High Flyer is a hedge fund which was early adopter of GPU acceleration in finance. The expertise they built in that field helped them launch a subsidiary Deepseek which recently released R1 after two other LLMs. Everybody now knows what Deepseek is but not many know that it is actually not running in any cloud. It is all on-prem.
In China, you can't get H100, but you can get H800 which is BW limited and H20 which is crippled version but enough to train and infer 680B parameter model. Some suggest that Deepseek innovated due to constraints. It may be, but it could just as well be that they figured out first that one does not need to load the whole model into memory for inference. The latest figures are that DSR1 only load 30B parameters which you can load on a Laptop GPU with only 12GB of VRAM.
Since the new on DSR1 hit the wires, the consumer grade GPUs have been flying off the shelf. It is super easy to run DSR1 using ollama (literally just type %ollama run deepseek-r1). Most of the value, I derive from DSR1 is the bit inside. For example, I asked DSR1 how to compress and load a LLM into VRAM. The answer was obvious as shown below:
1. Start by quantizing the model parameters to lower bit precision (e.g., 8-bit or 16-bit).
2. Apply pruning to remove unnecessary weights after quantization.
3. Use techniques like dynamic or static quantization for further compression during inference.
4. Implement checkpointing strategies to optimize memory usage during the inference process.
5. Possibly combine with other methods like knowledge distillation if there's excess capacity.
