Quick answer: NVIDIA wins for local LLM inference in 2026. CUDA support is mature and universal across every major framework. AMD’s ROCm has improved significantly, but still has compatibility gaps, slower inference, and a harder setup path.
Check NVIDIA GeForce RTX 4090 on Amazon→Buy on Shopee SG→The software stack decides everything
Hardware specs only matter if the software can use them. Here’s the real-world state of each ecosystem:
| Feature | NVIDIA (CUDA) | AMD (ROCm) |
|---|---|---|
| Ollama support | Native, plug-and-play | Supported, occasional issues |
| llama.cpp backend | cuBLAS (fastest) | ROCm/hipBLAS + Vulkan fallback |
| vLLM / TGI | Full support | Partial, limited GPU list |
| ExLlamaV2 | Full support | Not supported |
| PyTorch | First-class | Supported, some ops missing |
| Setup difficulty | Install driver, done | Driver + ROCm stack + env tweaks |
| Community help | Massive | Growing but smaller |
The pattern is clear: every LLM tool works on NVIDIA first. AMD support comes later, if at all.
One thing worth checking before you buy rather than after: AMD publishes the exact list of consumer cards ROCm supports, in its Linux system requirements. The RX 7700 XT, 7800 XT, 7900 GRE/XT/XTX and the RX 9060 XT and 9070/9070 XT are all on it. Cards that are not on it are not a matter of tweaking environment variables — they simply are not targets, and that is the single most common way people end up with an AMD card they cannot use for inference.
Inference speed comparison
Tokens per second running Llama 3 8B (Q4_K_M) under Ollama, modelled from each card’s memory bandwidth with a per-vendor efficiency factor rather than measured on a bench — see our methodology for how and why:
| GPU | VRAM | Bandwidth | Tok/s | Price |
|---|---|---|---|---|
| RTX 5090 | 32GB GDDR7 | 1,792 GB/s | ~105 tok/s | ~$4,900 |
| RTX 4090 | 24GB GDDR6X | 1,008 GB/s | ~65 tok/s | ~$2,200 |
| RX 7900 XTX | 24GB GDDR6 | 960 GB/s | ~40 tok/s | ~$900 |
| RTX 5080 | 16GB GDDR7 | 960 GB/s | ~55 tok/s | ~$1,400 |
| RX 9070 XT | 16GB GDDR6 | 650 GB/s | ~30 tok/s | ~$550 |
| RTX 4070 Ti Super | 16GB GDDR6X | 672 GB/s | ~40 tok/s | ~$800 |
| RX 7800 XT | 16GB GDDR6 | 624 GB/s | ~25 tok/s | ~$450 |
AMD cards consistently underperform their bandwidth specs by 20-40% versus NVIDIA equivalents. This is a software optimization gap, not a hardware limitation.
Where AMD actually makes sense
AMD is not a bad choice in every scenario:
- Budget VRAM, with a caveat: The RX 7900 XTX offers 24GB for ~$900, well under the RTX 4090 at ~$2,200. Against new cards that is real value. Against the used market it no longer is — a used RTX 3090 runs about $820 for the same 24GB with CUDA that simply works. See the resale note under common mistakes below before you commit to this one.
- Linux power users: If you’re comfortable with ROCm troubleshooting on Linux, AMD works fine for Ollama and llama.cpp daily use.
- Vulkan backend: llama.cpp’s Vulkan backend works on AMD without ROCm. Performance is lower, but setup is simpler. Intel Arc is a third option worth considering for budget builds — see Intel Arc B580 for LLM for a practical assessment of its 12GB GDDR6 advantage.
Where AMD does not work well:
- Windows LLM use — ROCm on Windows is a separate, narrower product. AMD ships it as the HIP SDK with its own compatibility list, which does not match the Linux one card for card. Check your specific model against the Windows page, not the Linux one
- Production serving — vLLM and TGI have incomplete AMD support
- Cutting-edge models — new architectures often need NVIDIA-first code paths
CUDA vs ROCm: the real gap
CUDA has a 15+ year head start. This shows in:
- Kernel optimization — CUDA kernels for attention, quantization, and sampling are highly tuned
- Flash Attention — critical for long-context inference, ROCm support lags
- Quantization libraries — GPTQ, AWQ, and EXL2 all target CUDA first
- Debugging tools — CUDA profiling and debugging is far more mature
ROCm is improving. AMD’s investment in the MI300X for data centers is driving better software. But consumer GPU support remains secondary to their data center push.
Recommendation by budget
| Budget | Best NVIDIA | Best AMD | Our Pick |
|---|---|---|---|
| ~$425-550 | RTX 4060 Ti 16GB | RX 9070 XT 16GB | NVIDIA (ease of use) |
| ~$800-900 | RTX 4070 Ti Super 16GB | RX 7900 XTX 24GB | Used RTX 3090 beats both |
| ~$1,400 | RTX 5080 16GB | — | NVIDIA |
| ~$2,200+ | RTX 4090 24GB | — | NVIDIA |
| ~$4,900+ | RTX 5090 32GB | — | NVIDIA |
AMD’s one genuinely competitive slot used to be the $700-800 band, where the RX 7900 XTX’s 24GB beat anything NVIDIA sold new at that price. The 2026 price moves closed it. The 7900 XTX now sits near $900 while a used RTX 3090 goes for roughly $820 — same 24GB, same model-size headroom, and CUDA instead of ROCm. Unless you specifically need a new card with a warranty, the argument that carried this recommendation no longer holds.
Check NVIDIA GeForce RTX 4090 on Amazon→Buy on Shopee SG→ Check NVIDIA GeForce RTX 5090 on Amazon→Buy on Shopee SG→ Check NVIDIA GeForce RTX 4070 Ti Super on Amazon→Buy on Shopee SG→When to buy NVIDIA vs When to buy AMD
Buy NVIDIA if you want plug-and-play LLM inference, use Windows, need support for vLLM/TGI serving, or want access to the widest range of quantization formats (GPTQ, AWQ, EXL2). NVIDIA is the default choice for anyone who values time over money.
Buy AMD only if you want 24GB on a new card with a warranty (RX 7900 XTX, ~$900), run Linux exclusively, and are comfortable troubleshooting ROCm driver issues. If buying used is acceptable, the RTX 3090 undercuts it on price and wins on software. If you are still deciding which OS to use for your LLM setup, our Windows vs Linux for local LLM guide covers the real-world differences in driver support, performance, and tool compatibility. The VRAM-per-dollar value is real, but so is the extra setup friction.
Common mistakes when choosing NVIDIA vs AMD
Assuming specs equal performance — An AMD card with matching bandwidth and VRAM will still run 20-40% slower on LLM tasks due to software optimization gaps. Always check real-world benchmarks, not spec sheets.
Assuming AMD is still the cheap way to 24GB — It was, and the habit has outlived the prices. At ~$900 the RX 7900 XTX costs more than the used RTX 3090 it competes with, so you are paying a premium for the ROCm troubleshooting rather than being compensated for it. Check current prices on both before assuming the savings exist.
Thinking ROCm will catch up soon — People have been saying this for years. AMD is investing heavily, but the consumer GPU software gap has narrowed slowly. Buy for today’s ecosystem, not promises.
Ignoring the resale market — A used NVIDIA RTX 3090 at ~$820 gives you 24GB with CUDA support that needs no troubleshooting, and it now costs less than a new RX 7900 XTX at ~$900. This is the single biggest reason the AMD case has weakened in 2026: AMD did not get worse, the used 3090 got cheaper than the new AMD card it competes with.
Bottom line
Buy NVIDIA unless you have a specific reason not to. The software ecosystem gap is the deciding factor — not clock speeds, not bandwidth, not TFLOPS. When every tool, every tutorial, and every troubleshooting guide assumes CUDA, going AMD means signing up for extra friction on every step.
If you want 24GB on a new card and are willing to work for it, the RX 7900 XTX is the one AMD card worth considering for LLM work — though at 2026 prices a used RTX 3090 undercuts it. For everything else, check our best GPU for Ollama guide.
The best GPU ecosystem for LLMs is the one where “install and run” actually works. In 2026, that’s still NVIDIA CUDA.
Frequently asked questions
Can AMD GPUs run local LLMs in 2026?
Yes. Ollama and llama.cpp both run on AMD through ROCm, and llama.cpp also has a Vulkan backend that needs no ROCm at all. The question is not whether it works but what it costs you to get there — expect driver and environment work on Linux, and check AMD’s own supported-card list before buying rather than after.
Is the RX 7900 XTX still the cheap route to 24GB?
It was, and the habit has outlived the prices. At around $900 it now costs more than a used RTX 3090 at roughly $820, which gives you the same 24GB with CUDA and no troubleshooting. Buy the AMD card if you specifically want a new card with a warranty; buy it for cheap VRAM and you are paying more for more work.
How much slower is AMD than NVIDIA for inference?
On paper less than people expect, in practice more. The RX 7900 XTX pushes 960 GB/s, between a used RTX 3090 at 936 and an RTX 4090 at 1008, so on anything that fits in its 24GB — a 34B at Q4 is about 20GB — it is in the same conversation. A 70B at Q4_K_M is 43GB and fits none of the three on one card. What costs you is everything around it — Flash Attention support lags, some quantization kernels are CUDA-first, and a working setup is more fragile.
Does ROCm work on Windows?
Not in the way CUDA does. AMD ships a narrower Windows product as the HIP SDK, and the local-LLM tooling people actually use assumes Linux. If you run Windows and want AMD, the realistic path is llama.cpp’s Vulkan backend, which is simpler to set up and slower.
Will ROCm catch up with CUDA?
People have been predicting it for years and the consumer gap has narrowed slowly. AMD’s real investment is in data-centre parts, and consumer GPU software follows behind that rather than leading it. Buy for what works today, not for a roadmap.