CoreWeave Inc. shares traded lower on Tuesday as investors digested a series of recent catalysts, including the company's official entry into the Nasdaq-100 Index and a new storage partnership. The stock had previously surged approximately 20% during the week, fueled by the index rebalance which became effective before the market opened on June 22, 2026. This inclusion forces buying from passive funds and ETFs tracking the index, such as the QQQ, which manages over $300 billion in assets.

The recent rally was also supported by Cantor Fitzgerald, which reiterated its Overweight rating and $167 price target. The firm highlighted run-rate EBITDA of $18.76 billion, up from $16.10 billion in April, and modeled an implied backlog of $125 billion with a path to more than $131 billion by the end of Q2 2026. Cantor Fitzgerald argued the market is "woefully undervaluing" CoreWeave and the broader "neocloud" group.

Operational Highlights and Partnerships

CoreWeave announced record-breaking results in the MLPerf Training v6.0 benchmark suite, training the DeepSeek-V3 671B model in approximately two minutes using 8,192 NVIDIA GB300 NVL72 GPUs. The company stated this demonstrated near-linear scaling efficiency on infrastructure currently available to customers. Additionally, CoreWeave secured a five-year, $335 million "multi-exabyte" agreement to expand HDD-based tiers inside its AI Object Storage, requiring no code modifications for existing customers.

Financial Positioning

To support its operations, CoreWeave priced a private offering of $1.25 billion of 9.625% senior notes and 2 billion euros of 8.500% senior notes. Both notes are due in 2032, with the offering expected to close on June 18, 2026. Proceeds are designated for general corporate purposes, including debt repayment.

Technical Analysis and Outlook

From a technical perspective, CoreWeave is holding above its major long-term moving averages, trading about 2% above the 20-day SMA ($108.08) and 1.5% below the 50-day SMA ($111.94). The stock remains above its 100-day SMA ($98.81) and 200-day SMA ($100.62), keeping the broader uptrend intact. The Relative Strength Index (RSI) sits at 51.84, indicating a neutral market condition. Key resistance is identified near $132.00, while support stands around $103.00. At the time of publication, CoreWeave shares were down 3.36% at $107.55.

CoreWeave, Inc. (NASDAQ: CRWV) announced record-breaking results in the MLPerf Training v6.0 benchmark suite, training the DeepSeek-V3 671B model in 2.02 minutes on 8,192 NVIDIA GB300 NVL72 GPUs. The performance marks the fastest DeepSeek-V3 training result in the benchmark and was achieved on the largest GB300 cluster submitted in this round. This speed addresses the critical constraint of training performance as frontier models scale to trillion-parameter sizes and agentic workloads become standard.

The benchmark results reflect CoreWeave's full-stack infrastructure optimizations across networking, orchestration, scheduling, storage, and software. The company submitted three GB300 NVL72 configurations on the DeepSeek-V3 671B workload, achieving the fastest results across all Closed/Available-cloud submissions. CoreWeave was the only submitter in the v6.0 round to scale a GB300 platform beyond 2,048 GPUs on this specific workload.

DeepSeek-V3 671B Performance Metrics

CoreWeave demonstrated consistent, near-linear scaling efficiency as the cluster size doubled. The training time improved predictably across the different node configurations.

Additional Benchmark Results

Beyond the DeepSeek-V3 results, CoreWeave reported performance metrics for other models on different hardware configurations. On a 4,096-GPU NVIDIA GB300 NVL72 deployment, the company reached the Llama-3.1-405B reference quality target in 9.77 minutes. This run utilized the NVIDIA NeMo Framework Release 26.04, CUDA graphs, and NVIDIA Spectrum-X Ethernet running RoCE.

On a smaller 8-node, 64-GPU NVIDIA HGX B200 cluster connected via InfiniBand, CoreWeave trained GPT-OSS-20B in 26.98 minutes and Llama-3.1-8B in 16.54 minutes. The company attributed these results to optimizations in orchestration, communication libraries, and distributed training configuration.

Infrastructure and Optimization

CoreWeave attributed its performance to several key infrastructure layers. CoreWeave Mission Control performs continuous health checks across rack-scale systems to validate hardware, firmware, network, and thermal health. The CoreWeave SUNK scheduler is topology-aware, placing workloads to maximize locality and minimize inter-rack communication for Mixture of Experts (MoE) workloads. Additionally, a rail-aware networking strategy balances traffic to prevent hotspots within the fabric at multi-thousand-GPU scale.

Chen Goldberg, Executive Vice President of Product and Engineering at CoreWeave, stated that the results came from the same infrastructure customers run in production today. Brendan Burke, Research Director at Futurum Research, noted that the results demonstrate full-stack AI expertise compounds real-world performance gains as new hardware arrives.