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Groq vs Wafer
Groq gets speed from custom silicon. Wafer gets it from agents that tune software stacks on NVIDIA and AMD GPUs. Hardware bet against software bet.
By The Subconscious Team · Updated
Groq vs Wafer: key differences
Both sell faster open models, from different layers. Groq built its own LPU chip, which keeps weights in SRAM and publishes 500 to 1,000 tokens per second on GPT-OSS. Wafer runs standard NVIDIA or AMD GPUs and uses AI agents to tune batching, decoding, quantization and kernels per workload. Wafer reports Qwen 3.5 397B running 2.8x faster than stock SGLang and GLM 5.1 and DeepSeek V4 Pro each 2x faster than vLLM, self-reported against untuned baselines. That puts Wafer on much larger models than Groq serves, though not at Groq's absolute speed.
Pricing and deployment differ too. Wafer Pass is a flat-rate subscription from $10 a week that covers every hosted model and plugs into Claude Code, Cline and OpenHands. Wafer also builds dedicated deployments around a customer's SLO and keeps re-tuning them. Groq bills per token with cache and Batch discounts and has no dedicated custom path. Wafer is very young with a small catalog. Groq has an operating history but an uncertain future after NVIDIA's hiring deal.
What Groq and Wafer do
Groq
Groq serves open models on its own chip, the LPU, which keeps model weights in on-chip SRAM and runs a deterministic schedule instead of waiting on GPU memory. Groq publishes 1,000 tokens per second on GPT-OSS 20B and 500 on GPT-OSS 120B. Latency stays tight between median and tail, which matters for strict SLAs. The API is OpenAI-compatible and also hosts Whisper for speech to text plus an agentic system called Groq Compound with built-in search and code execution.
Example models: GPT-OSS 120B, Qwen 3.6 27B
Full Groq profileWafer
Wafer builds AI agents that act as GPU performance engineers, then sells inference on the stacks those agents tune. The company came out of Y Combinator's Summer 2025 batch as a "Cursor for CUDA" that turned slow PyTorch into custom kernels. Founders Emilio Andere and Steven Arellano are based in San Francisco. Its agents profile a workload, generate candidate configs across batching, decoding, quantization, engines, kernels and hardware, measure each one and deploy the winner.
Example models: Qwen 3.5 397B Turbo, GLM 5.1 Turbo
Full Wafer profileShould you choose Groq or Wafer?
Groq vs Wafer at a glance
| Attribute | Groq | Wafer |
|---|---|---|
| Model access | Open weights | Open weights |
| Flagship models | GPT-OSS 120B, Qwen 3.6 27B | Qwen 3.5 397B Turbo, GLM 5.1 Turbo |
| Speed | 500–1,000 tok/s | 2–2.8x vs stock vLLM or SGLang |
| Price | Near the floor on small models | Wafer Pass from $10 a week |
| Customization | No fine-tuned model hosting | Agent-tuned dedicated deployments |
| Deployment | GroqCloud API | Serverless pass, dedicated |
| Long context | Around 131K max | Varies by model |
Frequently asked questions
What is the difference between Groq and Wafer?
Groq gets speed from custom silicon. Wafer gets it from agents that tune software stacks on NVIDIA and AMD GPUs. Hardware bet against software bet.
When should I choose Groq over Wafer?
Fastest replies on small open models; Voice agents needing steady tail latency; Per-token billing without subscriptions.
When should I choose Wafer over Groq?
Big open models like Qwen 3.5 397B in coding agents; Flat-rate access inside Claude Code or Cline; Dedicated endpoints tuned to a latency SLO.
Is Groq or Wafer cheaper?
Groq: Near the floor on small models. Wafer: Wafer Pass from $10 a week. The cheaper choice depends on the model and workload.
Which has more context, Groq or Wafer?
Groq: Around 131K max. Wafer: Varies by model.
Related comparisons
Running long-horizon agents?
If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.