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Every GPU and Mac in our directory, ranked by how well it runs Liquid AI d1-2B at 4-bit. Each grade weighs whether the model fits in memory and how fast it runs, so you can see what to buy at a glance.
At 4-bit, Liquid AI d1-2B needs about 3.6 GB of VRAM. The ranked list below shows every device that can run it.
125 of 125 devices can run Liquid AI d1-2B at a usable speed.
See full Liquid AI d1-2B details, specs, and benchmarksThe top devices for this model at 4-bit, ranked by fit and speed.
| Device | Grade | VRAM |
|---|---|---|
| ACEMAGIC M1A Pro (i9-13900HK + ARC A770)ACEMAGIC | SS | 3.6 GB |
| Acer Veriton GN100 AI MiniAcer | SS | 3.6 GB |
| AI TOP ATOM ATAGB10-9003Gigabyte | SS | 3.6 GB |
| AMD Instinct MI300XAMD | SS | 3.6 GB |
| AMD Instinct MI325XAMD | SS | 3.6 GB |
| AMD Instinct MI350PAMD |
| SS |
| 3.6 GB |
| AMD Instinct MI355XAMD | SS | 3.6 GB |
| AMD Radeon RX 7600 8GBAMD | SS | 3.6 GB |
| AMD Radeon RX 7700 XTAMD | SS | 3.6 GB |
| AMD Radeon RX 7800 XTAMD | SS | 3.6 GB |
| AMD Radeon RX 7900 XTAMD | SS | 3.6 GB |
| AMD Radeon RX 7900 XTXAMD | SS | 3.6 GB |
| SS | 3.6 GB | ||
| SS | 3.6 GB | ||
AI TOP ATOM ATAGB10-9003Gigabyte | SS | 3.6 GB | |
| SS | 3.6 GB | ||
| SS | 3.6 GB | ||
| SS | 3.6 GB | ||
| SS | 3.6 GB | ||
| SS | 3.6 GB | ||
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| SS | 3.6 GB | ||
| SS | 3.6 GB | ||
| SS | 3.6 GB | ||
| SS | 3.6 GB | ||
| SS | 3.6 GB | ||
| SS | 3.6 GB | ||
| SS | 3.6 GB | ||
Apple M4Apple | SS | 3.6 GB | |
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| SS | 3.6 GB | ||
| SS | 3.6 GB | ||
Apple M5Apple | SS | 3.6 GB | |
| SS | 3.6 GB | ||
| SS | 3.6 GB | ||
| SS | 3.6 GB | ||
| SS | 3.6 GB | ||
| SS | 3.6 GB | ||
| AdTools for AI BuildersAll the hardware and tools you need to run private AI at your office or at home.Shop Now | |||
| SS | 3.6 GB | ||
Hardware Compatibility
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Run It Locally
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Straight answers to the questions we hear most often.
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We size the model at 4-bit (Q4_K_M), add the KV cache for its context length and a fixed runtime overhead, then compare that to each device VRAM. A device is graded on how comfortably the model fits and how fast it should run, based on the card memory bandwidth.
It is an estimate of decode speed in tokens per second at 4-bit, derived from the device memory bandwidth and the model size. Real speed varies with the runtime, batch size, and context length, so treat it as a ballpark for comparing devices, not a guarantee.
4-bit (Q4_K_M) is what most people actually run locally. It roughly quarters the memory a model needs versus 16-bit, with a small quality trade-off, so far more hardware can run the model. Each model page has a full quantization breakdown if you want other levels.
Higher-quality quants like Q5, Q6, or Q8 need more VRAM, so a device that is a tight fit at 4-bit may not fit at all. The model detail page shows the VRAM required at every quantization level so you can plan for the exact one you want.