nearly $1,700 right now. The bandwidth difference matters less than the raw fact that you have enough VRAM to run the model in full.
Tip
For daily driver local LLM use, aim for models in the 9B–14B range with Q4_K_M quantization. They fit cleanly in 16GB, run fast enough to feel responsive, and deliver noticeably better output than 7B models. Qwen3.5 9B and Ministral 14B are the two best options on 16GB hardware as of March 2026.
The RTX 5070 Problem Nobody's Talking About
The RTX 5070 launched at $549 MSRP — and if you could actually buy one at that price, it'd be a different conversation. But you can't, really. Stock is thin, prices have drifted above MSRP at most online retailers, and the supply situation isn't improving. Nvidia cut consumer GPU production by an estimated 30–40% in 2026 to redirect DRAM to AI data centers, which has been pushing Blackwell cards further out of reach.
But even setting aside availability, the 5070 is genuinely worse for local LLM inference than the 4080 Super. The 5070 has 12GB of GDDR7 — and yes, GDDR7 is faster per-pin than GDDR6X, but that speed advantage doesn't save you when a 14B model doesn't fit at all. The 4080 Super has 52.22 TFLOPS of FP32 compute versus the 5070's 30.84 TFLOPS. More CUDA cores, higher memory capacity, and more raw throughput — all for less than what you'd pay for the 5070 in the real market right now.
The Blackwell architecture does bring FP4 quantization support, which could theoretically let you squeeze larger models into 12GB. The catch: most local LLM runtimes — Ollama, llama.cpp, LM Studio — don't fully exploit FP4 yet. The software ecosystem is still built around INT4 and INT8. You'd be buying future potential that might take another year to materialize in the tools most people actually use.
Warning
Don't confuse MSRP with street price on Blackwell GPUs. The RTX 5070 Ti saw a 25% price surge by February 2026, hitting ~$1,000 average. The RTX 5080 lists at $999 MSRP but sells for $1,400–$1,700 in practice. The RTX 5090 is running $3,500+ with supply nowhere near demand. The "cheaper Blackwell" argument only works if you can actually buy the card at launch MSRP.
Why Walmart Dumping These Cards Is Significant
Walmart stocking up on RTX 40-series isn't some random warehouse sale. It's a direct consequence of the supply dynamics playing out in 2026: Blackwell cards are scarce, Ada Lovelace cards are abundant, and retailers need something to put on shelves. The $482 markdown on the 4080 Super is Walmart trying to move units while the getting is good.
For local LLM builders, the timing is almost comically well-aligned. Prices on 50-series are going up, not down. The A100 comparison is irrelevant for home builders. And 16GB VRAM at $1,019 — for Ada Lovelace with 736 GB/s bandwidth — represents the kind of value that doesn't show up often.
One thing worth noting: the 4080 Super Founders Edition at Walmart is out of stock as of this writing, but third-party AIB cards (PNY specifically) are where the $1,019 price lives. Worth checking Walmart directly versus Amazon and Newegg — the price gap has been significant enough to matter.
The deal may not last. The Walmart clearance of RTX 40-series is tied to 50-series supply normalizing, and if that happens, these prices will creep back up. "Time-sensitive" is an overused phrase in tech. This one actually earns it.
Bottom line: If you want to run 14B models locally at usable speeds, the RTX 4080 Super at $1,019 is the call. The RTX 5070 has less VRAM, the 5080 costs $700 more, and everything above that is Founders Edition lottery territory. Buy the card where the value actually lives.