Model optimization
Llama 3 compression
Self-hosted Llama 3 reduces API costs but the compute cost of processing long prompts remains. Compression cuts prompt size by 65%, reducing GPU memory and inference time.
Self-hosted economics
Llama 3 70B on an A100 GPU processes ~30 tokens/second. A 4,000-token prompt takes ~133 seconds just for prefill. Compression to 1,400 tokens reduces prefill to ~47 seconds. That is 86 more seconds of generation capacity per query.
Frequently asked questions
Does compression increase throughput?
Yes. By reducing prefill time by ~65%, you can serve more queries per GPU.
Does it reduce GPU memory usage?
Yes. Smaller prompts require less KV-cache memory, allowing larger batch sizes.
Build with less context
Put compression in front of your next LLM call.
Use the hosted API or run SuperCompress locally. Keep the evidence, drop the token waste, and measure the savings before it reaches your model.