NVIDIA NIM vs SambaNova Cloud: Token Pricing, Speed & Intelligence
Full comparison of NVIDIA NIM and SambaNova Cloud — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.
NVIDIA NIM
Optimized inference microservices for NVIDIA GPUs — deploy any model on your own hardware
NVIDIA NIM (NVIDIA Inference Microservices) provides containerized, GPU-optimized inference microservices for deploying LLMs and other AI models on NVIDIA hardware. NIM packages models with TensorRT-LLM optimizations, enabling production-grade throughput on H100, A100, and RTX GPUs. Available via the NVIDIA API catalog for cloud access or as self-hosted containers for on-premises and private cloud deployments.
SambaNova Cloud
High-throughput inference on SambaNova RDU chips — gpt-oss-120b, GLM-5.3, Llama
SambaNova Cloud runs inference on SambaNova's proprietary Reconfigurable Dataflow Unit (RDU) chips, delivering very high throughput (800+ tokens/sec on gpt-oss-120b). The catalog covers gpt-oss-120b, GLM-5.3, MiniMax-M3, DeepSeek-V3.2, Meta-Llama-3.3-70B, and Gemma 4 31B. Pricing is PAYG with prompt caching on select models.
Key metric comparison
—
—
—
—
—
—
—
—
—
—
—
—
Live token pricing
Strengths and weaknesses
NVIDIA NIM
SambaNova Cloud
Key differentiators
An inference platform that lets you run the same TensorRT-LLM-optimized microservice both on NVIDIA's cloud API and on your own NVIDIA GPU infrastructure with consistent performance characteristics.
Proprietary RDU chips deliver industry-leading throughput on open-weight models at competitive per-token rates.
Frequently asked questions
NVIDIA NIM FAQs
What is NVIDIA NIM?
NVIDIA NIM (Inference Microservices) are containerized AI inference packages optimized for NVIDIA GPUs using TensorRT-LLM. They can be deployed on NVIDIA's cloud API catalog or self-hosted on any NVIDIA GPU infrastructure.
How do I access NVIDIA NIM models?
You can access NIM models via the NVIDIA API catalog at build.nvidia.com — free for prototyping. For production self-hosting, NIM containers are available through NVIDIA AI Enterprise licensing.
What models does NVIDIA NIM support?
NIM supports a broad catalog including Llama 4, Mistral, Qwen3, DeepSeek V3/R1, Nemotron, and NVIDIA's own optimized model variants. The catalog grows with each major open-weight model release.
SambaNova Cloud FAQs
What makes SambaNova Cloud different?
SambaNova runs inference on its own Reconfigurable Dataflow Unit (RDU) chips rather than NVIDIA GPUs. This delivers very high throughput — over 800 tokens/sec on gpt-oss-120b — at competitive per-token rates.
What models does SambaNova Cloud offer?
As of October 2026: gpt-oss-120b ($0.22/$0.59), GLM-5.3 ($1.40/$4.40, cached $0.14), MiniMax-M3 ($0.60/$2.40, cached $0.06), DeepSeek-V3.2 ($3.00/$4.50), Meta-Llama-3.3-70B ($0.60/$1.20), and Gemma 4 31B ($0.38/$1.15). See cloud.sambanova.ai/plans/pricing for current rates.
Does SambaNova offer prompt caching?
Yes, on GLM-5.3 (cached input $0.14/1M vs $1.40/1M standard) and MiniMax-M3 (cached input $0.06/1M vs $0.60/1M standard).
Provider resources
NVIDIA NIM — Optimized inference microservices for NVIDIA GPUs — deploy any model on your own hardware
NVIDIA NIM (NVIDIA Inference Microservices) provides containerized, GPU-optimized inference microservices for deploying LLMs and other AI models on NVIDIA hardware. NIM packages models with TensorRT-LLM optimizations, enabling production-grade throughput on H100, A100, and RTX GPUs. Available via the NVIDIA API catalog for cloud access or as self-hosted containers for on-premises and private cloud deployments.
An inference platform that lets you run the same TensorRT-LLM-optimized microservice both on NVIDIA's cloud API and on your own NVIDIA GPU infrastructure with consistent performance characteristics.
SambaNova Cloud — High-throughput inference on SambaNova RDU chips — gpt-oss-120b, GLM-5.3, Llama
SambaNova Cloud runs inference on SambaNova's proprietary Reconfigurable Dataflow Unit (RDU) chips, delivering very high throughput (800+ tokens/sec on gpt-oss-120b). The catalog covers gpt-oss-120b, GLM-5.3, MiniMax-M3, DeepSeek-V3.2, Meta-Llama-3.3-70B, and Gemma 4 31B. Pricing is PAYG with prompt caching on select models.
Proprietary RDU chips deliver industry-leading throughput on open-weight models at competitive per-token rates.
Key strengths compared
NVIDIA NIM
- TensorRT-LLM optimizations deliver maximum throughput on NVIDIA GPUs
- Self-hosted deployment option for air-gapped and private cloud environments
- Broad model catalog: Llama, Mistral, Qwen, DeepSeek, and NVIDIA-optimized variants
- Self-hosted path requires NVIDIA GPU infrastructure investment
- Cloud API catalog is primarily for evaluation — production at scale needs on-prem or cloud GPU contracts
SambaNova Cloud
- Proprietary RDU chips deliver very high throughput — 800+ tokens/sec on gpt-oss-120b
- Competitive pricing on gpt-oss-120b ($0.22/$0.59 per 1M)
- Prompt caching on GLM-5.3 (cached input at $0.14/1M)
- Smaller model catalog vs OpenRouter or Together AI
- No proprietary frontier models — inference-only platform
Key weaknesses compared
NVIDIA NIM
- Self-hosted path requires NVIDIA GPU infrastructure investment
- Cloud API catalog is primarily for evaluation — production at scale needs on-prem or cloud GPU contracts
- Less suited for teams without existing NVIDIA hardware
SambaNova Cloud
- Smaller model catalog vs OpenRouter or Together AI
- No proprietary frontier models — inference-only platform
- RDU architecture may behave differently from GPU inference for some workloads
Provider category context
NVIDIA NIM is a inference api, founded in 2024. SambaNova Cloud is a inference api, founded in 2017. Both are inference api providers — the comparison is primarily about pricing, model selection, and feature differentiation within the same tier.
How to choose between them
Both NVIDIA NIM and SambaNova Cloud host open-weight models. The key differentiators are latency, throughput, and which specific model versions each provider offers. Check the speed metrics above — inference API providers often differ significantly on tokens-per-second for the same model. Pricing is typically competitive between them; availability of specific model versions (e.g., Llama 3.1 405B, DeepSeek V3) may be the deciding factor.