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SambaNova Systems
3.3
based on 3 Reviews
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About SambaNova Systems
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SambaNova provides advanced AI capabilities that help organizations explore the universe, find cures for cancer, and gain insights for a competitive edge. The company focuses on delivering best-in-class software solutions within SambaStudio, enabling enterprises to achieve the transformative promise of AI technologies with a fully integrated hardware-software system. This system delivers innovation across the full AI stack, including the most accurate generative AI models optimized for enterprise and government.
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SambaNova Systems Ratings
based on 3 reviews
Overall Rating
3.3/5
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4
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3
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Category Ratings
4.0
Salary
3.7
Work-life balance
3.4
Work satisfaction
3.1
Company culture
3.0
Skill development
3.0
Promotions
2.7
Job security
SambaNova Systems is rated 3.3 out of 5 stars on AmbitionBox, based on 3 company reviews.This rating reflects an average employee experience, indicating moderate satisfaction with the company’s work culture, benefits, and career growth opportunities. AmbitionBox gathers authentic employee reviews and ratings, making it a trusted platform for job seekers and employees in India.
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SambaNova Systems Reviews
SambaNova Systems Salaries
SambaNova Systems salaries have received with an average score of 4.0 out of 5 by 3 employees.
Principal Engineer
(4 salaries)
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₹44 L/yr - ₹1.1 Cr/yr
Principal Software Engineer
(4 salaries)
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₹58 L/yr - ₹68 L/yr
Product Manager
(4 salaries)
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₹36.9 L/yr - ₹47.2 L/yr
Lead Consultant
(2 salaries)
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₹37.8 L/yr - ₹48.3 L/yr
Senior Software Engineer
(2 salaries)
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₹61.6 L/yr - ₹68 L/yr
Senior Product Manager
(2 salaries)
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₹47.5 L/yr - ₹52.5 L/yr
TPM
(1 salaries)
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₹40.5 L/yr - ₹51.8 L/yr
Senior Python Developer
(1 salaries)
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₹23.4 L/yr - ₹29.9 L/yr
Senior Accountant
(1 salaries)
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₹23.4 L/yr - ₹29.9 L/yr
Senior Principal Engineer
(1 salaries)
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₹96 L/yr - ₹1.1 Cr/yr
SambaNova Systems Jobs
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Director Enterprise Sales
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SambaNova Systems News
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Nvidia's competitors are gaining traction in these key industries
- Startups are challenging Nvidia's dominance in the AI chip market, particularly for high-frequency trading and sovereign AI projects.
- Efforts are being made to find more efficient and cost-effective solutions compared to Nvidia's expensive chips with immense power requirements.
- While Nvidia still holds a dominant market share, competitors claim superior performance, speed, energy efficiency, and cost advantages.
- Industries such as high-frequency trading and ad targeting are turning to alternative chip architectures for specific workloads.
- Firms like SambaNova Systems and Etched are entering the chips market with offerings tailored for high-value use cases, such as transformer models for chatbots and private computations for HFT firms.
- AI chips are becoming crucial for sectors like ad targeting, content recommendations, and sovereign clouds, prompting diversification away from Nvidia.
- Companies investing in AI tools are also exploring alternatives like AMD's GPUs and newer entrants like Cerebras, SambaNova Systems, and Qualcomm.
- The competition in the inference market is intensifying, with startups focusing on offering their own data centers for inference services alongside chip sales.
- Despite the potential advantages of non-Nvidia chips in terms of efficiency and cost savings, companies are hesitant due to the risk of commitment and the flexibility of GPU technology.
- The evolving market dynamics and varying demands across industries present both challenges and opportunities for Nvidia's competitors in the AI chip sector.
Insider | 8 Jun, 2025
Kubernetes Native llm-d Could Be a ‘Turning Point in Enterprise AI’ for Inferencing
- Red Hat AI introduced llm-d, a Kubernetes-native distributed inference framework to address challenges in deploying AI models in production-ready environments.
- Developed in collaboration with tech giants like Google Cloud, IBM Research, NVIDIA, and others, llm-d optimizes AI model serving in demanding environments with multiple GPUs.
- llm-d's architecture includes techniques like Prefill and Decode Disaggregation and KV Cache Offloading to boost efficiency and reduce memory usage on GPUs.
- With Kubernetes-powered clusters and controllers, llm-d achieved significantly faster response times and higher throughput compared to baselines in NVIDIA H100 clusters.
- Google Cloud reported 2x improvements in time-to-first-token with llm-d for use cases like code completion, enhancing application responsiveness.
- llm-d features AI-aware network routing, supports various hardware like NVIDIA, Google TPU, AMD, and Intel, and aids in efficient scaling of AI inference.
- Industry experts believe llm-d by Red Hat could mark a turning point in Enterprise AI by enhancing production-grade serving patterns using Kubernetes and vLLM.
- Companies focus on scaling AI inference solutions, with efforts from hardware providers like Cerebras, Groq, and SambaNova aiming to accelerate AI inference in data centers.
- Recent research efforts have also been made in software frameworks and architectures to optimize AI inference, with advancements in reducing pre-fill compute and improving serving throughput.
- A study by Huawei Cloud and Soochow University reviewed efficient LLM inference serving methods at the instance level and cluster level, addressing various optimization techniques.
- vLLM introduced a 'Production Stack' for Kubernetes native deployment, focusing on distributed KV Cache sharing and intelligent autoscaling to reduce costs and improve response times.
Analyticsindiamag | 30 May, 2025
A guide to Nvidia's competitors: AMD, Qualcomm, Broadcom, startups, and more are vying to compete in the AI chip market
- Nvidia dominates the AI semiconductor market due to early investment in GPUs for AI and the development of CUDA software.
- Competitors like AMD, Qualcomm, Broadcom, and startups are challenging Nvidia with new AI chip designs and strategies.
- Nvidia has over 80% market share in AI chips inside data centers, powering products like ChatGPT and Claude.
- Nvidia's early lead in AI computing stemmed from GPUs originally used for complex video games before expanding into AI.
- AMD, led by CEO Lisa Su, is Nvidia's primary competitor in the AI computing market, with the MI300 GPU for data centers.
- Application-specific integrated circuits (ASICs) from companies like Broadcom and Marvell are challenging GPUs with cost-effective customization for AI workloads.
- Major cloud providers like Amazon and Google are developing their own AI chips to compete with Nvidia, offering alternatives for AI workloads.
- Intel's AI chip line, Gaudi, competes with Nvidia and AMD, while Huawei poses a significant AI chip challenge from China.
- Startups like Cerebras, Groq, and Sambanova Systems are introducing new chip designs and business models to the AI computing market.
- Despite Nvidia's AI chip dominance, competitors are innovating rapidly to close the gap and offer competitive solutions in the evolving market.
Insider | 11 May, 2025
Ironwood is Google’s Answer to the GPU Crunch
- AI's demand for GPUs has surged, prompting Google to introduce Ironwood, its seventh-generation TPU designed for inference purposes.
- Ironwood is optimized for AI models that actively search, interpret, and generate insights, offering performance and scalability without compromise.
- Google Cloud customers will have access to Ironwood later this year, supporting advanced models like Gemini 2.5 Pro and AlphaFold.
- Ironwood is engineered to handle the complex needs of thinking models with large-scale tensor operations, delivering high compute performance.
- Each Ironwood chip provides 4,614 TFLOPs of peak compute and is power-efficient with liquid cooling, ensuring consistent performance.
- Google's investment in TPUs and custom chips has shown efficiency and productivity in managing data center needs, despite being a major NVIDIA customer.
- Google's potential collaboration with MediaTek to build next-gen TPUs could offer cost benefits, enhancing chip availability.
- AWS is also developing its own chips like Trainium2 and Graviton4, marking a trend of leading cloud providers investing in custom hardware solutions.
- Competition in the chip market includes startups like Groq, Cerebras Systems, and SambaNova Systems, challenging NVIDIA's dominance.
- OpenAI is progressing towards developing custom AI chips to reduce reliance on NVIDIA, with plans to finalize its first in-house chip design.
Analyticsindiamag | 9 Apr, 2025
What Was Former Intel CEO Doing at NVIDIA’s Flagship Event?
- Former Intel CEO, Pat Gelsinger, made a surprising appearance at NVIDIA's GTC 2025 event.
- Gelsinger participated in a panel discussion at the event and shared strong opinions on quantum computing and GPU usage.
- He believes data centers of the future will include CPUs, GPUs, and quantum processing units (QPUs).
- Gelsinger disagreed with NVIDIA CEO Jensen Huang's timeline for quantum computing, stating it could be achieved in a few years.
- He expressed the need for quantum computing to tackle complex human-related problems and innovations.
- Gelsinger also criticized the use of GPUs for AI model inference, suggesting a need for more optimized solutions.
- He highlighted the emergence of inference-specific hardware from companies like Groq, Cerebras, and SambaNova.
- Despite differing views, Gelsinger remains a fan of Jensen Huang and NVIDIA's achievements.
- Gelsinger rejected the idea that GPUs are ideal for AI inference due to cost inefficiencies and the need for optimization.
- In conclusion, Gelsinger's presence at the event showcased his strong opinions and continued admiration for NVIDIA's work.
Analyticsindiamag | 20 Mar, 2025
Nvidia won the AI training race, but inference is still anyone's game
- Nvidia has dominated AI training clusters, but the inference battle is still open, except for custom cloud silicon.
- AI compute has been focused on training rather than inference, but this is expected to shift as models improve.
- Inference performance depends on memory capacity, bandwidth, and compute, varying based on model architecture.
- Companies like AMD, Meta, and Microsoft are exploring diverse hardware options to optimize inference workloads.
- Startups like Cerebras, SambaNova, and Groq prioritize speed in inference with innovative chip architectures.
- Fast inference is crucial as models evolve, leading to the development of chips like d-Matrix's Corsair accelerators.
- Vendors like Hailo AI, EnCharge, and Axelera are creating low-power, high-performance chips for edge and PC markets.
- Established chipmakers are integrating NPUs into SoCs, while cloud providers continue investing in Nvidia hardware.
- Nvidia remains a major player in AI infrastructure, gearing up for large-scale inference deployments with its latest GPUs.
- The introduction of Nvidia's GB200 NVL72 with enhanced compute capabilities paves the way for improved throughput in models like GPT-4.
- Nvidia's next-gen Blackwell-Ultra platform is anticipated to focus on enhancing inference capabilities, emphasizing on tokens-per-dollar economics.
The Register | 13 Mar, 2025
SambaNova Expands Deployment with SoftBank Corp. to Offer Fast AI Inference Across APAC
- SambaNova expands deployment with SoftBank Corp. in Japan to offer fast AI inference services.
- Developers in the APAC region will have access to SambaNova Cloud and its efficient AI chips.
- They will be able to utilize open source models such as Swallow, Llama, and Qwen for AI development.
- The partnership aims to strengthen generative AI initiatives and accelerate AI advancements in the region.
Global Fintech Series | 5 Mar, 2025
Introducing DeepSearcher: A Local Open Source Deep Research
- DeepSearcher is an open-source project that builds upon the principles of research agents and provides additional concepts like query routing, conditional execution flow, and web crawling.
- It serves as a Python library and command-line tool, offering more features than previous prototypes and showcasing agentic RAG in AI applications.
- The agent highlights the need for faster inference services and explores 'inference scaling' for improved output, using SambaNova's custom-built hardware and inference-as-a-service for efficient processing.
- DeepSearcher's architecture involves steps like defining and refining the question, researching, analyzing, synthesizing, and introduces improvements in research processes.
- The routing step involves prompting an LLM to decide relevant information sources for a given query, while the search step performs a similarity search with Milvus.
- Reflecting on the retrieved data helps in identifying gaps, leading to the generation of additional sub-queries to further refine the search.
- Conditional execution flow in DeepSearcher allows for iterative refinement of the research question until a report is generated, ensuring coherence and consistency in the final output.
- The agent synthesizes decomposed questions and chunks into a comprehensive report, demonstrating improvements over past approaches by maintaining consistency in the report structure.
- DeepSearcher's results include generating detailed reports, like on the evolution of The Simpsons, showcasing the agent's capabilities in research and analysis.
- The system leverages online inference services to enhance the quality of output reports, highlighting the significance of reasoning models in research agents.
- Future work includes exploring additional agentic concepts, improving the design space of research agents, and encouraging users to engage with DeepSearcher on GitHub.
Dev | 21 Feb, 2025
Nvidia rival claims DeepSeek world record as it delivers industry-first performance with 95% fewer chips
- SambaNova Systems claims world's fastest deployment of DeepSeek-R1 LLM, achieving 198 tokens per second using 16 custom-built chips.
- SambaNova's SN40L RDU chip is reportedly 3x faster and 5x more efficient than GPUs, while maintaining the reasoning power of DeepSeek-R1.
- SambaNova plans to increase its speed to 5x faster than the latest GPU speed on a single rack, and offer 100x capacity by year-end for DeepSeek-R1.
- DeepSeek-R1 671B is now available on SambaNova Cloud, with API access offered to select users.
Tech Radar | 21 Feb, 2025
Hugging Face’s AI Deployment Revolution: What You Need to Know
- Hugging Face has introduced Inference Providers to simplify AI model deployment and eliminate the need for manual cloud configuration.
- Developers can now deploy models like DeepSeek on SambaNova's AI servers directly from Hugging Face's platform in a few clicks.
- Hugging Face is tapping into the growing ecosystem of serverless inference to make deployment faster and more flexible.
- Free-tier users receive limited inference credits, while Hugging Face Pro subscribers get additional monthly credits for model deployment.
Medium | 5 Feb, 2025
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