SambaNova
4.8Paid350
Editorial matchup · June 2026
SambaNova vs Tenstorrent: Which AI Tool Is Better in 2026?
Side-by-side comparison of SambaNova and Tenstorrent — pricing, features, and use cases. Reviewed by our editorial team in Jun 2026.
Use-case score 2–0Updated Jun 2026
AI/ML Models
Tenstorrent
4.5Paid145
SambaNova's SN50 RDU is designed specifically for large-scale agentic workloads, using Dataflow technology and three-tier memory architecture to reduce data movement, while Tenstorrent's Blackhole chip is built with new RISC-V cores and offers an infinitely scalable solution.
SambaNova's SN50 features 64GB of high-bandwidth memory, 432 MB of SRAM, and 256 GB to 2 TB of DDR5, allowing it to host AI models with up to 10 trillion parameters. In contrast, Blackhole includes 16 RISC-V CPU cores and 140 Tensix++ cores on a 6nm process, with 12x400 Gbps Ethernet.
Both platforms target inference as their primary use case, but with different architectural philosophies.
SambaNova's architecture is optimized for dataflow within and between chips for LLM and generative AI, supporting the composition of experts mechanism where multiple specialized models work on certain parts of data and tasks.
Tenstorrent, by contrast, bets on openness, building its AI processors on the open-standard RISC-V architecture with a completely open-source software stack. SambaNova claims its SN50 is 5x faster than competitive chips and offers 3x lower total cost of ownership for agentic workloads.
Tenstorrent's Galaxy Blackhole compute server integrates tensor processors, RISC-V CPUs, near-compute memory, and 400G networking in a single box using a 6nm process, GDDR6 RAM, direct-attach Ethernet, and air cooling.
Most TT-Metal documentation and verified model support as of Q1 2026 targets Wormhole n300 and n150 cards, with Blackhole software support earlier in the development cycle, whereas SambaNova pivoted in 2025 to become an AI cloud services provider with mature production deployments.
Agentic AI and Composition-of-Experts workloads
SambaNova
SambaNova's SambaStack switches between multiple frontier-scale models, enabling complex agentic AI workflows to execute end-to-end on one node with infrastructure flexibility.
Open-source and developer flexibility
Tenstorrent
Tenstorrent is all-in on a completely open-source software stack and open-standard RISC-V architecture, with full support through TT-Forge, TT-NN, TT-Metalium, and TT-LLK.
Dense LLM inference at maximum scale
SambaNova
SambaNova continues to be the only provider to offer high speed inference on the largest open-source models, with DeepSeek R1 671B running at 250 tokens per second per user.
Section 01
Best for what
4 use cases scored. SambaNova wins 2, Tenstorrent wins 0.
- EvenPricing value
Neither tool publishes a starting price.
- EvenFree tier
Neither tool offers a free tier or trial.
- SambaNovaUser ratings
SambaNova averages 4.8 / 5 vs 4.5 / 5 on the other side.
- SambaNovaReview volume
SambaNova has 161 ratings vs 146 on the other.
Section 02
Pros & cons
Where each tool earns its rating — and where it falls short.
SambaNova
AI/ML ModelsPros
- SambaNova can run multiple models simultaneously using its tiered memory architecture, allowing models to remain resident in memory and be switched quickly with minimal latency.
- Intel participated in SambaNova's latest Series E funding round and reportedly pursued acquisition, signaling enterprise confidence.
- The SN50 delivers 5X more compute and 4X more network bandwidth than the SN40 for improved cluster interconnect.
- SambaNova's capability to cache input tokens in memory reduces time-to-first-token relative to mainstream GPU architectures and can swap multiple AI models in a fraction of the time that Nvidia GPUs require.
- SambaNova achieves strong results without quantization techniques, meaning there is no loss of accuracy.
Cons
- Adopting SambaNova means adopting a full-stack, opinionated platform rather than a pluggable component.
- SambaNova Composer only supports architectures it can map to the RDU dataflow, excluding vLLM, SGLang, and custom attention kernels.
- Access to the hardware is through SambaNova Cloud's API, not through bare-metal SSH, limiting low-level control.
- While NVIDIA retains ~80-90% share of the broad AI chip market, SambaNova commands a high-value niche with narrower ecosystem maturity.
SambaNova
AI/ML ModelsPros
- SambaNova can run multiple models simultaneously using its tiered memory architecture, allowing models to remain resident in memory and be switched quickly with minimal latency.
- Intel participated in SambaNova's latest Series E funding round and reportedly pursued acquisition, signaling enterprise confidence.
- The SN50 delivers 5X more compute and 4X more network bandwidth than the SN40 for improved cluster interconnect.
- SambaNova's capability to cache input tokens in memory reduces time-to-first-token relative to mainstream GPU architectures and can swap multiple AI models in a fraction of the time that Nvidia GPUs require.
- SambaNova achieves strong results without quantization techniques, meaning there is no loss of accuracy.
Cons
- Adopting SambaNova means adopting a full-stack, opinionated platform rather than a pluggable component.
- SambaNova Composer only supports architectures it can map to the RDU dataflow, excluding vLLM, SGLang, and custom attention kernels.
- Access to the hardware is through SambaNova Cloud's API, not through bare-metal SSH, limiting low-level control.
- While NVIDIA retains ~80-90% share of the broad AI chip market, SambaNova commands a high-value niche with narrower ecosystem maturity.
Tenstorrent
AI InfrastructurePros
- Tenstorrent's AI processors are built on the open-standard RISC-V architecture with open-source support, avoiding vendor lock-in.
- Tenstorrent is led by legendary architect Jim Keller—best known for AMD's Zen processors and Tesla's self-driving chips.
- Tenstorrent raised extensive Series D funding from investors including Jeff Bezos at significant valuation, backing the technology.
- Blackhole features integrated RISC-V cores that can manage data movement without constant host CPU roundtrips.
- Blackhole supports native PCIe 5.0 backward-compatible with existing CUDA servers, allowing direct integration into heterogeneous clusters.
Cons
- Most TT-Metal documentation and verified model support as of Q1 2026 targets Wormhole cards, with Blackhole software support earlier in development.
- TT-Metal is open-source but not production-ready at the same maturity level as CUDA.
- Tenstorrent Galaxy hardware is not available on cloud marketplaces as of April 2026, requiring direct purchase.
- While Tenstorrent claims DeepSeek at 308 tokens per second with roadmap to 500, SambaNova already delivers 250+ tokens per second on production.
Section 03
At a glance
Every spec on one page. Live-pulled from each tool's detail page.
- PricingInquirePaid
- Pricing modelPaidPaid
- Free tierNoNo
- Free trialNoNo
- Rating4.8 / 5 (161 ratings)4.5 / 5 (146 ratings)
- Saves350145
- CategoriesAI/ML ModelsAI Infrastructure, Engineering & Simulation
- VerifiedYesNo
- Top 100 tier——
- Last updatedJun 2026Jun 2026
Frequently asked
SambaNova vs Tenstorrent FAQs
Quick answers to the questions readers ask before picking between these two.
Which platform is better for large language model inference?
SambaNova currently leads for large LLM inference, being the only provider offering high-speed inference on the largest open-source models like DeepSeek R1 671B at 250 tokens per second per user.
Do these chips require custom software development?
SambaNova Composer is proprietary and supports only architectures it can map to the dataflow, excluding vLLM, SGLang, and custom kernels. Tenstorrent offers a completely open-source software stack built on RISC-V standards.
Which chip is more power-efficient?
SambaNova's SN40L requires only 16 chips in a single air-cooled rack, delivering full utilization of 10.2 PFLOPs. Tenstorrent's Blackhole uses GDDR6, direct-attach Ethernet, and air cooling designed for lower operational costs.
Can I run both GPUs and these chips together?
Tenstorrent's Blackhole supports native PCIe 5.0 backward-compatible with existing CUDA servers, allowing direct integration into GPU-based infrastructure. SambaNova requires dedicated deployment through SambaCloud or on-premise SambaRack.
What is the current production status?
SambaNova pivoted in 2025 to become an AI cloud services provider with SoftBank deploying SN50 in next-generation AI data centers in Japan. Tenstorrent is in volume production with its Galaxy Blackhole compute server, though Blackhole software support is earlier in its development cycle.
How do these compare on inference latency for interactive applications?
SambaNova RDUs deliver low latency with high throughput for near-real-time AI agent inference. Tenstorrent's Blackhole addresses host CPU overhead for small-batch workloads that was a bottleneck on Wormhole.
Bottom line
SambaNova wins decisively for enterprises deploying agentic AI systems that rely on rapid model switching and composition-of-experts architectures. Its three-tier memory, dataflow design, and Intel partnership position it as production-ready for sovereign AI deployments and strict on-premise governance.
The SN50 launch and SoftBank deployment validate enterprise maturity. Tenstorrent is the better choice for organizations prioritizing open standards, developer flexibility, and avoiding vendor lock-in.
Its RISC-V foundation, fully open-source software stack, and heterogeneous computing appeal to teams building custom infrastructure or seeking maximum control over silicon roadmaps. Blackhole's launch signals maturation, though software ecosystem lags SambaNova.
For dense transformer inference at massive scale, SambaNova's proven 250+ tokens/second on 671B models and zero-quantization approach outpaces Tenstorrent's earlier-stage software ecosystem.
For teams embracing emerging but open technologies, Tenstorrent's leadership and ecosystem backing offer credible long-term optionality. Neither matches GPU flexibility, but both deliver superior inference efficiency per watt for their respective niches.
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