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Subconscious vs SambaNova
SambaNova speeds up decode with custom chips. Subconscious speeds up long agents in software, cutting the work inside every step of a trace past 200K tokens.
By The Subconscious Team · Updated
Subconscious vs SambaNova: key differences
SambaNova's answer to slow agents is hardware. Its RDU maps the model graph onto the chip, and the SN50 generation pairs GPU prefill with RDU decode, running MiniMax M2.7 near 820 tokens per second in its fastest configuration. SambaNova claims SN50 supports 10M token contexts, though the hardware is still ramping and many headline numbers are vendor benchmarks. Subconscious attacks the same agent problem in software. Its runtime drops in for vLLM or SGLang, prunes the KV cache so each step processes less, and delivers 2x faster task completion, 50% to 80% lower cost than standard inference and a 5M+ effective context window. Because Subconscious's gains come from software, they run on standard GPUs today, with no new hardware to wait for.
The buying motion differs too. SambaNova delivers much of its value through racks sold to neoclouds and partnerships, with SambaCloud as a smaller self-serve surface for MiniMax M2.7, DeepSeek, Gemma 4 31B and GPT-OSS 120B. Its millisecond hot swapping suits agents that bounce between several models. Subconscious sells tokens through a managed API billed on processed tokens after compression, plus dedicated and on-prem deployments. SambaNova fits fast decode on large open models or a premium speed tier inside a data center. Subconscious fits cases where trace length itself drives the bill.
What Subconscious and SambaNova do
Subconscious
Subconscious is an MIT CSAIL spinout in Kendall Square that builds inference for long-horizon agents, the workloads where a single trace runs past 200K tokens and often into the millions. Its runtime drops in as a replacement for vLLM or SGLang. Instead of rereading an ever-growing context on every step, it prunes the KV cache and preserves suffix state, and Subconscious co-designs the runtime with post-trained model variants it calls Marathon. Against open models on standard inference, Subconscious delivers 2x faster task completion, delivers a 5M+ effective context window, cuts cost 50% and up to 80%, and scores neutral to 10% better on agentic benchmarks.
Example models: GLM 5.3, DeepSeek V4.1 Flash
Full Subconscious profileSambaNova
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 profileShould you choose Subconscious or SambaNova?
Subconscious
Choose Subconscious for
- Long traces where context growth, not decode, drives cost
- A software-only drop-in for vLLM or SGLang
- Billing on processed tokens after compression
SambaNova
Choose SambaNova for
- Fast decode on large open models like MiniMax M2.7
- Agents that hot swap between several models
- Neoclouds adding a premium speed tier in air-cooled racks
Subconscious vs SambaNova at a glance
| Attribute | Subconscious | SambaNova |
|---|---|---|
| Model access | Open weights | Open weights |
| Flagship models | GLM 5.3, DeepSeek V4.1 Flash | MiniMax M2.7, GPT-OSS 120B, DeepSeek |
| Speed | 2x faster task completion | ~820 tok/s on MiniMax M2.7 (SN50) |
| Price | 50–80% lower cost; billed on processed tokens | $0.22 in, $0.59 out (GPT-OSS 120B) |
| Customization | Marathon post-trained variants | Unknown |
| Deployment | Managed API, dedicated, on-prem | SambaCloud, racks for neoclouds |
| Long context | 5M+ effective context | Up to 192K (MiniMax M2.7) |
Frequently asked questions
What is the difference between Subconscious and SambaNova?
SambaNova speeds up decode with custom chips. Subconscious speeds up long agents in software, cutting the work inside every step of a trace past 200K tokens.
When should I choose Subconscious over SambaNova?
Long traces where context growth, not decode, drives cost; A software-only drop-in for vLLM or SGLang; Billing on processed tokens after compression.
When should I choose SambaNova over Subconscious?
Fast decode on large open models like MiniMax M2.7; Agents that hot swap between several models; Neoclouds adding a premium speed tier in air-cooled racks.
Is Subconscious or SambaNova cheaper?
Subconscious: 50–80% lower cost; billed on processed tokens. SambaNova: $0.22 in, $0.59 out (GPT-OSS 120B). The cheaper choice depends on the model and workload.
Which has more context, Subconscious or SambaNova?
Subconscious: 5M+ effective context. SambaNova: Up to 192K (MiniMax M2.7).
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Run your longest agent traces on Subconscious
Point the OpenAI or Anthropic SDK, or the coding agent you already use, at Subconscious. Keep SambaNova for the work it does best and send the long runs to us.