Groq LPU vs NVIDIA GPU for LLM Inference: Architectural Differences and Cost-Per-Token in 2026

Groq's Language Processing Unit (LPU) is not a GPU. It is a tensor streaming processor built on a deterministic VLIW (Very Long Instruction Word) architecture. Unlike NVIDIA GPUs which use SIMT (Single Instruction, Multiple Threads) with warp schedulers and shared memory hierarchies, the LPU uses a dataflow architecture where instructions are scheduled at compile time. There is no instruction fetch or branch prediction at runtime.

NVIDIA GPUs rely on massive thread-level parallelism to hide memory latency, using 128+ warps per SM to keep compute units busy while waiting for HBM transactions. The LPU takes the opposite approach: it eliminates memory latency by using SRAM exclusively (no DRAM) and streaming data through the compute fabric in a predictable pipeline. This makes the LPU deterministic but limits total memory capacity to roughly 230 MB per chip.

Memory capacity is the LPU's primary limitation and the GPU's primary advantage. A single Groq LPU inference chip contains 230 MB of on-chip SRAM, sufficient to hold the weights of a Llama 3 8B model at FP16 when split across multiple LPUs. In contrast, a single H200 SXM can hold the full model in its 141 GB of HBM3e. The LPU requires model parallelism across many chips for any model above approximately 3 billion parameters.

Groq compensates by scaling horizontally. A Groq Rack delivers 144 LPU inference chips connected via a proprietary chip-to-chip interconnect with 1.2 TB/s of bandwidth per direction. For a 70B parameter model at FP8, the LPU rack spreads weights across approximately 36 chips, introducing inter-chip communication overhead that partially offsets the latency advantage of the deterministic architecture.

The LPU's deterministic scheduling eliminates tail latency. Groq reports P99 latency within 1.5x of P50 latency for LLM inference, compared to 3-5x for GPU-based inference where warp scheduling and memory controller contention produce significant variance. For real-time applications like voice assistants or agentic loops running under 50 ms SLOs, this consistency is valuable.

GPU latency variance comes primarily from HBM bank conflicts and TLB misses during attention computation. FlashAttention 3 reduces this variance through async prefetching, but GPUs still exhibit 2-3x higher P99 latency than LPUs on identical batch sizes. For batch sizes above 64, the gap narrows as the GPU's higher total memory bandwidth becomes the dominant factor and the LPU's inter-chip communication overhead grows.

At low batch sizes (1-8), the LPU delivers 3-4x higher tokens-per-second than an equivalent-cost GPU configuration. This advantage comes from the elimination of memory-bound stalls in the prefill phase. For interactive chat applications with small batches, Groq's architecture is objectively superior.

At high batch sizes (128+), the GPU advantage reemerges. The H200's 4.8 TB/s of HBM bandwidth per GPU, multiplied across 8 GPUs in an NVL domain, provides more aggregate memory bandwidth than the LPU's SRAM fabric. For batched offline inference or synthetic data generation, GPUs deliver lower cost-per-token. The cross-over point is approximately batch size 32 for 70B models and batch size 8 for 8B models.

At current market rates, Groq LPU inference costs approximately $0.38 per million tokens for a 70B model at FP8. An equivalent 8x H200 configuration costs $0.72 per million tokens on spot pricing, while 8x B300 costs $0.55 per million tokens. For real-time workloads with batch size 1-4, the LPU holds a 40-90% cost advantage.

For batch inference workloads (batch size 64+), the math flips. The H200 achieves $0.42 per million tokens and the B300 achieves $0.31 per million tokens, beating the LPU on cost. These figures assume optimal framework configuration: vLLM with chunked prefill for GPUs and Groq's proprietary runtime for LPUs.

The total cost picture must include egress and availability. GPU capacity on ClusterBid offers multi-provider redundancy and geographic distribution, while Groq inference is available only through Groq Cloud's API, which operates from three US data centers as of mid-2026.

The LPU excels in interactive, latency-sensitive inference: chatbots, real-time translation, coding assistants, and voice interfaces. Any workload where a user waits for a response benefits from the LPU's deterministic low latency. Models under 70B parameters see the largest advantage, as larger models require more LPU chips and diminish the deterministic benefit.

GPUs remain the better choice for offline inference, synthetic data generation, batch processing, and any workload involving models over 100B parameters. GPUs also win on flexibility: the same H200 cluster that serves inference can be repurposed for fine-tuning, RLHF, or training without hardware migration. The LPU is a single-purpose inference accelerator with no training capability.

Most AI teams should treat the LPU as a specialized inference accelerator for latency-critical paths, not a GPU replacement. A common pattern in 2026 is running chat and agent inference on LPUs while routing batch processing, fine-tuning, and large-model inference to GPU clusters. This hybrid approach optimizes both latency and cost.

For teams committed to GPU-only infrastructure, the B300's FP4 support and improved memory bandwidth narrow the latency gap with LPUs significantly. At batch size 1 with a 70B model, the B300 is approximately 30% slower than the LPU on time-to-first-token but offers 3x the throughput headroom for burst traffic and zero migration cost to training workflows.