AI chip · Groq
Groq LPU (Language Processing Unit)
Deterministic-compilation inference accelerator optimized for low-latency LLM token generation; SRAM-based memory architecture.
Market position
The inference speed record-holder: Llama 70B at 500+ tokens/sec per user through 2024, an order of magnitude ahead of anyone else, by moving all weights into SRAM. Zero HBM, deterministic latency. Weakness: no training, and per-chip capacity is small so real deployments need dozens of racks.
Efficiency and power
How much work you get per watt and per dollar, and how much power a full rack draws.
- Perf per watt
- 0.50 FP16 TFLOPS/W188 TFLOPS ÷ 375 W = 0.50
What fits in 0.23 GB
Which open-source LLMs run on one Groq LPU (Language Processing Unit), by precision. Weights only: add roughly 15% headroom for real serving. If a model does not fit at FP16, try INT8 or INT4 (smaller quality trade-off than most people expect).
| Model | Params | FP16 | INT8 | INT4 |
|---|---|---|---|---|
| Llama 3.1 8B dense | 8 B | 16 GB ✗ | 8 GB ✗ | 4 GB ✗ |
| Llama 3.1 70B dense | 70 B | 140 GB ✗ | 70 GB ✗ | 35 GB ✗ |
| Llama 3.1 405B dense | 405 B | 810 GB ✗ | 405 GB ✗ | 203 GB ✗ |
| Llama 3.3 70B dense | 70 B | 140 GB ✗ | 70 GB ✗ | 35 GB ✗ |
| DeepSeek V3 MoE (671B total, 37B active per token) | 671 B | 1342 GB ✗ | 671 GB ✗ | 336 GB ✗ |
| DeepSeek R1 MoE (671B total, 37B active per token) | 671 B | 1342 GB ✗ | 671 GB ✗ | 336 GB ✗ |
| Qwen 2.5 7B dense | 7 B | 14 GB ✗ | 7 GB ✗ | 4 GB ✗ |
| Qwen 2.5 72B dense | 72 B | 144 GB ✗ | 72 GB ✗ | 36 GB ✗ |
| Mixtral 8x7B MoE (46.7B total, 12.9B active per token) | 46.7 B | 93 GB ✗ | 47 GB ✗ | 23 GB ✗ |
| Mixtral 8x22B MoE (141B total, 39B active per token) | 141 B | 282 GB ✗ | 141 GB ✗ | 71 GB ✗ |
| Gemma 2 27B dense | 27 B | 54 GB ✗ | 27 GB ✗ | 14 GB ✗ |
| Command R+ dense | 104 B | 208 GB ✗ | 104 GB ✗ | 52 GB ✗ |
| Kimi K2 MoE (1T total, 32B active per token) | 1000 B | 2000 GB ✗ | 1000 GB ✗ | 500 GB ✗ |
Math: FP16 = params × 2 bytes; INT8 = params × 1 byte; INT4 = params × 0.5 bytes. MoE models sum every expert (full weights on disk), not the per-token active subset.
Common questions
How much does Groq LPU (Language Processing Unit) cost?
Groq LPU (Language Processing Unit) doesn't have a public launch list price. Vendors like this one usually price through direct sales rather than a public sheet. See list price per TFLOP for chips that do publish.
How much memory does Groq LPU (Language Processing Unit) have?
0.23 GB of SRAM (230 MB on-die), running at 80,000 GB/s. Straight from the vendor datasheet. See chips with the most memory for context.
How much power does one Groq LPU (Language Processing Unit) draw?
375 W at the chip. A full server draws more once you add CPU, memory, networking and cooling: see the rack power number above. Compare to other chips on perf-per-watt.
Which open-source LLMs fit on one Groq LPU (Language Processing Unit)?
Of 13 open-source LLMs we track, 0 fit at FP16, 0 at INT8, and 0 at INT4. Full table above with each model's memory need.
Key facts
- Class
- Compute SoC (AI accelerator)
- Designer
- Groq
- Safety-critical?
- No (data-center inference / training)
- Record as of
- 2026-09-27
Specifications (9 fields, click to expand)
Straight from the vendor datasheet. Dense throughput shown first; sparse (2:4) numbers in parentheses where the vendor publishes them. Full datasheet linked below.
- Process node
- GlobalFoundries 14nm
- TDP
- 375 W
- Memory
- 0.23 GB SRAM (230 MB on-die)
- Memory bandwidth
- 80,000 GB/s
- FP16 (dense)
- 188 TFLOPS
- INT8 (dense)
- 750 TOPS
- Form factor
- PCIe (GroqCard) / rack-scale GroqRack
- Announced
- 2020-01-01
- Released
- 2021-06-01
Source: vendor datasheet
Foundry & process
- Process node
- GlobalFoundries 14nm
Compare with
Sources
Adoption rows appear as we confirm each chip-in-robot pairing from a public source. See every chip we track for the full catalog or the Groq page.