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GMI Cloud vs Wafer
GMI Cloud sells owned hardware and a broad multimodal catalog; Wafer sells agent-tuned open-model stacks and per-token access for coding agents.
By The Subconscious Team · Updated
GMI Cloud vs Wafer: key differences
Both companies claim a speed edge from engineering below the model. GMI says its in-house Cluster Engine runs near bare metal and recovers the 10 to 15% overhead of standard cloud virtualization. Wafer uses AI agents to tune batching, quantization, engines and kernels per workload, and reports its tuned Qwen 3.5 397B at 2.8x stock SGLang, with GLM 5.1 and DeepSeek V4 Pro each 2x a vLLM baseline. Both sets of numbers are self-reported and neither company has much third-party benchmarking, so test before committing.
Beyond speed they diverge. GMI owns NVIDIA hardware across the US and APAC and serves 200+ models, including video, image and audio, with a path to reserved GPUs. Wafer is very young, with a small hosted catalog of big open models, and it runs on NVIDIA or AMD. Its serverless API bills per token for Claude Code, Cline and OpenHands users. Choose GMI for multimodal breadth and APAC residency. Choose Wafer for coding agents on large open models or a dedicated endpoint re-tuned against a strict latency SLO.
What GMI Cloud and Wafer 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, B200 and GB200 supply, with GB300 on pre-order. In March 2026 it announced a 1GW sovereign AI factory in Kagoshima, Japan. The company pivoted from crypto mining into AI, which gave it experience standing up high-density power and cooling fast. In September 2026 it raised $668M: a $223M Series B led by ARCHIV with NVIDIA participating, plus a $445M credit facility led by CTBC. It reports over $600M in contracted ARR and about 4T tokens a week on its inference platform.
Example models: DeepSeek V4 Flash, GLM-5.2, Google Veo
Full GMI Cloud 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. Wafer raised a $4M seed led by Fifty Years in April 2026 and a $40M Series A co-led by Marathon and Chemistry in September 2026 at a $220M post-money valuation, with AMD Ventures and Wing joining. 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: GLM-5.3, DeepSeek V4.1 Flash
Full Wafer profileShould you choose GMI Cloud or Wafer?
GMI Cloud
Choose GMI Cloud for
- Multimodal apps across text, image, video and audio
- In-country hosting in Taiwan, Thailand or Malaysia
- Owned hardware with reserved H100 or H200 options
Wafer
Choose Wafer for
- Per-token access for agentic coding tools
- Large open models tuned for interactive speed
- Hedging GPU supply across NVIDIA and AMD
GMI Cloud vs Wafer at a glance
| Attribute | GMI Cloud | Wafer |
|---|---|---|
| Model access | Open and third-party models | Open weights |
| Flagship models | DeepSeek V4 Flash, GLM-5.2, Google Veo | GLM-5.3, DeepSeek V4.1 Flash |
| Speed | Near bare-metal performance | ~168 tok/s on GLM-5.3; 2–2.8x vs stock vLLM or SGLang (vendor) |
| Price | H100 from $2.60/GPU-hr | Per token, prepaid credits |
| Customization | Unknown | Agent-tuned dedicated deployments |
| Deployment | Shared, autoscaling, reserved GPUs | Serverless, dedicated |
| Long context | Varies by model | Up to 1M (GLM-5.3, DeepSeek V4.1 Flash) |
Frequently asked questions
What is the difference between GMI Cloud and Wafer?
GMI Cloud sells owned hardware and a broad multimodal catalog; Wafer sells agent-tuned open-model stacks and per-token access for coding agents.
When should I choose GMI Cloud over Wafer?
Multimodal apps across text, image, video and audio; In-country hosting in Taiwan, Thailand or Malaysia; Owned hardware with reserved H100 or H200 options.
When should I choose Wafer over GMI Cloud?
Per-token access for agentic coding tools; Large open models tuned for interactive speed; Hedging GPU supply across NVIDIA and AMD.
Is GMI Cloud or Wafer cheaper?
GMI Cloud: H100 from $2.60/GPU-hr. Wafer: Per token, prepaid credits. The cheaper choice depends on the model and workload.
Which has more context, GMI Cloud or Wafer?
GMI Cloud: Varies by model. Wafer: Up to 1M (GLM-5.3, DeepSeek V4.1 Flash).
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.