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Groq vs General Compute
Groq and General Compute both serve open models on non-GPU chips. Groq builds its own LPU, while General Compute rents ASICs from several vendors.
By The Subconscious Team · Updated
Groq vs General Compute: key differences
Groq runs its own LPU and publishes 500 tokens per second on GPT-OSS 120B and 1,000 on GPT-OSS 20B, with tight tail latency, Whisper and the Groq Compound agent system. Its catalog is small and capped around 131K context. General Compute serves GPT-OSS 120B at $0.21 in and $0.79 out per million tokens and reports over 1,000 tokens per second on it in its own benchmark, using GPU prefill and decode on Cerebras, SambaNova, Positron AI and d-Matrix chips. It also carries MiniMax M2.7 with 192K context.
Groq is the pick for voice agents that pair Whisper with fast replies and for SLAs judged on tail latency, and its prices on small models sit near the floor. General Compute fits teams that want GPT-OSS 120B decode speed with custom LoRA or fine-tuned checkpoints, which Groq does not host. Groq has added NVIDIA GPUs since NVIDIA licensed the LPU, while General Compute spreads decode across several chip vendors.
What Groq and General Compute 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 3.8 27B
Full Groq 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 Groq or General Compute?
General Compute
Choose General Compute for
- Fine-tuned checkpoints on fast hardware
- GPT-OSS 120B at high decode speed
- MiniMax M2.7 with 192K context
Groq vs General Compute at a glance
| Attribute | Groq |  General Compute |
|---|---|---|
| Model access | Open weights | Open weights |
| Flagship models | GPT-OSS 120B, Qwen 3.8 27B | MiniMax M2.7, DeepSeek V3.2, GPT-OSS 120B |
| Speed | 500–1,000 tok/s | 1,000+ tok/s on GPT-OSS 120B (vendor) |
| Price | Near the floor on small models | $0.21–$0.28 in, $0.38–$1.20 out per 1M |
| Customization | No fine-tuned model hosting | Custom LoRA and fine-tuned checkpoints |
| Deployment | GroqCloud API | Serverless API, dedicated racks, bare metal |
| Long context | Around 131K max | 192K (MiniMax M2.7) |
Frequently asked questions
What is the difference between Groq and General Compute?
Groq and General Compute both serve open models on non-GPU chips. Groq builds its own LPU, while General Compute rents ASICs from several vendors.
When should I choose Groq over General Compute?
Voice agents with Whisper; Predictable tail latency; Cheap small-model calls.
When should I choose General Compute over Groq?
Fine-tuned checkpoints on fast hardware; GPT-OSS 120B at high decode speed; MiniMax M2.7 with 192K context.
Is Groq or General Compute cheaper?
Groq: Near the floor on small models. 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, Groq or General Compute?
Groq: Around 131K max. General Compute: 192K (MiniMax M2.7).
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