Fast-inference ASIC
A fast-inference ASIC is a custom chip designed specifically for LLM token generation — Groq LPU, Cerebras CS-3/CS-4, SambaNova RDU, Tenstorrent are 2026 examples delivering 5-20× the tokens/s of GPUs at lower latency.
GPUs were designed for graphics + general parallel compute; LLM inference exposes their inefficiencies (memory bandwidth bound on autoregressive decode). Fast-inference ASICs flip this: chip layout matches LLM access patterns — Groq LPU eliminates HBM with on-chip SRAM; Cerebras CS-3/CS-4 puts the entire model on a single wafer-scale chip; SambaNova RDU specializes in 'Composition of Experts'. Production benefits: 5-20× tokens/s, lower per-token cost on large open-weight models, ultra-low TTFT for voice agents. Trade-offs: smaller model menu than GPU clouds, vendor lock-in on hardware, less mature tooling. By 2026 fast-inference ASICs are mainstream for voice agents and high-throughput open-weight workloads; closed-weight frontier models (Claude, GPT) still run on GPUs.
When to use fast-inference asic
- Voice agents + real-time apps.
- High-throughput open-weight inference.
Common mistakes
- Picking ASIC for closed-weight models — they're only available on GPU clouds.
FAQ
What is fast-inference asic?
A fast-inference ASIC is a custom chip designed specifically for LLM token generation — Groq LPU, Cerebras CS-3/CS-4, SambaNova RDU, Tenstorrent are 2026 examples delivering 5-20× the tokens/s of GPUs at lower latency.
When should I use fast-inference asic?
Voice agents + real-time apps. High-throughput open-weight inference.
What are the most common mistakes with fast-inference asic?
Picking ASIC for closed-weight models — they're only available on GPU clouds.
Related terms
- LPU (Language Processing Unit) — An LPU is Groq's custom chip architecture for LLM inference — eliminates HBM memory bottleneck by keeping all weights in on-chip SRAM, delivers extreme tokens-per-second on supported models.
- Wafer-scale chip — A wafer-scale chip uses an entire silicon wafer as a single chip — Cerebras CS-3 (and CS-4 in 2026) is the only commercial wafer-scale inference chip, fitting LLMs entirely on one silicon die without inter-chip communication overhead.
- Throughput per dollar — Throughput per dollar is the production metric for LLM inference cost — tokens served per second of compute time per dollar of GPU cost — used to compare inference engines, serving platforms, and hardware in 2026.
Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/fast-inference-asic.md.