RX 9070 XT vs. RTX 4080 Super: Which GPU Is Better for Local LLMs in 2026?

Walmart just flooded the market with RTX 40-series cards this week. As of March 19, the PNY RTX 4080 Super dropped from $1,501 to $1,019 — the first time this card has been anywhere near reasonable in over a year. Meanwhile the RX 9070 XT has been sitting at $729–799 retail since it launched.

For gamers, that Walmart sale is noise. But for anyone running local LLMs, it puts two very different 16GB cards into the same buying conversation. Both fit most 14B models in VRAM. Both handle quantized 30B with a bit of squeeze. And both will cost you somewhere in the $800–1,100 range depending on which sale you catch this weekend.

So which one actually runs LLMs better?

The answer isn't what most comparison articles will tell you.

See also: FSR 4.1 vs DLSS 4.5: Gaming + AI →

The Spec That Actually Matters

Clock speeds, shader counts, synthetic benchmarks — none of that determines how fast your models run tokens. LLM generation is almost entirely memory bandwidth bound. The GPU loads model weights from VRAM to compute cores over and over, billions of times per inference call. The faster that data moves, the faster you get tokens.

RTX 4080 Super

16GB GDDR6X

736.3 GB/s

52.22 TFLOPS

320W

Ada Lovelace

~$1,019 (Walmart sale) The RTX 4080 Super has a 14.2% memory bandwidth advantage. And in LLM benchmarks, that gap shows up almost perfectly. On a 14B model (Q4 quantized, Ollama), the RTX 4080 Super deal generates around 60 tokens/s. The RX 9070 XT lands at 47–49 tokens/s on the same workload. That's roughly 20% slower — close enough to the bandwidth delta that you can basically predict AMD's inference speed from the spec sheet alone.

For smaller models the gap compresses. The RX 9070 XT delivers ~163 tokens/s on a 1.5B Llama model vs the 4080 Super's ~247 tokens/s on the same. Still a delta, but at those speeds both cards feel instant.

The Windows Problem Nobody Talks About

Here's where the comparison gets interesting — and where most gaming-focused articles completely miss the point.

CUDA just works. You install Ollama on Windows, it finds your RTX 4080 Super in about three seconds, and you're running tokens. LM Studio, llama.cpp, vLLM, any Python inference stack — they all treat Nvidia cards as first-class citizens. There's no configuration, no patching, no forum threads.

AMD's ROCm on Windows is a different story.

As of January 2026, there's an open GitHub issue in the Ollama repo where ROCm simply fails to initialize on the RX 9070 XT in Windows 11 — the device gets detected, then immediately filtered out with "filtering device which didn't fully initialize," and Ollama falls back to CPU. Users with the related Radeon AI PRO R9700 (same RDNA4 gfx1201 chip) are hitting identical failures with HIP SDK 7.1 installed.

The Vulkan path is actually fascinating though. Someone benchmarked Ollama 0.15.1 on an RX 9070 XT using Vulkan vs ROCm on Linux:

Power Draw

68W

149W Vulkan was 8.9% faster and drew 54% less power. That's not a typo. RDNA4's implementation of Vulkan compute is genuinely better for this workload than its ROCm path right now. If you're on Linux with the RX 9070 XT, use Vulkan for Ollama.

But none of that helps the Windows user who just wants to ollama run qwen3:14b and get on with their life.

What Models You Can Actually Run

Both cards share the same 16GB ceiling, so the model shortlist is identical on paper. In practice:

Runs cleanly (100% in VRAM, Q4_K_M):

• Llama 3.1 8B — fits in ~5GB, screams on both cards
• Qwen3 14B — ~12GB, comfortable, this is the sweet spot for 16GB
• GPT-OSS 20B — ~12–13GB compressed (MXFP4), fits with headroom
• Ministral 3:14B — ~13GB

Tight fit / partial offload territory:

• Qwen3-Coder 30B — ~20GB, needs ~25% CPU offload, drops to 50–57 t/s
• Anything 33B Q4 — borderline, context length will push you off the cliff

Don't bother at 16GB:

• Qwen3 70B, Llama 3.1 70B — these need 40GB+ for comfortable inference
• GPT-OSS 120B — requires offloading to RAM; you'll get 12 tokens/s if you have 64GB of system RAM and patience

Gaming + LLM Dual-Use Verdict

The 9070 XT is the better gaming card, full stop. It beats the RTX 4080 Super in 3DMark Time Spy (30,445 vs 28,304), runs cooler, and costs $200–300 less right now. If your primary use case is gaming and you want to occasionally run some local LLMs, the 9070 XT on Linux is a genuinely strong option.

But the question was which is better for local LLMs, and that answer is the RTX 4080 Super — for two reasons:

One: 736 GB/s vs 644 GB/s is a real, measurable gap. On a 14B model you're looking at 60 t/s vs 48 t/s. That's 4–5 extra tokens per second, which doesn't sound like much until you're watching it at interactive speed. The 4080 Super feels noticeably snappier at the output rates that actually matter for reading responses.

Two: CUDA support is categorical, not marginal. Every inference framework, every tutorial, every quantization tool assumes CUDA. AMD has closed a lot of ground in 2025 with ROCm, and on Linux with Vulkan the 9070 XT is a legitimate LLM card. But if you're on Windows — which most people are — the RTX 4080 Super eliminates an entire class of configuration problems that will cost you hours.

The Walmart sale this weekend brings the 4080 Super to its most competitive price point ever. Whether it stays there after the initial flood clears is a fair question. But at ~$1,019 vs ~$750 for the 9070 XT, you're paying roughly $270 for faster inference, zero driver drama, and access to the full CUDA ecosystem. For someone treating this as a serious local AI workstation, that's a defensible premium.

If you're on Linux and price-conscious, the 9070 XT + Vulkan backend is a legitimate path. You accept the ~15–20% inference slowdown. You do some extra setup. But you get better gaming performance and meaningful savings.

On Windows? Get the RTX 4080 Super while Walmart still has them.