vs
Groq vs GMI Cloud
GMI Cloud owns NVIDIA GPUs across US and APAC data centers with 100+ text, image, video and audio models. Groq serves a few text models fast on its own chip.
By The Subconscious Team · Updated
Groq vs GMI Cloud: key differences
GMI Cloud is a vertically integrated GPU cloud. It owns its NVIDIA hardware in Tier-4 data centers in Silicon Valley, Colorado, Taiwan, Thailand and Malaysia, and its Inference Engine exposes 100+ models, including 45+ LLMs and 50+ video models from providers like Google Veo and Kling. Customers can move from shared endpoints to reserved H100 or H200 capacity. Groq owns its hardware too, but it is a custom LPU chip, and the catalog is a few open text models plus Whisper. GMI covers far more models and modalities; Groq runs its short list much faster.
Region is often the tiebreaker. GMI's in-country APAC facilities offer data residency that Groq does not list. GMI's performance claims, like recovering 10 to 15% virtualization overhead, come from GMI, and its developer mindshare is small, so testing matters. Groq publishes its speeds per model, and its small-model prices sit near the floor, though its long-term investment is in question after NVIDIA hired most of its engineers. Asia-Pacific multimodal apps fit GMI. US voice agents fit Groq.
What Groq and GMI Cloud 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 profileGMI Cloud
GMI Cloud is a vertically integrated GPU cloud and inference platform that owns its NVIDIA hardware. It runs Tier-4 data centers in Silicon Valley, Colorado, Taiwan, Thailand and Malaysia, and as an NVIDIA Cloud Partner it gets priority access to H100, H200 and B200 supply. The company pivoted from crypto mining into AI, which gave it experience standing up high-density power and cooling fast. An $82M Series A came from Headline, Wistron and Thai energy group Banpu.
Example models: GLM-4.7-Flash, Google Veo
Full GMI Cloud profileShould you choose Groq or GMI Cloud?
Groq vs GMI Cloud at a glance
| Attribute | Groq | GMI Cloud |
|---|---|---|
| Model access | Open weights | Open and third-party models |
| Flagship models | GPT-OSS 120B, Qwen 3.6 27B | GLM-4.7-Flash, Google Veo |
| Speed | 500–1,000 tok/s | Near bare-metal performance |
| Price | Near the floor on small models | $0.07 in, $0.40 out (GLM-4.7-Flash) |
| Customization | No fine-tuned model hosting | Unknown |
| Deployment | GroqCloud API | Shared, autoscaling, reserved GPUs |
| Long context | Around 131K max | Varies by model |
Frequently asked questions
What is the difference between Groq and GMI Cloud?
GMI Cloud owns NVIDIA GPUs across US and APAC data centers with 100+ text, image, video and audio models. Groq serves a few text models fast on its own chip.
When should I choose Groq over GMI Cloud?
Real-time voice agents on open text models; Tight tail latency for strict SLAs; Low per-token cost on small models.
When should I choose GMI Cloud over Groq?
Inference kept in Taiwan, Thailand or Malaysia; LLMs plus video generation on one API; Reserved H100 or H200 capacity on the same endpoint.
Is Groq or GMI Cloud cheaper?
Groq: Near the floor on small models. GMI Cloud: $0.07 in, $0.40 out (GLM-4.7-Flash). The cheaper choice depends on the model and workload.
Which has more context, Groq or GMI Cloud?
Groq: Around 131K max. GMI Cloud: 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.