SambaNova Pricing & Developer Hub
Everything you need to know about SambaNova pricing in 2026: platform fees, serverless quotas, compute instance rates, and bill optimization strategies.
Hardware Architecture: Reconfigurable Dataflow Units (RDUs) vs GPUs
SambaNova Systems is pioneering a non-von-Neumann computing architecture for artificial intelligence. Rather than relying on traditional SIMD (Single Instruction, Multiple Data) graphics processing units (GPUs) that are fundamentally bottlenecked by memory bus bandwidth, SambaNova engineered the SN40L Reconfigurable Dataflow Unit (RDU).
RDUs map complete deep learning neural network computation graphs directly onto silicon tiles of reconfigurable memory and compute units. By streaming model activations through on-chip memory pipelines without intermediate DRAM round-trips, SambaNova achieves the world's fastest open-source LLM inference speeds: generating over 450 tokens/second on Llama-3.3-70B and over 140 tokens/second on frontier 405B models.
SambaNova Cloud API Pricing (March 2026)
| Hosted Model | Inference Speed | Input Price / 1M | Output Price / 1M | Latency Advantage |
|---|---|---|---|---|
| Llama 3.3 70B Instruct | 460 tokens / sec | $0.60 / 1M | $1.20 / 1M | 10x faster than standard GPUs |
| Llama 3.1 405B Instruct | 142 tokens / sec | $5.00 / 1M | $10.00 / 1M | Instant interactive 405B generation |
| Qwen 2.5 72B Instruct | 420 tokens / sec | $0.60 / 1M | $1.20 / 1M | High-speed multi-lingual reasoning |
Production Use Case: Real-Time Agentic Workflows
Multi-step AI agent workflows (where an agent must plan, reflect, call external database tools, and synthesize answers across 5 to 10 sequential LLM calls) are notoriously sluggish on conventional GPU cloud APIs, often requiring 15 to 30 seconds per query. At 450 tokens/second on SambaNova, that entire 10-step agent loop completes in under 2.5 seconds, making real-time voice and conversational interfaces commercially viable.
Frequently Asked Questions
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SambaNova SN40L Reconfigurable Dataflow Architecture: 460 Tok/s Llama 70B
SambaNova Systems bypasses standard GPU SIMD computing bottlenecks using custom **Reconfigurable Dataflow Units (RDUs)**. While GPUs waste execution cycles dispatching instructions to fixed arithmetic units, SambaNova's SN40L chip physically configures its silicon array into a hardware dataflow graph that matches the neural network's architecture. On Llama 3.3 70B, SambaNova delivers sustained generation speeds of **460 tokens per second** at competitive pricing: **$0.60 per million input tokens** and **$1.20 per million output tokens**, with full 128k context support.
SambaNova SN40L Performance & Pricing Matrix
| Model Deployment | Silicon Architecture | Input ($/M) | Output ($/M) | Generation Speed | Context Window |
|---|---|---|---|---|---|
| Meta Llama 3.3 70B Instruct | SambaNova SN40L RDU | $0.60 | $1.20 | 460 tok/sec | 128,000 tokens |
| Meta Llama 3.1 8B Instruct | SambaNova SN40L RDU | $0.10 | $0.20 | 650 tok/sec | 128,000 tokens |
| Qwen 2.5 72B Instruct | SambaNova SN40L RDU | $0.60 | $1.20 | 420 tok/sec | 128,000 tokens |
| Traditional Nvidia H100 | GPU Tensor Cores (vLLM) | $0.13 | $0.40 | 85 tok/sec | Variable context |
SambaNova High-Throughput OpenAI-Compatible Client (Python)
from openai import OpenAI
client = OpenAI(
base_url="https://api.sambanova.ai/v1",
api_key=os.environ["SAMBANOVA_API_KEY"]
)
# Stream completion at 460 tokens/sec for real-time document extraction
completion = client.chat.completions.create(
model="Meta-Llama-3.3-70B-Instruct",
messages=[{"role": "user", "content": "Extract all technical specifications from the attached report."}],
stream=True
)
for chunk in completion:
print(chunk.choices[0].delta.content or "", end="", flush=True)Engineering Q&A: SambaNova Dataflow Architecture
How does an RDU differ conceptually from a GPU?
A GPU executes software instructions sequentially on fixed hardware units. An RDU configures its silicon switches into a direct spatial representation of the neural network graph, allowing data tokens to flow through hardware pipes continuously without CPU instruction fetch overhead.
What is the primary use case for 460 tokens/sec throughput?
High-speed conversational voice pipelines, automated coding autocomplete loops, and multi-agent synthetic evaluation pipelines where generating long tokens at 80 tok/s causes human cognitive friction.
Hardware Spatial Mapping & Pipeline Parallelism on SN40L
The architectural breakthrough of SambaNova's SN40L chip lies in its 3-tier memory system: on-chip SRAM for ultra-fast activations, on-package high-bandwidth memory (HBM) for model weights, and terabytes of host DDR memory for massive context storage. By spatially mapping distinct transformer layers across physical RDU tiles, tokens stream through the pipeline in continuous hardware waves, achieving 460 tok/sec on Llama 3.3 70B without the multi-millisecond synchronization delays inherent in GPU tensor parallelism.
SambaNova Enterprise SRE Guardrails
- High-Throughput Stream Buffering: When streaming responses at 460+ tokens per second, ensure Node.js and Python web servers use non-blocking buffered socket writes. Slow client connections can cause backpressure that stalls server memory queues.
- Context Window Utilization: Take advantage of SambaNova's native 128k context support. Large document analysis and legal contracts process in a single generation pass without requiring complex RAG chunking pipelines.
Does SambaNova support fine-tuned custom model weights?
Yes. Enterprise customers can compile custom fine-tuned open-source checkpoints into SambaNova dataflow binaries, deploying private model endpoints with guaranteed dedicated throughput.
How does SambaNova API pricing compare to Groq?
On Llama 3.3 70B, SambaNova charges $0.60/M input and $1.20/M output for 460 tok/s. Groq charges $0.59/M input and $0.79/M output for 280 tok/s. SambaNova offers higher raw tokens-per-second generation speeds on 70B models.
SambaNova Dataflow Graph Compilation & Token Memory Efficiency
SambaNova's software stack compiles high-level PyTorch models directly into hardware dataflow execution graphs using the SambaFlow compiler. The compiler analyzes the entire model architecture, allocating attention heads and MLP layers across physical RDU tiles while eliminating intermediate CPU memory transfers. This hardware-software co-design allows models to process long 128k context windows without the quadratic memory expansion penalties that cause traditional GPU clusters to run out of memory.
High-Volume Enterprise Tip: SambaNova provides dedicated enterprise racks for on-premise installation. Organizations can deploy SN40L dataflow hardware inside their private datacenters for air-gapped sovereign AI generation.