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SambaNova vs Crusoe
SambaNova speeds up decode on its own dataflow chip. Crusoe speeds up prefill on GPUs by reusing KV cache across its cluster, and adds fine-tuning.
By The Subconscious Team · Updated
SambaNova vs Crusoe: key differences
The two attack opposite ends of a request. SambaNova's RDU maps the model graph onto the chip to cut trips to off-chip memory, and SambaNova reports its SN50 rack running MiniMax M2.7 near 820 tokens per second in its fastest configuration, on hardware still ramping. Crusoe runs NVIDIA and AMD GPUs and claims up to 9.9x faster time to first token and 5x throughput versus vLLM on prefix-heavy work through MemoryAlloy. Both lean heavily on vendor numbers. Both catalogs are small. SambaCloud serves MiniMax M2.7, DeepSeek, Gemma 4 31B and GPT-OSS 120B, the last at $0.22 in and $0.59 out. Crusoe serves DeepSeek, GLM 5.3, Kimi K2.6, Gemma, gpt-oss and Nemotron from $0.05 in and $0.20 out per million.
SambaNova's hardware hot swaps between models in milliseconds and supports input caching, useful for agents that bounce between models, and its air-cooled 20 kW racks fit existing data centers. Much of its business runs through rack sales to neoclouds. MiniMax M2.7 context tops out at 192K. Crusoe owns the layer below: it sources energy, builds data centers and reports over 6 GW of contracted capacity. It also offers what SambaNova's catalog lacks, including serverless LoRA fine-tuning, self-serve dedicated endpoints per GPU-hour and raw GB200, B200 and MI355X clusters. Pick SambaNova for fast interactive decode on large open models, and Crusoe for fine-tuning, repeated long prompts and GPU capacity for training.
What SambaNova and Crusoe do
SambaNova
SambaNova designs its own inference chip, the Reconfigurable Dataflow Unit, and sells fast tokens on large open models through SambaCloud. The RDU maps the model graph onto the chip to cut trips to off-chip memory. A three-tier memory design of SRAM, HBM and bulk DRAM lets one system host very large models and hot swap between several of them in milliseconds. SambaCloud serves models like MiniMax M2.7, DeepSeek, Gemma 4 31B and GPT-OSS 120B, with speeds reported by Artificial Analysis.
Example models: MiniMax M2.7, GPT-OSS 120B
Full SambaNova profileCrusoe
Crusoe started in 2018 turning wasted natural gas into power for computing and has since become a vertically integrated AI infrastructure company: it sources energy, builds data centers and rents GPUs through Crusoe Cloud. It designed and built the Abilene, Texas campus behind the OpenAI and Oracle Stargate project, planned at 1.2 GW, and in March 2026 announced an adjacent 900 MW campus for Microsoft. On September 17, 2026 it closed the first part of a $3.9B Series F at a $30.9B post-money valuation, and it reports over 6 GW of contracted capacity. Crusoe Cloud lists GB200 NVL72, B200 and AMD MI355X by quote, with H100 at $3.90 and H200 at $4.29 per GPU-hour on demand.
Example models: DeepSeek V4 Pro, GLM 5.3, Kimi K2.6
Full Crusoe profileShould you choose SambaNova or Crusoe?
SambaNova
Choose SambaNova for
- Fast decode on MiniMax M2.7 and other large open models
- Agents that hot swap between several models
- Neoclouds adding a premium speed tier
Crusoe
Choose Crusoe for
- Long repeated prompts where time to first token dominates
- LoRA fine-tuning of open models
- Training clusters on GB200 or AMD MI355X
SambaNova vs Crusoe at a glance
| Attribute | SambaNova | Crusoe |
|---|---|---|
| Model access | Open weights | Open weights |
| Flagship models | MiniMax M2.7, GPT-OSS 120B, DeepSeek | DeepSeek V4, GLM 5.3, Kimi K2.6, Nemotron 3 |
| Speed | ~820 tok/s on MiniMax M2.7 (SN50) | Up to 9.9x faster TTFT vs vLLM (vendor claim) |
| Price | $0.22 in, $0.59 out (GPT-OSS 120B) | $0.05–$1.74 in, $0.20–$4.40 out per 1M |
| Customization | Unknown | Serverless LoRA fine-tuning |
| Deployment | SambaCloud, racks for neoclouds | Serverless, self-serve and tailored dedicated, raw GPUs |
| Long context | Up to 192K (MiniMax M2.7) | Varies by model; cluster-wide KV cache |
Frequently asked questions
What is the difference between SambaNova and Crusoe?
SambaNova speeds up decode on its own dataflow chip. Crusoe speeds up prefill on GPUs by reusing KV cache across its cluster, and adds fine-tuning.
When should I choose SambaNova over Crusoe?
Fast decode on MiniMax M2.7 and other large open models; Agents that hot swap between several models; Neoclouds adding a premium speed tier.
When should I choose Crusoe over SambaNova?
Long repeated prompts where time to first token dominates; LoRA fine-tuning of open models; Training clusters on GB200 or AMD MI355X.
Is SambaNova or Crusoe cheaper?
SambaNova: $0.22 in, $0.59 out (GPT-OSS 120B). Crusoe: $0.05–$1.74 in, $0.20–$4.40 out per 1M. The cheaper choice depends on the model and workload.
Which has more context, SambaNova or Crusoe?
SambaNova: Up to 192K (MiniMax M2.7). Crusoe: Varies by model; cluster-wide KV cache.
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.