Hardware Compatibility

Best GPUs to Run F2LLM-v2-0.6B Locally

Every GPU and Mac in our directory, ranked by how well it runs F2LLM-v2-0.6B 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, F2LLM-v2-0.6B needs about 0.8 GB of VRAM. The ranked list below shows every device that can run it.

102 of 102 devices can run F2LLM-v2-0.6B at a usable speed.

See full F2LLM-v2-0.6B details, specs, and benchmarks

Every Device, Ranked

The 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	0.8 GB
Acer Veriton GN100 AI MiniAcer	SS	0.8 GB
AMD Instinct MI300XAMD	SS	0.8 GB
AMD Instinct MI325XAMD	SS	0.8 GB
AMD Instinct MI355XAMD	SS	0.8 GB

Running This Model Locally: FAQ

How do you estimate whether a GPU can run this model?

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.

What does the speed number mean?

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.

Why 4-bit and not full precision?

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.

The grades assume 4-bit. What if I run a bigger quant?

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.