Cerebras vs Groq vs SambaNova: speed benchmark compared

If you are optimizing for tokens-per-second rather than cost-per-token, the Cerebras vs Groq vs SambaNova speed debate matters more than model selection. All three field custom silicon that bypasses the memory-bandwidth wall of GPUs, but they differ sharply in API surface, model coverage, and failure modes.

Architecture: what actually makes them fast

Cerebras

Cerebras builds wafer-scale engines. A single CS-3 wafer integrates hundreds of thousands of compute cores with tens of gigabytes of on-wafer SRAM, delivering bandwidth that HBM-based GPUs cannot match for weight-heavy inference. The tradeoff is rigidity: a model must be compiled into Cerebras’s format and fit within the wafer’s memory map. You get massive throughput on supported shapes, but you do not bring your own CUDA graph.

Groq

Groq’s LPU (Language Processing Unit) is a deterministic tensor streaming processor. Each chip carries around 230 MB of SRAM and streams weights through a fixed schedule, eliminating cache misses and scheduler jitter. The chip is not general-purpose; it runs a dataflow program generated by Groq’s compiler. That determinism is why Groq’s latency curve stays flat under load.

SambaNova

SambaNova’s RDU (Reconfigurable Dataflow Unit) maps a model to a spatial dataflow graph executed on-chip with large on-chip memory tiers plus HBM. It sits between Cerebras’s monolithic wafer and Groq’s fixed-function LPU: more flexible than Groq for model variants, less dense than Cerebras for a single giant model. Enterprises like the RDU because it can be packaged as an on-prem appliance.

Latency and throughput characteristics

Published demos show Groq serving Llama-3-70B at roughly 200–300 tokens/s with time-to-first-token (TTFT) under 10 ms at batch size 1. Cerebras pushes higher on pure throughput—often quoted in the 1000+ tokens/s range for 70B-class models—because the wafer feeds all cores simultaneously. SambaNova lands between, with consistent mid-high throughput and TTFT in the tens of milliseconds.

The Cerebras vs Groq vs SambaNova speed gap narrows as context length grows. Long prompts stress memory bandwidth differently; Groq’s deterministic pipeline keeps inter-token latency predictable, while Cerebras can degrade if the active parameter set spills out of on-wafer memory. For streaming chat, TTFT dominates perceived speed; for batch jobs, aggregate tokens/sec matters more.

Concurrency changes the picture. Groq’s LPU clusters scale by adding chips per model replica; Cerebras scales by wafer count. Neither lets you spin up arbitrary parallel batches the way vLLM on GPUs does.

Model coverage and capabilities

Groq maintains a curated list: Llama-3.1-8B/70B, Mixtral-8x7B, Gemma-7B, and a few others compiled for the LPU. Cerebras supports a similar open-weight roster (Llama-3.1, Mistral, etc.) but each model must be wafer-compatible. SambaNova covers Llama families and select enterprise models, often via negotiated access rather than a public model zoo.

None of the three offer self-serve fine-tuning. They are inference-only. If you need LoRA serving or custom training, stay on GPU clusters. Tool calling and JSON mode exist on Groq for several models; Cerebras and SambaNova lag in parity with OpenAI’s function-calling spec.

Cost model

Groq’s public token pricing is the most aggressive among the three for equivalent open weights. Cerebras charges a premium for speed—expected when you consume wafer-time. SambaNova avoids list pricing; it is enterprise contracts and appliance purchases.

Do not compare raw token price in isolation. At 1000 tokens/s, a Cerebras call that finishes in 0.1 s saves wall-clock cost in a latency-sensitive service even if its per-token rate is 2x Groq’s. Conversely, for asynchronous bulk work, Groq’s low list rate wins. All three meter by token; none charge for provisioned capacity the way GPU instances do.

Ergonomics and API compatibility

All three expose OpenAI-compatible REST endpoints. That is the only reason this comparison is practical. A minimal Groq streaming call:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.groq.com/openai/v1",
    api_key="gsk_xxx",
)
resp = client.chat.completions.create(
    model="llama-3.1-70b-versatile",
    messages=[{"role": "user", "content": "ping"}],
    stream=True,
)
for chunk in resp:
    print(chunk.choices[0].delta.content or "", end="")

Cerebras and SambaNova use the same shape, differing only in base_url and model identifiers. Streaming works everywhere. Error surfaces vary: Groq returns standard 429s on free-tier limits; Cerebras returns capacity errors when wafers are saturated; SambaNova errors are often contract-defined.

If you want to avoid writing three clients and handling fallback by hand, a gateway that aggregates them behind one OpenAI-compatible endpoint helps. n4n.ai does this with automatic fallback when a provider is rate-limited or degraded, and per-token usage metering across all three.

Ecosystem and limits

Groq has the widest self-serve developer adoption; its community posts benchmarks constantly and the docs are clean. Cerebras partners with model labs for record-setting runs but has thinner public documentation. SambaNova pushes enterprise PoCs and on-prem rack deployments.

Hard limits to plan for:

• Context windows: Groq caps at 128k for many models; Cerebras similar; SambaNova varies by contract.
• Rate limits: Groq free tier is strict; Cerebras waits on wafer capacity; SambaNova is negotiated.
• Regional availability: all US-centric, with limited EU endpoints.
• Failure modes: Groq throttles hard at limit; Cerebras can queue; SambaNova appliance downtime is your problem.

Head-to-head summary

The Cerebras vs Groq vs SambaNova speed ranking flips depending on whether you measure TTFT or total job time.

Which to choose

Real-time interactive agents

Pick Groq. The deterministic LPU gives the lowest and most predictable TTFT. When a user is waiting on a streaming reply, jitter kills UX more than peak throughput. Groq’s OpenAI-compatible API and free tier make prototyping trivial.

Bulk extraction or batch summarization

Cerebras wins on the Cerebras vs Groq vs SambaNova speed axis for throughput. If you need to chew through millions of documents and can tolerate batch latency, the wafer’s token rate amortizes cost despite higher per-token price.

Enterprise on-prem with data residency

SambaNova is the only one shipping appliances. If regulations mandate hardware in your rack, the RDU is the path; expect a sales cycle and custom model onboarding.

Multi-provider production

Do not marry one. Route by model and latency budget. Use client-side fallback or a gateway that honors routing directives and forwards provider cache-control hints. That keeps you insulated when Groq’s free tier throttles or a Cerebras wafer is in maintenance. The architectural tradeoffs above are stable for 2025 planning even as model lists expand.