Groq vs Cerebras: Cheapest Inference Chip to Rent in 2026

Groq and Cerebras both sell speed, not GPUs. This Groq vs Cerebras comparison exists because buyers keep landing on separate reviews of each chip against an H100, with no page that puts Groq's LPU and Cerebras's WSE-3, the two most-searched non-Nvidia inference chips, directly against each other. Here's the direct comparison: architecture, tokens/sec, cost per million tokens, and where each chip's catalog and lock-in stop it from being the obvious pick.

The short answer: Cerebras is faster on raw throughput, by roughly 3x to 8x depending on which Groq benchmark you use, but Groq is cheaper per million tokens on the two models Spheron tracked across both rate cards. Cerebras's own materials put its price-performance edge at up to 6x, which only holds because its tokens/sec lead is bigger than its sticker-price premium. Neither chip runs DeepSeek, Gemma, Mistral, or anything multimodal, and neither offers self-serve fine-tuning. If your model or your fine-tuning need falls outside that shared gap, a rented GPU is still the answer, not a third inference chip.

For the architecture deep dives this post pulls numbers from, see our full Groq API pricing breakdown and Cerebras vs NVIDIA H100 inference guide. Those posts do the model-by-model math against GPU rental; this one puts Groq and Cerebras against each other.

Groq LPU vs Cerebras WSE: The Short Answer

Cerebras wins on speed. Groq wins on price. That's the whole trade, and almost everything below is detail on how wide each gap actually is.

Sources: Cerebras CS-3 vs Groq LPU comparison, Spheron's Groq API pricing breakdown, Spheron's Cerebras vs H100 inference guide, getmaxim.ai Cerebras cost calculator.

*Groq's Llama 3.3 70B range spans two different measurement methods: Cerebras's own comparison page puts Groq at 403-750 tokens/sec, while Artificial Analysis's independent, standardized benchmark measured Groq at 293.6 tokens/sec on the same model. See the tokens-per-second section below for why the two numbers diverge.

Architecture: SRAM-Based LPU vs Wafer-Scale Engine

Both companies made the same core bet: pull the model weights off HBM entirely and keep them in on-chip SRAM, where bandwidth is measured in petabytes per second instead of terabytes per second. They just scaled that bet to wildly different sizes.

Groq's LPU: 230MB SRAM per Chip, Dataflow Pipeline

Groq's LPU carries 230MB of on-chip SRAM and draws roughly 375W per chip, running natively at 8-bit precision, with 16-bit inference running significantly slower (Cerebras's own comparison of the two). That's a small, single-purpose chip: it does deterministic, sequential dataflow through the decode step of autoregressive generation, and nothing else. A single LPU can't hold a 70B model's weights, so Groq assembles hundreds of chips into an interconnected fabric to serve any model of consequence, trading chip count for the SRAM capacity a wafer-scale design gets in one piece.

We covered the mechanics of this SRAM-over-HBM tradeoff in more depth in our breakdown of NVIDIA's licensed Groq 3 LPU, which is the architecture behind Groq's approach carried into NVIDIA's own inference lineup, not the exact chip GroqCloud runs today. The underlying idea (replace HBM with a flat on-die SRAM mesh so decode never leaves the chip) is the same one GroqCloud's current-generation LPU already ships on, just at a different scale and under a different production roadmap.

Cerebras's WSE-3: 44GB On-Die SRAM, 900,000 Cores, 21 PB/s Fabric

The WSE-3 doesn't assemble many small chips into a system, it is the system. A single silicon wafer holds 900,000 AI cores and 44GB of on-die SRAM, all connected by an on-chip fabric running at 21 PB/s, with no HBM stack anywhere in the design (Spheron's Cerebras vs H100 inference guide). The full system draws roughly 27kW and delivers about 125 PFLOPS, and unlike Groq's 8-bit-native pipeline, the WSE-3 supports 16-bit precision natively in hardware (Cerebras CS-3 vs Groq LPU).

That's the tradeoff in one line: Groq spreads a workload across many small, low-power chips wired together; Cerebras puts the entire workload on one enormous chip with no chip-to-chip hop at all. Cerebras claims roughly 3x higher compute-per-watt than an 8-GPU DGX system running the same class of workload, a number that matters if you're the one paying the power bill on a dedicated deployment rather than a per-token API call. 44GB is still tight for a 70B model at full precision, which is why Cerebras, like Groq, leans on quantization and multi-system partitioning for its larger checkpoints.

Tokens Per Second Head to Head

Cerebras is faster on every published benchmark, and the gap is not close, though the exact size of the gap depends on whose numbers you use for Groq. On Llama 3.3 70B, Cerebras's own comparison page puts Groq at 403-750 tokens/sec against Cerebras's 2,100-2,500 tokens/sec, a 3-6x spread (Cerebras CS-3 vs Groq LPU). Independent benchmarking firm Artificial Analysis measured Groq lower still on the same model, at 293.6 tokens/sec under its own standardized test conditions (Artificial Analysis), which widens the gap to roughly 7-8x against Cerebras rather than 3-6x. The two figures aren't a contradiction so much as two different yardsticks: Cerebras's number comes from a vendor comparison, Artificial Analysis's comes from an independent, standardized test harness run across many providers at once. On GPT-OSS-120B, Cerebras's own documentation claims roughly 3,000 tokens/sec, while Artificial Analysis measured a sustained ~1,700 tokens/sec independently; Groq lands at roughly 478-493 tokens/sec on the same model.

Llama 3.3 70B and GPT-OSS 120B Benchmarks

*Low end (293.6) is Artificial Analysis's independent measurement; high end (750) is Cerebras's own comparison figure. See below.

Cerebras founder and CEO Andrew Feldman put the gap plainly at the RAISE Summit 2026: "We're the fastest inference in the industry, not by a little bit, but 10, 15, 20, 30 times faster than GPUs" (SiliconANGLE). That claim is measured against GPUs, not Groq, but it tells you where Cerebras is positioning its wafer-scale bet: as the fast option against everything, chips included.

Groq's own throughput numbers still stand up against most of the market, just not against Cerebras. That 293.6 tokens/sec figure referenced above is the one Artificial Analysis uses to rank Groq as the fastest of 14 benchmarked providers serving Llama 3.3 70B, just ahead of SambaNova at 288.6 tokens/sec (Artificial Analysis). Groq's positioning has always been speed relative to the rest of the GPU-and-API market, not speed relative to Cerebras specifically, and Cerebras isn't in that particular provider comparison. Once you put the two chips side by side using their own published numbers, Cerebras's wafer-scale bandwidth advantage wins the throughput contest outright, by anywhere from 3x to 8x depending on which Groq figure you start from.

Cost Per Million Tokens Head to Head

The short version: Groq is cheaper per million tokens on both models we could compare directly. GPT-OSS 120B runs $0.15/$0.60 per million input/output tokens on Groq versus roughly $0.35/$0.75 on Cerebras. Llama 3.3 70B runs $0.59/$0.79 on Groq versus roughly $0.85/$1.20 on Cerebras. Cerebras's own materials claim up to a 6x price-performance advantage over Groq anyway, and that claim is defensible, just not in the way "price-performance" sounds. It's about throughput per dollar spent generating tokens fast enough to matter for a workload, not about the sticker price per token being lower.

Published Rate Cards for Llama 3.3 70B and GPT-OSS 120B

Groq's Llama 3.3 70B and GPT-OSS 120B rates come from our Groq API pricing breakdown, sourced from Groq's own published rate card. The Cerebras Llama 3.3 70B rate is from getmaxim.ai's Cerebras cost calculator, current as of 11 Aug 2026; the GPT-OSS 120B figure follows the same public rate card pattern. Cerebras's own comparison notes its pricing runs "similarly to Groq or up to ~50% higher," assuming a 3:1 input-to-output token ratio, which lines up with what these two rate cards show.

Blended at a typical 80/20 input-heavy split, Groq's Llama 3.3 70B rate works out to roughly $0.63-$0.67 per million tokens, a number that's competitive with, or below, what a heavily-tuned self-hosted H100 deployment running at 85% utilization costs to run (~$0.62/M tokens), per our earlier cost-per-token benchmarking.

Why Raw Tokens/Sec Doesn't Map Directly to Cost Per Token

Here's the thing that trips people up comparing these two chips: Cerebras generates 3-6x more tokens per second, but its price per token is only 40-70% higher than Groq's, not 3-6x higher. That gap between the throughput multiple and the price multiple is exactly where Cerebras's "6x price-performance" claim comes from. If you're paying by wall-clock time to finish a fixed amount of generation work (a batch job, an agent loop with a latency budget, anything where speed itself has a dollar value), Cerebras can come out ahead even at a higher price per token, because it finishes the job faster.

If you're paying purely by the token with no time pressure on the workload, none of that throughput advantage shows up in your bill. You're just comparing $0.15 to $0.35, and Groq wins. The right lens depends on which constraint actually binds your workload: token volume, or wall-clock latency. Our broader inference cost economics guide works through this utilization-vs-volume distinction in more depth if cost per token alone isn't giving you the full picture.

Pricing fluctuates based on GPU availability. The prices above are based on 11 Aug 2026 and may have changed. Check current GPU pricing → for live rates.

Model Availability and Framework Lock-In on Each

Neither platform is a general-purpose inference layer. Both are narrow, curated catalogs tuned for a small set of models running well on purpose-built silicon, and that curation is the real cost of choosing either one.

Cerebras's Inference API supports Llama 3.1 8B, Llama 3.1 70B, Llama 3.3 70B, Qwen 3-32B, Qwen 3-235B Instruct, Qwen 3-235B Thinking, GPT-OSS-120B, and ZAI GLM-4.7 (Spheron's Cerebras vs H100 inference guide). Groq's non-enterprise catalog is narrower: Llama 3.1 8B Instant, Llama 3.3 70B Versatile, GPT-OSS 20B, GPT-OSS 120B, Qwen 3.6 27B, and an enterprise-only Minimax M2.7 with pricing available on request.

What's Missing from Both Catalogs

DeepSeek, Gemma, Mistral, and every multimodal model are absent from both platforms as of 2026. If your production stack runs any of those, this comparison doesn't apply to you at all. You're renting a GPU either way. Our DeepSeek V3.2 vs Llama 4 vs Qwen3 comparison works out cost per token and GPU sizing across three model families neither Groq nor Cerebras serves. This is the same fixed-architecture tradeoff that shows up across the whole non-GPU inference chip category: Etched AI's Sohu chip hard-codes transformer attention with the identical no-MoE, no-multimodal ceiling, which our Etched Sohu vs NVIDIA ASIC comparison covers in more detail.

SDK and Self-Serve Fine-Tuning Differences

Neither chip gives you a self-serve path to a fine-tuned checkpoint. Groq gates LoRA fine-tuning entirely behind its Enterprise tier and a sales request, with no self-serve API or UI. Cerebras hardware supports training, but the public Cerebras Inference API is inference-only, and fine-tuning requires a dedicated cluster engagement rather than an on-demand purchase.

Both platforms also lock you into a proprietary SDK. You can't point a standard vLLM or SGLang client at either chip; the client library, the request format, and the operational tooling are vendor-specific to Groq or to Cerebras respectively. If either company changes pricing, deprecates a model, or has an outage, you have no fallback runtime without a rewrite.

Cerebras does offer a lower barrier to trying it out than Groq's enterprise-gated fine-tuning path: a Free Trial tier with $5 in credits and access to every Cerebras-hosted model, plus a self-serve Developer tier starting at $10 with 10x higher rate limits than the free tier (Cerebras pricing). That's a meaningfully easier way to benchmark Cerebras against your own workload before committing to production spend than Groq's tier structure offers.

Where Renting Standard GPUs Still Wins on Flexibility

Both companies are having a moment. Groq raised $750 million in September 2025 at a $6.9 billion valuation, roughly double where it stood a year earlier, with BlackRock, Neuberger Berman, Samsung, and Cisco among the investors (Yahoo Finance). Cerebras IPO'd on May 14, 2026 at $185/share and closed its first trading day at $311, a 68% gain that pushed its market cap to nearly $70 billion, or up to $86 billion fully diluted (Yahoo Finance). Neither company is going anywhere in 2026, and neither is going to widen its model catalog to match what a GPU already runs.

That's the real ceiling on this whole comparison: you're picking between two narrow, fast, cheap-per-token catalogs, or a GPU that runs anything you can find on Hugging Face. An H100 on Spheron currently runs $2.65/hr on-demand, with spot pricing around $2.10/hr when available. At any concurrency above roughly 8 simultaneous requests, a rented H100 running vLLM with continuous batching produces more tokens per dollar than either Groq or Cerebras on a shared-fleet API rate, and it does it for any model, with fine-tuning included, on a stack you fully control. Our GPU inference selection guide covers L40S, H100, H200, and B200 with cost-per-token data at realistic batch sizes if a GPU turns out to be the better fit for your workload than either chip.

If your model or your latency budget doesn't fit either Groq's or Cerebras's catalog, SambaNova's SN40L is a third SRAM-and-dataflow-style challenger worth a look, particularly for large MoE inference; see our SambaNova SN40L vs H200/B200 comparison for the specifics. And if latency budgets, not raw cost, are what's actually driving the decision, our TTFT and inter-token latency SLO guide covers how to set and enforce those SLOs regardless of which chip ends up serving the traffic.

Groq vs Cerebras: Which Should You Rent in 2026

Pick Cerebras when your workload is latency-sensitive at low concurrency and your model is on its list. A single WSE-3 clearing 2,000+ tokens/sec on Llama 70B is a genuine advantage for real-time agent loops or single-user chat where every millisecond of generation time compounds.

Pick Groq when cost per token is the binding constraint and your model is one of its six. Groq's sticker price beats Cerebras on every model we could compare directly, and it's already competitive with a well-tuned self-hosted H100 deployment running at 85% utilization, without you having to do any of the tuning.

Pick neither, and rent a GPU instead, when your model is DeepSeek, Gemma, Mistral, or multimodal (all off both catalogs), when you need self-serve fine-tuning, when you're running above roughly 8 concurrent requests where GPU throughput scaling overtakes both flat-rate chips, or when vendor lock-in to a proprietary SDK is a risk you're not willing to take on. That last group covers most production teams running anything beyond a narrow, well-defined chatbot workload.

If neither Groq's nor Cerebras's catalog covers your model, or you need fine-tuning access neither offers self-serve, renting the GPU directly gets you both.

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