A functional playbook to lower cost-per-token with next-gen HBM– without surrendering throughput.
Cut cost-per-token on HBM 4 -course GPUs by modeling tokens/sec limits, enhancing KV bandwidth, and using batching, quantization, and caching methods.
You don’t buy memory data transfer for boasting civil liberties– you purchase it for less expensive tokens The method with HBM 4 is to transform that extra data transfer and capability into lower $/ token without quietly slowing the version down. Allow’s be actual: a slower however less expensive collection is still expensive if users really feel the lag. The goal is same (or far better) TTS/TPM , lower TCO per token
The one formula that keeps you honest
Cost-per-token is just TCO separated by tokens produced:
$/ token = (CapEx_amortized + Energy + Center + Misc)/ Total_tokens
- CapEx_amortized : hardware rate over beneficial life.
- Power : watts × hours × cost/kWh (consist of PUE).
- Total_tokens : tokens/sec × hours × usage.
If tokens/sec expands faster than your TCO line, the wins and $/ token drops HBM 4 helps since several LLM translate paths are memory-bandwidth bound , specifically with lengthy contexts …