vsGMI Cloud vs Thinking Machines
GMI Cloud owns NVIDIA hardware in the US and Asia and serves 100+ models. Thinking Machines is a research lab whose API trains open models more than it serves them.
By The Subconscious Team · Updated
GMI Cloud vs Thinking Machines: key differences
GMI Cloud is an infrastructure company. It runs Tier-4 data centers in Silicon Valley, Colorado, Taiwan, Thailand and Malaysia, and its Inference Engine exposes 100+ models across LLMs, video, image and audio through an OpenAI-compatible API. Customers move from shared endpoints to autoscaling to reserved H100 or H200 capacity on the same API, with GLM-4.7-Flash at $0.07 in and $0.40 out. GMI lists no managed fine-tuning. Thinking Machines is the reverse. Tinker handles LoRA SFT and RL on open weights like GLM-5.3, Kimi K2.6 and Qwen3.5, with teams writing the training code, and serving is limited to a beta API for its Inkling models.
For Asia-Pacific buyers who need data kept in-country, GMI is the only option of the two. It also covers media generation, such as Google Veo and Kling, on one bill. Thinking Machines brings a model GMI does not: Inkling, a 975B Apache 2.0 MoE with 1M context and text, image and audio input, at $1.00 in and $4.05 out. Tinker's training context runs 32K to 256K depending on the model. GMI has less developer mindshare and third-party benchmarking than US peers, so its claims need testing. Thinking Machines' checkpoint endpoint is not meant for user-facing traffic.
What GMI Cloud and Thinking Machines do
GMI 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 profileThinking Machines
Thinking Machines Lab is the San Francisco lab Mira Murati, formerly CTO of OpenAI, founded in February 2025, with OpenAI co-founder John Schulman as chief scientist. It raised about $2 billion at a $12 billion valuation in July 2025 in a round led by Andreessen Horowitz, and in March 2026 signed a multi-year Nvidia deal for one gigawatt of Vera Rubin capacity. Its main developer product is Tinker, an API for post-training open-weight models that launched in October 2025 and is now generally available. Tinker exposes four low-level calls, forward_backward, optim_step, sample and save_state, so teams write their own supervised or reinforcement learning loops while Thinking Machines runs the distributed GPU work. Training uses LoRA adapters rather than full weight updates.
Example models: Inkling, Inkling-Small, Qwen3.5, Kimi K2.6
Full Thinking Machines profileShould you choose GMI Cloud or Thinking Machines?
GMI Cloud
Choose GMI Cloud for
- APAC data residency for inference
- LLMs and video generation on one API
- A path from shared endpoints to reserved GPUs
Thinking Machines
Choose Thinking Machines for
- Custom LoRA training loops, including RL
- Specializing large open MoE models
- Evaluating Inkling in beta
GMI Cloud vs Thinking Machines at a glance
| Attribute | GMI Cloud | Thinking Machines |
|---|---|---|
| Model access | Open and third-party models | Open weights |
| Flagship models | GLM-4.7-Flash, Google Veo | Inkling, Inkling-Small |
| Speed | Near bare-metal performance | Unknown |
| Price | $0.07 in, $0.40 out (GLM-4.7-Flash) | Per 1M tokens by prefill, sample, train; Inkling $1.00 in, $4.05 out |
| Customization | Unknown | LoRA SFT and RL via Tinker |
| Deployment | Shared, autoscaling, reserved GPUs | Training API, beta serverless (Inkling only) |
| Long context | Varies by model | Inkling up to 1M; Tinker 32K–256K |
Frequently asked questions
What is the difference between GMI Cloud and Thinking Machines?
GMI Cloud owns NVIDIA hardware in the US and Asia and serves 100+ models. Thinking Machines is a research lab whose API trains open models more than it serves them.
When should I choose GMI Cloud over Thinking Machines?
APAC data residency for inference; LLMs and video generation on one API; A path from shared endpoints to reserved GPUs.
When should I choose Thinking Machines over GMI Cloud?
Custom LoRA training loops, including RL; Specializing large open MoE models; Evaluating Inkling in beta.
Is GMI Cloud or Thinking Machines cheaper?
GMI Cloud: $0.07 in, $0.40 out (GLM-4.7-Flash). Thinking Machines: Per 1M tokens by prefill, sample, train; Inkling $1.00 in, $4.05 out. The cheaper choice depends on the model and workload.
Which has more context, GMI Cloud or Thinking Machines?
GMI Cloud: Varies by model. Thinking Machines: Inkling up to 1M; Tinker 32K–256K.
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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.