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An open, cost-effective AI accelerator priced well below NVIDIA.
Pros
- Roughly half the price of H100-class GPUs
- Open Ethernet networking, no proprietary fabric
- 60% more memory than H100 SXM
Cons
- Lower raw performance than H100/Blackwell
- Software ecosystem trails CUDA and ROCm
- Smaller deployment base and tooling
✓ Where it shines / best for
- Enterprises wanting a lower-cost alternative to NVIDIA for training/inference
- Open Ethernet-based cluster scale-out without proprietary fabric
- On-prem and OEM-server AI deployments
✕ Not the best fit for
- CUDA-dependent codebases that can't migrate
- Absolute peak-performance frontier training vs latest NVIDIA
- Buyers needing the largest software ecosystem and tooling breadth
Features
- ✓ Data-center scale
- ✓ API access
- ✓ Inference
- ✓ Training
- ✓ FP8
- ✓ Ethernet
- ✓ PCIe
- ✓ HBM2E
- ✓ OAM
- ✓ On-device / offline
Pricing
| Plan | Price | Billing | Notes |
|---|---|---|---|
| PCIe / OAM accelerator | ~$15,000–$16,000 | per card (est.) | Reported list/OEM pricing per accelerator; sold through OEMs (Dell, Supermicro) and IBM Cloud. Positioned well below comparable NVIDIA. |
| Universal Baseboard (8-card) | ~$125,000 | per UBB (est.) | 8-accelerator HL-325L baseboard system pricing reported by Intel. |
| Cloud (IBM Cloud) | Usage-based | per hour | Available as a cloud instance on IBM Cloud; rate by contract/instance. |
Pricing verified from the official source. Prices change often — confirm on the vendor's site before buying.
Specifications
| sram | 96 MB on-die, 19.2 TB/s |
| power | 600W TDP |
| memory | 128 GB HBM2e, 3.7 TB/s |
| networking | 24x 200 GbE RoCE |
| architecture | Gaudi 3 (Intel), dual-chiplet |
| fp8_bf16_performance | 1.8 PFLOPS (1,835 TFLOPS BF16) |
Sponsored
A full review is being generated for this product and will appear here shortly.