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SambaNova vs Boundless
SambaNova sells fast decode on large open models from its own RDU chip. Boundless serves open models plus GPT and Claude and tunes reserved capacity.
By The Subconscious Team · Updated
SambaNova vs Boundless: key differences
SambaNova designs the RDU chip and serves MiniMax M3 in preview with 1M context, MiniMax M2.7, DeepSeek, Gemma 4 31B and GPT-OSS 120B on SambaCloud, with GPT-OSS 120B at $0.22 in and $0.59 out per million tokens. SemiAnalysis measured about 800 tokens per second on MiniMax M2.7 on an SN50 rack, and SambaNova also sells racks to neoclouds. Boundless serves MiniMax M3 at $0.28 in and $1.10 out and MiniMax M3.1 Flash at $0.15 in and $0.60 out, both with 1M context, beside DeepSeek, GLM, Kimi and resold GPT and Claude models. Vercel measured DeepSeek V4.1 Flash 1017 on Boundless at 342 tokens per second.
SambaNova is the pick for fast decode on large open models and for neoclouds that want SN50 hardware of their own. Boundless fits teams that want open and closed models on one OpenAI and Anthropic compatible API with zero data retention, or a reserved deployment its engineers tune against latency and uptime targets after a free two-week trial.
What SambaNova and Boundless 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 M3 (in preview with a 1M context), MiniMax M2.7, DeepSeek, Gemma 4 31B and GPT-OSS 120B, with speeds reported by Artificial Analysis.
Example models: MiniMax M3, MiniMax M2.7, GPT-OSS 120B
Full SambaNova profileBoundless
Boundless launched in September 2026 as an inference partner for AI-native startups. It sells two things. The Model API serves pre-tuned models on shared capacity, billed per token from prepaid credits with a budget per API key. Tailored inference reserves capacity for one customer, and Boundless engineers pick the model on the customer’s own evals, tune precision, batching, caching, speculative decoding and parallelism, then run the deployment against uptime, throughput and latency targets written into the contract. A free two-week evaluation tests those targets on real traffic against the customer’s current provider first.
Example models: GLM-5.3, DeepSeek V4.1 Flash, Kimi K3
Full Boundless profileShould you choose SambaNova or Boundless?
SambaNova vs Boundless at a glance
| Attribute | SambaNova | Boundless |
|---|---|---|
| Model access | Open weights | Open weights, plus closed GPT and Claude |
| Flagship models | MiniMax M3, MiniMax M2.7, GPT-OSS 120B | GLM-5.3, DeepSeek V4.1 Flash, Kimi K3 |
| Speed | ~800 tok/s on MiniMax M2.7 (SN50, SemiAnalysis) | 342 tok/s on DeepSeek V4.1 Flash 1017 (Vercel) |
| Price | $0.22 in, $0.59 out (GPT-OSS 120B) | $0.04–$2.30 in, $0.21–$11.40 out per 1M (open) |
| Customization | Unknown | Serving-stack tuning, no fine-tuning |
| Deployment | SambaCloud, racks for neoclouds | Serverless API, reserved tailored capacity |
| Long context | Up to 1M (MiniMax M3, preview) | 1M context on most models |
Frequently asked questions
What is the difference between SambaNova and Boundless?
SambaNova sells fast decode on large open models from its own RDU chip. Boundless serves open models plus GPT and Claude and tunes reserved capacity.
When should I choose SambaNova over Boundless?
Fast decode on MiniMax M2.7; GPT-OSS 120B at $0.22 in; SN50 racks for neoclouds.
When should I choose Boundless over SambaNova?
Kimi K3 and GLM-5.3 on one API; GPT and Claude resold; Free two-week trial on real traffic.
Is SambaNova or Boundless cheaper?
SambaNova: $0.22 in, $0.59 out (GPT-OSS 120B). Boundless: $0.04–$2.30 in, $0.21–$11.40 out per 1M (open). The cheaper choice depends on the model and workload.
Which has more context, SambaNova or Boundless?
SambaNova: Up to 1M (MiniMax M3, preview). Boundless: 1M context on most models.
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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.