Fireworks AI vs General Compute

Fireworks sells fast GPU inference and post-training on 400+ models. General Compute bets on ASIC decode for speed on a four-model catalog.

By The Subconscious Team · Updated October 27, 2026

Fireworks AI vs General Compute: key differences

Fireworks posts 167 to 174 tokens per second on DeepSeek V4 Pro in third-party tests, serves 400+ models with full 1M context on DeepSeek V4.1 Flash and GLM 5.3, and runs SFT, DPO and reinforcement fine-tuning with fine-tunes served at base price. General Compute hands decode to ASICs from Cerebras, SambaNova, Positron AI and d-Matrix and reports over 1,000 tokens per second on GPT-OSS 120B in its own benchmark. Its catalog is MiniMax M2.7, DeepSeek V3.2, DeepSeek V3.1 and GPT-OSS 120B at $0.21 to $0.28 in per million tokens, with context up to 192K.

Fireworks is the pick for current open models, long context and post-training an open model to beat a closed API. General Compute fits latency-bound loops on the models it carries, and it serves LoRA or full fine-tuned checkpoints, though it has no training service of its own. Its speed figures are self-reported, while Fireworks has third-party numbers.

What Fireworks AI and General Compute do

Fireworks AI

Fireworks AI was founded in 2022 by former Meta PyTorch engineers led by CEO Lin Qiao, and it sells speed on open models. Its custom serving stack has posted 167 to 174 tokens per second on DeepSeek V4 Pro in third-party measurements, several times what most GPU peers hit on the same model. The catalog holds 400+ models across text, vision, audio and embeddings, served through an OpenAI-compatible API. In July 2026 it raised a $1.505B Series D at a $17.5B valuation, with a reported $1B+ run rate and 40T+ tokens a day.

Example models: DeepSeek V4.1 Flash, GLM 5.3, Kimi K3

Full Fireworks AI 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 profile

Should you choose Fireworks AI or General Compute?

Fireworks AI

Choose Fireworks AI for

Current open models with 1M context
SFT, DPO and RL fine-tuning
Fine-tunes served at base price

General Compute

Choose General Compute for

Decode speed on GPT-OSS 120B
Hosting checkpoints trained elsewhere
Reserved ASIC racks

Fireworks AI vs General Compute at a glance

Attribute	Fireworks AI	General Compute
Model access	Open weights	Open weights
Flagship models	DeepSeek V4.1 Flash, GLM 5.3, Kimi K3	MiniMax M2.7, DeepSeek V3.2, GPT-OSS 120B
Speed	167–174 tok/s on DeepSeek V4 Pro	1,000+ tok/s on GPT-OSS 120B (vendor)
Price	Fine-tunes served at base price	$0.21–$0.28 in, $0.38–$1.20 out per 1M
Customization	SFT, DPO, RFT; Training API	Custom LoRA and fine-tuned checkpoints
Deployment	Serverless, dedicated GPUs	Serverless API, dedicated racks, bare metal
Long context	Full 1M on DeepSeek V4.1 Flash, GLM 5.3	192K (MiniMax M2.7)

Frequently asked questions

What is the difference between Fireworks AI and General Compute?

Fireworks sells fast GPU inference and post-training on 400+ models. General Compute bets on ASIC decode for speed on a four-model catalog.

When should I choose Fireworks AI over General Compute?

Current open models with 1M context; SFT, DPO and RL fine-tuning; Fine-tunes served at base price.

When should I choose General Compute over Fireworks AI?

Decode speed on GPT-OSS 120B; Hosting checkpoints trained elsewhere; Reserved ASIC racks.

Is Fireworks AI or General Compute cheaper?

Fireworks AI: Fine-tunes served at base price. 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, Fireworks AI or General Compute?

Fireworks AI: Full 1M on DeepSeek V4.1 Flash, GLM 5.3. General Compute: 192K (MiniMax M2.7).

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.

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