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SambaNova vs Luminal
SambaNova serves large open models fast on its own dataflow chip. Luminal compiles models for speed on GPUs and ASICs without new hardware.
By The Subconscious Team · Updated
SambaNova vs Luminal: key differences
SambaNova runs MiniMax M2.7, GPT-OSS 120B and DeepSeek on its SN-series dataflow chips, reporting about 820 tokens per second on MiniMax M2.7, and sells racks to neoclouds. Luminal gets speed from software: a compiler that turns any model into native kernels for GPUs or ASICs ahead of time.
Both quote vendor benchmarks. SambaNova's GPT-OSS 120B costs $0.22 in and $0.59 out on SambaCloud. Luminal reports 36K tokens per second aggregate on the same model over 8 H100s but publishes no prices. SambaNova fits buyers wanting fast hosted decode or new hardware; Luminal fits teams staying on GPUs they already run.
What SambaNova and Luminal 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 profileLuminal
Luminal builds an inference compiler. Where vLLM and SGLang interpret a model at runtime, Luminal compiles it ahead of time into native kernels for GPUs and ASICs. Models get lowered to a small graph of 15 primitive ops, and the compiler searches over fusion, tiling, memory and scheduling choices instead of relying on hand-written rules, which it says can find optimizations like Flash Attention on its own. The compiler is open source in Rust under Apache 2.0 or MIT, runs on CUDA and Metal with ROCm on the roadmap, and works as a torch.compile backend.
Example models: GPT-OSS 120B, Llama 3 8B
Full Luminal profileShould you choose SambaNova or Luminal?
SambaNova
Choose SambaNova for
- Fast decode on large open models
- Published per-token prices on SambaCloud
- Dataflow racks for neoclouds
Luminal
Choose Luminal for
- Speeding up GPUs you already own
- Serving custom or fine-tuned architectures off any catalog
- An open-source engine teams can run on their own hardware
SambaNova vs Luminal at a glance
| Attribute | SambaNova | Luminal |
|---|---|---|
| Model access | Open weights | Bring your own weights |
| Flagship models | MiniMax M2.7, GPT-OSS 120B, DeepSeek | No public catalog |
| Speed | ~820 tok/s on MiniMax M2.7 (SN50) | 36K tok/s on GPT-OSS 120B, 8xH100 (vendor) |
| Price | $0.22 in, $0.59 out (GPT-OSS 120B) | Pay per use; rates not published |
| Customization | Unknown | Compiles any PyTorch or HF model |
| Deployment | SambaCloud, racks for neoclouds | Serverless (early access), on-prem license |
| Long context | Up to 192K (MiniMax M2.7) | Unknown |
Frequently asked questions
What is the difference between SambaNova and Luminal?
SambaNova serves large open models fast on its own dataflow chip. Luminal compiles models for speed on GPUs and ASICs without new hardware.
When should I choose SambaNova over Luminal?
Fast decode on large open models; Published per-token prices on SambaCloud; Dataflow racks for neoclouds.
When should I choose Luminal over SambaNova?
Speeding up GPUs you already own; Serving custom or fine-tuned architectures off any catalog; An open-source engine teams can run on their own hardware.
Is SambaNova or Luminal cheaper?
SambaNova: $0.22 in, $0.59 out (GPT-OSS 120B). Luminal: Pay per use; rates not published. The cheaper choice depends on the model and workload.
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