Head to head

Groq LPU vs Google TPU Ironwood

Groq LPU (Groq) and Google TPU Ironwood (Google) both sit in Chips & Compute. Groq LPU is the cheaper entry point at Pricing not published. Contact sales or check account console for usage-based rates.. Google TPU Ironwood shipped more tracked changes in the last 30 days (12 vs 4). Every value below comes from the latest crawl of the vendors' own pages.

AttributeGroq LPU

Groq

Google TPU Ironwood

Google

SegmentChips & ComputeChips & Compute
VersionLPUIronwood (7th generation)
Entry costPricing not published. Contact sales or check account console for usage-based rates.from $5.4/unit
Pricing tiersGroqCloud Console Free Tier / Pay-As-You-Go freeOn-Demand (us-central1 Iowa) $12 · DWS Flex-start price (us-central1 Iowa) $6 · DWS Calendar Mode price (us-central1 Iowa) $8.4 · 1-year Commitment (us-central1 Iowa) $8.4 · 3-year Commitment (us-central1 Iowa) $5.4 · On-Demand (europe-west2 London) $13.2 · DWS Flex-start price (europe-west2 London) $6 · DWS Calendar Mode price (europe-west2 London) $8.4
Model stackcanopylabs/orpheus-arabic-saudi · canopylabs/orpheus-v1-english · kimi-k2-instruct-0905 · Llama / Kimi / GPT-OSS · Llama 3.1 8B Instant · Llama 3.3 70B Versatile · LPU v2 · meta-llama/llama-4-maverick-17b-128e-instruct · meta-llama/llama-4-scout-17b-16e-instruct · minimax-m2.5 · minimax/minimax-m2.5 · minimaxai/minimax-m2.5 · moonshotai/kimi-k2-instruct-0905 · openai/gpt-oss-120b · openai/gpt-oss-20b · openai/gpt-oss-safeguard-20b · Orpheus English · orpheus-arabic-saudi · orpheus-v1-english · Qwen 3.6 27B · qwen/qwen3-vl-32b-instruct · qwen3-vl-32b-instruct · Whisper V3 Large · whisper-large-v3Gemini · Ironwood · Ironwood (TPU v7) · Ironwood TPU · Ironwood TPU (7th Generation) · TPU v7 · TPU7x · TPU7x (Ironwood) · XLA compiler
Context window131k
Public API
Routes models
Changes / 30d412
OriginGlobalGlobal
Capabilitiesdeterministic latency, OpenAI-compatible API, open-model catalogue, batch API, Fast LLM inference, OpenAI Compatibility, Prompt Caching, Speech to Text9,216-chip pods, inference optimised, liquid cooled, JAX + PyTorch/XLA, Large-scale training, Reasoning and inference, 9,216 liquid-cooled chips per pod, 42.5 ExaFlops performance
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