Every open model in our directory, ranked by how well it runs on Hailo-8L M.2 AI Accelerator Module (GB) at 4-bit. Grades weigh whether the model fits in memory and how fast it should run.
Hailo-8L M.2 AI Accelerator Module has 0.0 GB of VRAM. The ranked list below shows every model it can run.
See full Hailo-8L M.2 AI Accelerator Module specs and pricingThe top models this device can run at 4-bit, ranked by fit and speed.
Specs not available for scoring. This product is missing VRAM or memory bandwidth data.
Specs not available for scoring. This product is missing VRAM or memory bandwidth data.
Model Compatibility
Use the calculator to weigh this device against any other, model by model, with speed and fit scores.
Straight answers to the questions we hear most often.
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We size each model at 4-bit (Q4_K_M), add the KV cache for its context length and a fixed runtime overhead, then compare that to this device VRAM. Each model is graded on how comfortably it fits and how fast it should run on this card.
It is an estimate of decode speed in tokens per second at 4-bit, based on this device memory bandwidth and each model size. Real speed depends on your runtime, batch size, and context length, so use it to compare models, not as a hard promise.
Lower-bit quantization shrinks a model so more of them fit. These grades already assume 4-bit, which is the common local default. Going below 4-bit can squeeze a larger model in at some quality cost; going above needs more VRAM and may not fit.
Mixture-of-experts models only activate a fraction of their parameters at a time, so their runtime memory is closer to the active size than the headline size. We score fit on the active parameters, which is why some very large models still fit.
