Head-to-head comparisons
The match-ups people actually ask about, settled with the numbers that matter for local AI — not gaming benchmarks. Every GPU here has a full profile page; models live in the model directory.
RTX 4060 vs RTX 4070 for local AI
Two popular NVIDIA cards at different tiers. The 4060's 8 GB is the minimum comfortable VRAM for the 7B–8B class; the 4070's 12 GB opens the 14B class and gives real headroom for context.
| Option A | Option B | |
|---|---|---|
| VRAM | 8 GB | 12 GB |
| Memory bandwidth | ~272 GB/s | ~504 GB/s |
| 7B–8B at Q4 | Comfortable | Comfortable, faster |
| 13B–14B at Q4 | Offloads to RAM | Fits in VRAM |
| Verdict | Best value entry | The sweet spot for serious use |
If 8 GB already hosts the models you want, the 4060 saves money. If you plan to touch 13B+ models or long context, the 4070's extra 4 GB is the difference between smooth and offloaded.
RTX 4060 Laptop (8 GB) vs desktop RTX 4060
Same name, different chips. The laptop variant trades bandwidth for portability — capacity is similar, speed is not.
| Option A | Option B | |
|---|---|---|
| VRAM | 8 GB | 8 GB |
| Memory bandwidth | ~256 GB/s | ~272 GB/s |
| Sustained load | Thermal-limited, plug in | Full power always |
| 7B–8B Q4 speed | Good (20–35 tok/s est.) | Good to excellent |
| Verdict | You already own it — use it | Better if buying new |
A gaming laptop you already own is a perfectly capable local-AI machine — see our guide on laptop vs desktop GPUs before spending on a desktop card.
RTX 4090 vs RX 7900 XTX
The two 24 GB consumer kings. NVIDIA wins the ecosystem race; AMD wins the price-per-GB race.
| Option A | Option B | |
|---|---|---|
| VRAM | 24 GB | 24 GB |
| Memory bandwidth | ~1,008 GB/s | ~960 GB/s |
| Backend maturity | CUDA — everything works | ROCm/Vulkan — good, more setup |
| 32B Q4 | Yes, fast | Yes, slightly slower |
| Verdict | Zero-friction enthusiast pick | Value 24 GB for Linux users |
Both host the full 32B class. Choose by ecosystem tolerance: if you want software to just work, CUDA; if you run Linux and want the same VRAM for less, the XTX — details in our CUDA vs ROCm guide.
7B vs 8B vs 13B models
Model size classes, not specific brands. The jump from 7B to 13B is the largest single quality step you can buy with VRAM.
| Option A | Option B | |
|---|---|---|
| Q4 footprint | 7B: ~4.5 GB | 13B: ~8 GB |
| Runs on 8 GB VRAM | Yes, comfortable | Barely — offloads |
| Quality | Solid for chat, drafts | Clear step up in coherence |
| Speed on midrange GPU | Fast | Roughly half the speed |
| Verdict | The universal class | The quality tier, if VRAM allows |
Rule of thumb: run the largest model that fully fits your VRAM at 4-bit with ~2 GB to spare for context — then prefer the smaller model you can run entirely in VRAM over the bigger one that offloads.
Compare anything yourself
The checker grades the whole catalogue against any profile — enter one card, note the results, enter the other.