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Subconscious vs General Compute
General Compute speeds up decode by running it on ASICs. Subconscious speeds up long agent traces by cutting the context each step has to reread.
By The Subconscious Team · Updated
Subconscious vs General Compute: key differences
General Compute runs prefill on NVIDIA GPUs and decode on chips from Cerebras, SambaNova and others. In its own benchmark it reports over 1,000 output tokens per second on GPT-OSS 120B. Its serverless API serves MiniMax M2.7 with 192K context at $0.28 in and $1.20 out per million tokens, plus DeepSeek V3.2, V3.1 and GPT-OSS 120B at 32K to 128K context, and most of its dedicated rack capacity goes live in Q1 2027. Subconscious serves GLM 5.3 and DeepSeek V4.1 Flash on a runtime that prunes the KV cache and preserves suffix state. It delivers 2x faster task completion, 50% to 80% lower cost than standard inference and a 5M+ effective context window, and bills processed tokens rather than tokens sent.
General Compute is a good pick for short, output-heavy calls like voice or chat, where tokens per second is the wait. On coding agents that run past 200K tokens, the bottleneck moves to rereading context, which faster decode does not fix and which exceeds General Compute's largest window. Subconscious also speaks the Anthropic format, so it plugs into Claude Code as well as Codex and GitHub Copilot.
What Subconscious and General Compute do
Subconscious
Subconscious is an MIT CSAIL spinout in Kendall Square that builds inference for long-horizon agents, the workloads where a single trace runs past 200K tokens and often into the millions. Its runtime drops in as a replacement for vLLM or SGLang. Instead of rereading an ever-growing context on every step, it prunes the KV cache and preserves suffix state, and Subconscious co-designs the runtime with post-trained model variants it calls Marathon. Against open models on standard inference, Subconscious delivers 2x faster task completion, delivers a 5M+ effective context window, cuts cost 50% and up to 80%, and scores neutral to 10% better on agentic benchmarks.
Example models: GLM 5.3, DeepSeek V4.1 Flash
Full Subconscious profile
General Compute
General Compute is a San Francisco inference cloud built on alternative chips. It splits each request across hardware, with NVIDIA GPUs handling prefill and ASICs from Cerebras, SambaNova, Positron AI and d-Matrix handling decode. In its own open benchmark on GPT-OSS 120B it measured 1,025 to 1,234 output tokens per second against 100 to 121 on an unnamed GPU cloud, and up to 16.2x faster end to end. Founders Finn Puklowski and Jason Goodison raised a $15M seed led by FUSE, an up to $400M debt facility from Upper90 and, on October 27, 2026, a $100M Series A co-led by Blackbird and Abstract.
Example models: MiniMax M2.7, DeepSeek V3.2, GPT-OSS 120B
Full General Compute profileShould you choose Subconscious or General Compute?
Subconscious
Choose Subconscious for
- Coding agents past 200K tokens
- 5M+ effective context
- Newer open models like GLM 5.3
General Compute
Choose General Compute for
- Fast decode on short, output-heavy calls
- Voice and real-time chat
- Reserved ASIC racks under one contract
Subconscious vs General Compute at a glance
| Attribute | Subconscious |  General Compute |
|---|---|---|
| Model access | Open weights | Open weights |
| Flagship models | GLM 5.3, DeepSeek V4.1 Flash | MiniMax M2.7, DeepSeek V3.2, GPT-OSS 120B |
| Speed | 2x faster task completion | 1,000+ tok/s on GPT-OSS 120B (vendor) |
| Price | 50–80% lower cost; billed on processed tokens | $0.21–$0.28 in, $0.38–$1.20 out per 1M |
| Customization | Marathon post-trained variants | Custom LoRA and fine-tuned checkpoints |
| Deployment | Managed API, dedicated, on-prem | Serverless API, dedicated racks, bare metal |
| Long context | 5M+ effective context | 192K (MiniMax M2.7) |
Frequently asked questions
What is the difference between Subconscious and General Compute?
General Compute speeds up decode by running it on ASICs. Subconscious speeds up long agent traces by cutting the context each step has to reread.
When should I choose Subconscious over General Compute?
Coding agents past 200K tokens; 5M+ effective context; Newer open models like GLM 5.3.
When should I choose General Compute over Subconscious?
Fast decode on short, output-heavy calls; Voice and real-time chat; Reserved ASIC racks under one contract.
Is Subconscious or General Compute cheaper?
Subconscious: 50–80% lower cost; billed on processed tokens. General Compute: $0.21–$0.28 in, $0.38–$1.20 out per 1M. The cheaper choice depends on the model and workload.
Which has more context, Subconscious or General Compute?
Subconscious: 5M+ effective context. General Compute: 192K (MiniMax M2.7).
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Run your longest agent traces on Subconscious
Point the OpenAI or Anthropic SDK, or the coding agent you already use, at Subconscious. Keep General Compute for the work it does best and send the long runs to us.