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SambaNova vs Wafer
Both sell faster open-model inference for coding agents. SambaNova gets there with custom silicon; Wafer with agent-tuned software on NVIDIA and AMD GPUs. Chip versus stack.
By The Subconscious Team · Updated
SambaNova vs Wafer: key differences
This is a real head-to-head on speed, with two different methods. SambaNova designs the Reconfigurable Dataflow Unit and pairs it with GPUs in a disaggregated setup, GPUs for prefill and RDUs for decode, claiming about 820 tokens per second on MiniMax M2.7 on SN50. Wafer keeps standard GPUs and tunes the software. Its agents search batching, decoding, quantization, engines and kernels, and Wafer reports Qwen 3.5 397B running 2.8x faster than stock SGLang, with GLM 5.1 and DeepSeek V4 Pro 2x faster than vLLM.
Pricing and deployment differ more than the goal. Wafer Pass is a flat subscription from $10 a week that drops into Claude Code, Cline and OpenHands, and dedicated deployments keep re-tuning as load changes, on NVIDIA or AMD. SambaNova sells SambaCloud access and racks to neoclouds, with millisecond model hot swapping. Both sets of speed numbers are self-reported. Wafer is very young with a small catalog, and SambaNova's newest hardware is still ramping, so benchmark both on your own traffic.
What SambaNova and Wafer 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 profileWafer
Wafer builds AI agents that act as GPU performance engineers, then sells inference on the stacks those agents tune. The company came out of Y Combinator's Summer 2025 batch as a "Cursor for CUDA" that turned slow PyTorch into custom kernels. Founders Emilio Andere and Steven Arellano are based in San Francisco. Its agents profile a workload, generate candidate configs across batching, decoding, quantization, engines, kernels and hardware, measure each one and deploy the winner.
Example models: Qwen 3.5 397B Turbo, GLM 5.1 Turbo
Full Wafer profileShould you choose SambaNova or Wafer?
SambaNova
Choose SambaNova for
- Fast decode backed by dedicated inference silicon.
- Agents that hot swap across several large models.
- Operators adding air-cooled racks.
Wafer
Choose Wafer for
- Flat weekly pricing for coding harnesses.
- Dedicated endpoints tuned continually to an SLO.
- Teams that want to hedge across NVIDIA and AMD.
SambaNova vs Wafer at a glance
| Attribute | SambaNova | Wafer |
|---|---|---|
| Model access | Open weights | Open weights |
| Flagship models | MiniMax M2.7, GPT-OSS 120B, DeepSeek | Qwen 3.5 397B Turbo, GLM 5.1 Turbo |
| Speed | ~820 tok/s on MiniMax M2.7 (SN50) | 2–2.8x vs stock vLLM or SGLang |
| Price | $0.22 in, $0.59 out (GPT-OSS 120B) | Wafer Pass from $10 a week |
| Customization | Unknown | Agent-tuned dedicated deployments |
| Deployment | SambaCloud, racks for neoclouds | Serverless pass, dedicated |
| Long context | Up to 192K (MiniMax M2.7) | Varies by model |
Frequently asked questions
What is the difference between SambaNova and Wafer?
Both sell faster open-model inference for coding agents. SambaNova gets there with custom silicon; Wafer with agent-tuned software on NVIDIA and AMD GPUs. Chip versus stack.
When should I choose SambaNova over Wafer?
Fast decode backed by dedicated inference silicon; Agents that hot swap across several large models; Operators adding air-cooled racks.
When should I choose Wafer over SambaNova?
Flat weekly pricing for coding harnesses; Dedicated endpoints tuned continually to an SLO; Teams that want to hedge across NVIDIA and AMD.
Is SambaNova or Wafer cheaper?
SambaNova: $0.22 in, $0.59 out (GPT-OSS 120B). Wafer: Wafer Pass from $10 a week. The cheaper choice depends on the model and workload.
Which has more context, SambaNova or Wafer?
SambaNova: Up to 192K (MiniMax M2.7). Wafer: Varies by model.
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.