A Groq LPU (Language Processing Unit) is a deterministic, spatial dataflow chip built to run transformer inference as a fixed pipeline of tensor operations, not as software-dispatched kernels. Groq LPU vs GPU inference speed compares a purpose-built sequential accelerator against general-purpose SIMT hardware running CUDA graphs: the LPU wins on latency-critical decode and time-to-first-token, while GPUs retain an edge on training, flexibility, and batch efficiency at massive scale.
What the LPU architecture actually does
Groq’s LPU is not a GPU with different branding. It is a statically scheduled dataflow processor where each layer of a transformer is mapped to a physical path of on-chip SRAM and multiply-accumulate units. There is no HBM round-trip per layer, no warp scheduling, and no kernel launch overhead. The compiler lays out the entire model as a deterministic sequence of cycles.
This design trades programmability for predictability. You cannot run arbitrary CUDA code on an LPU. You compile a specific model topology (currently transformer decoders and similar) using Groq’s toolchain, and the chip executes that graph at a fixed rate. Because the schedule is known at compile time, the chip streams activations from one stage to the next without dynamic arbitration.
The practical consequence: for a 70B-class model that fits the LPU’s on-chip memory footprint after weight partitioning across a rack, the decode step is limited by clock rate and MAC density, not by memory bandwidth contention. GPUs, even H100s with 3TB/s HBM3, still pay a tax for coalescing accesses and context switching across batches.
GPU inference baseline
A standard GPU serving stack (vLLM, TensorRT-LLM, HuggingFace TGI) runs the model as a series of kernels. The prefill phase computes attention and fills the KV cache; the decode phase generates one token per forward pass. Each pass launches matmul kernels, attention kernels, and reduction ops. The CUDA driver and GPU scheduler multiplex these on streaming multiprocessors.
Under continuous batching, a GPU can serve many sequences concurrently, raising utilization. But tail latency grows with batch size because a single slow sequence delays the next scheduled wave. Memory bandwidth is the dominant cost: weights must be read from HBM for every token generated. An A100 with 80GB can hold a 70B model in FP16 with tensor parallelism, but each decode step still moves hundreds of gigabytes of weights per second.
Published community baselines put single-stream 70B decode on 8×A100 in the 20–40 tokens/sec range, depending on quantization and sequence length. Groq has demonstrated vendor-published numbers above 200 tokens/sec for the same model class on a single LPU node. That is the core of Groq LPU vs GPU inference speed: an order-of-magnitude gap on isolated low-batch decode.
Where the speed difference comes from
The gap is not “more FLOPS.” It is the elimination of three GPU taxes:
- Kernel launch latency – each op on GPU requires host-side enqueue and device-side dispatch. LPU runs a pre-laid cycle-accurate path.
- Memory traffic – GPU decodes pull weights from HBM each step. LPU keeps weights in distributed SRAM and passes activations through wiring.
- Scheduling uncertainty – GPU batches contend for SMs. LPU has no scheduler; the compiler already decided every cycle.
For prefill (processing the input prompt), GPUs with high FLOPS and HBM bandwidth are competitive because the workload is compute-dense and parallel across tokens. LPU prefill is fast but not disproportionately so. The standout delta appears in decode, especially at batch size 1–8 where GPU utilization is poor but LPU determinism still yields fixed latency.
Why this matters for production serving
If you serve a coding assistant or a chat UI where users expect the first token in <200ms and steady streaming thereafter, GPU tail latency under load is your enemy. Groq LPU vs GPU inference speed becomes a product decision: do you accept GPU variance and cost-efficient batching, or pay for LPU determinism to guarantee snappy UX?
Throughput per dollar is the counterpoint. GPUs amortize cost across large batches and handle many model architectures. LPU capacity is tied to Groq’s compiler and hardware availability. For bulk offline summarization with high concurrency, GPU fleets win on economics. For interactive agents that fan out many small LLM calls, LPU’s low time-to-first-token reduces wall-clock orchestration time.
Concrete example: measuring both from one client
You can benchmark the difference without standing up two stacks. An OpenAI-compatible gateway that fronts multiple providers lets you swap the model string and capture latency. For instance, n4n.ai exposes one endpoint that routes to either a Groq LPU backend or a GPU provider based on the model prefix, and returns standard usage metadata.
from openai import OpenAI
import time
client = OpenAI(base_url="https://api.n4n.ai/v1", api_key="YOUR_KEY")
def timed_completion(model, prompt):
t0 = time.perf_counter()
resp = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": prompt}],
max_tokens=128,
stream=False,
)
elapsed = time.perf_counter() - t0
return resp, elapsed
groq_resp, groq_t = timed_completion("groq/llama-2-70b", "Explain spatial dataflow.")
gpu_resp, gpu_t = timed_completion("gpu/llama-2-70b", "Explain spatial dataflow.")
g_tokens = groq_resp.usage.completion_tokens
p_tokens = gpu_resp.usage.completion_tokens
print(f"Groq LPU: {groq_t:.2f}s, {g_tokens} tok, {g_tokens/groq_t:.1f} tok/s")
print(f"GPU: {gpu_t:.2f}s, {p_tokens} tok, {p_tokens/gpu_t:.1f} tok/s")Run this against your own traffic shape. You will typically see the LPU path return first and sustain higher decode rate at low concurrency. At batch size 32 simulated via parallel requests, the GPU’s aggregated throughput may surpass the LPU node because the GPU spreads weight reads across many sequences while the LPU is pinned to its deterministic pipeline.
Common misconceptions
“LPU is just a faster GPU.” No. It is a different compute paradigm. Code that assumes warp-level primitives or HBM addressing will not run. You compile a model, you do not program the chip.
“Groq LPU vs GPU inference speed is always 10x.” The ratio is workload-dependent. For prefill-heavy or high-batch scenarios, the gap narrows sharply. At batch 64 with continuous batching on H100, GPU throughput per token can match or exceed LPU while costing less.
“LPUs replace GPUs for training.” They do not. Training requires backprop, dynamic shapes, and frequent optimizer state updates. LPU’s deterministic forward-only path is useless there.
“Any model runs on LPU.” Only topologies Groq’s compiler supports. Custom architectures or heavy branching logic need rework. GPU stacks ingest new HuggingFace models in hours; LPU onboarding is a compiler project.
When to choose which
Pick an LPU (Groq) when:
- You need consistent sub-300ms time-to-first-token for interactive apps.
- Your workload is decode-dominated, batch size low, and model is supported.
- You want to avoid GPU scarcity and CUDA version drift.
Pick GPUs when:
- You run large batches, mixed modalities, or embedding/rerank models.
- You need to fine-tune or rapidly swap architectures.
- Your cost model depends on maximizing utilization per card.
Groq LPU vs GPU inference speed is not a headline number; it is a curve across batch size, sequence length, and model fit. Measure on your own prompts, with your own concurrency, before committing. The architecture difference is real, but so are the constraints.
A note on routing and metering
If you operate a gateway that abstracts provider differences, honor client routing directives and forward provider cache-control hints. That lets you A/B the same request against LPU and GPU backends without changing application code, and per-token usage metering makes the speed-vs-cost tradeoff visible instead of guessed. The deterministic LPU path is easiest to reason about when your observability already separates prefill from decode tokens.