Intel Gaudi 3 vs AMD Instinct MI300X and MI325X: The Enterprise AI Accelerator Buying Guide for 2026

Comparable hardware specifications

These are vendor specifications and theoretical maxima, not measured application performance. The comparison uses OAM devices because all three are offered in eight-accelerator OAM platforms.

| Specification | Intel Gaudi 3 | AMD Instinct MI300X | AMD Instinct MI325X | |---|---|---|---| | HBM capacity | 128 GB HBM2e↗ | Up to 192 GB HBM3↗ | Up to 256 GB HBM3E↗ | | Peak HBM bandwidth | 3.7 TB/s↗ | 5.3 TB/s↗ | 6 TB/s↗ | | Maximum board/device power | 900 W TDP↗ | 750 W TBP↗ | 1,000 W TBP↗ | | Form factor | OCP OAM 2.0 mezzanine↗ | OAM module; eight-GPU UBB 2.0 platform↗ | OAM module; eight-GPU UBB 2.0 platform↗ | | Host interface | PCIe 5.0 x16, 128 GB/s bidirectional↗ | PCIe 5.0 x16, 128 GB/s↗ | PCIe 5.0 x16, 128 GB/s↗ | | Intra-node networking | Twenty-one 200 GbE links per accelerator in an eight-way non-blocking all-to-all baseboard↗ | Seven 128 GB/s Infinity Fabric links; eight GPUs connected in a ring↗ | Seven 128 GB/s Infinity Fabric links; eight GPUs connected in a ring↗ | | Inter-node networking | Three 200 GbE RoCE ports per accelerator; 4.8 TbE aggregate baseboard scale-out through six OSFP800 ports↗ | Scale-out listed as PCIe 5.0 x16, requiring platform networking↗ | Scale-out listed as PCIe 5.0 x16, requiring platform networking↗ |

MI325X doubles Gaudi 3’s per-device HBM and raises peak bandwidth by approximately 62%, while MI300X supplies 50% more HBM and approximately 43% more peak bandwidth. Conversely, Gaudi 3 incorporates its RoCE networking into each accelerator rather than relying solely on host-attached scale-out networking, according to Intel’s architecture and networking description↗.

Kaito's verdict on specs: Raw HBM numbers favor AMD — MI325X's 256 GB HBM3E at 6 TB/s is the largest memory pool in this class. But Gaudi 3's integrated 200 GbE RoCE networking per accelerator is a genuine architectural differentiator for multi-node scale-out. Neither advantage matters if the software stack cannot exploit it — validate with your actual workload before signing a purchase order.

Data-center deployment requirements

Eight-accelerator nodes, power and cooling

At maximum device power, accelerator-only demand is 7.2 kW for eight Gaudi 3 devices, 6 kW for eight MI300X devices, and 8 kW for eight MI325X devices. Those figures exclude CPUs, DRAM, storage, fans, pumps, NICs and conversion losses and follow directly from the vendors’ Gaudi 3↗, MI300X↗, and MI325X↗ maximum-power specifications.

Intel’s more realistic reference-design allowance is 10,000 W for a complete eight-Gaudi-3 node↗. Its design places four such nodes in a rack and specifies dual redundant three-phase 400/415 V PDUs rated to 63 A, or 48 A derated. That implies a power-dense deployment requiring facility-level validation rather than a simple server replacement.

Gaudi 3 OAM supports passive air or liquid cooling at up to 900 W, while Intel’s reference system is an air-cooled chassis. MI325X’s 1,000 W accelerator ceiling makes cooling design especially important even though the supplied AMD data sheet does not prescribe a rack thermal architecture. Buyers should request OEM-certified inlet-temperature, airflow or coolant, rack-density, and derating data before comparing facilities costs.

Fabric and cabling

Inside a Gaudi 3 node, 21 of each accelerator’s 24 integrated 200 GbE ports form an all-to-all network; the remaining three provide scale-out. Intel’s baseboard exposes the node’s external connectivity as six 800 GbE OSFP ports↗.

Intel’s cluster design uses three independent network plys and a full Clos fabric. It assigns six 800 GbE connections to every compute node and recommends leaf-spine expansion when a cluster exceeds one switch level. Smaller deployments may use an L2 fabric, while the larger reference design uses L3 and requires configuration of 24 scale-out interfaces per node through `gaudinet.json`, as detailed in the cluster reference design↗.

MI300X and MI325X instead use seven Infinity Fabric links for eight-GPU platform connectivity. Their data sheets describe 128 GB/s bidirectional links between GPUs, but inter-node performance will depend on the OEM’s host NICs, switches, topology and ROCm communication configuration rather than the accelerator module alone (MI300X platform↗; MI325X platform↗).

Storage, control and management

Do not run accelerator, storage, control and management traffic through an undifferentiated fabric. Intel’s reference separates an 800 GbE accelerator network, a full-Clos 100 GbE storage fabric, a 100/25 GbE control-plane design, and out-of-band management using 25 and 1 GbE connections. Its 32-node storage example targets approximately 20 GB/s large-block random reads per storage server and approximately 0.5 TB/s across the cluster, although actual capacity and throughput remain workload-dependent (reference architecture↗).

The same design separates BMC, PDU and switch access with management VLANs and identifies provisioning, upgrades, health, metrics and user management as control-plane responsibilities. These requirements apply in principle to AMD clusters too, but AMD platform quotations must identify equivalent storage, observability, BMC, scheduler and fabric-management components.

Software and migration

Intel’s Gaudi software suite integrates PyTorch and supplies a graph compiler, runtime, kernel library and automatic kernel fusion. Intel also documents integrations with DeepSpeed, Hugging Face, vLLM and Ray, plus optimized implementations of Paged Attention and Flash Attention (Gaudi software suite↗). Optimum Habana should be treated as the Hugging Face-oriented migration layer, not as proof that every CUDA extension or custom kernel will run unchanged.

The provided documentation establishes a Gaudi-specific software path rather than generic oneAPI portability. A buyer should therefore inventory custom CUDA operators, compiler assumptions, collective calls and serving extensions before accepting “PyTorch compatible” as equivalent to application compatibility. Intel exposes an LLVM-based TPC SDK for custom kernels, but that creates a Gaudi-specific optimization and maintenance obligation.

ROCm supports PyTorch, TensorFlow, ONNX Runtime, Triton and JAX in the MI300X software description↗, while AMD presents MI325X as a drop-in-compatible successor on the same eight-GPU platform. Both GPUs use the `gfx942` target and remain officially supported in the July 15, 2026 ROCm system requirements↗.

ROCm’s current matrix gives MI300X support across every listed operating system. MI325X excludes RHEL 8.10, Rocky Linux 9 and Oracle Linux 8. Both support KVM passthrough and SR-IOV in specified Ubuntu and RHEL combinations; MI300X additionally has documented ESXi 8.0 Update 3 passthrough support. These distinctions matter for regulated estates and existing virtualization standards, not just developers.

Benchmarks: measured results versus peaks

Gaudi 3’s 1,678 TFLOPS FP8/BF16 matrix figure and AMD’s advertised sparse FP8 figures are theoretical hardware peaks, not tokens per second, training time or service-level latency (Intel compute table↗; AMD MI300X data sheet↗). Sparsity-enabled and dense results must not be mixed.

Public MLPerf evidence establishes MI300X participation in inference and large multi-node training, including a reported 512-MI300X Oracle Cloud deployment. It does not, in the supplied material, provide a workload-specific time or throughput metric suitable for reproducing a direct comparison here. More importantly, the evidence does not establish an equivalent public MLPerf result for Gaudi 3. Consequently, there is no defensible public Gaudi-3-versus-MI300X or MI325X token benchmark to quote as apples-to-apples.

The proof of concept should use identical model weights, prompts, context distributions and quality targets. Record the following metrics for a defensible comparison:

• Time to first token (TTFT) and inter-token latency (ITL): the two metrics that determine user-perceived responsiveness in interactive inference deployments.
• Output tokens per second and requests per second at target concurrency: measure at your actual production load, not a single-stream synthetic benchmark.
• Power at the wall and HBM utilization: divide throughput by wall power to get tokens-per-watt, the most honest efficiency metric for TCO modeling.
• Scaling efficiency across nodes: for multi-node training, measure samples per second at 1, 2, 4, and 8 nodes to expose fabric and collective-communication bottlenecks.
• Checkpoint overhead and recovery time: critical for long training runs where hardware or software failures are statistically inevitable.

Training tests should record time to convergence or a fixed quality target, checkpoint overhead, scaling efficiency and recovery behavior—not merely samples per second.

Procurement, cloud availability and pricing

Gaudi 3 is available through IBM Cloud virtual-server and OpenShift deployment routes and through OEM systems, including the Dell PowerEdge XE9680 configuration named in Intel’s 32-node bill of materials↗. However, the supplied official material does not expose a live, universally applicable public hourly list price. The historical third-party figure of approximately $60 per hour must not be treated as a current verified rate because its exact configuration, term and present regional applicability are not established. Obtain an IBM catalog or configure-price-quote result identifying whether the charge is per GPU-hour or whole eight-accelerator-instance-hour.

Official AMD material confirms that MI300X is sold as an eight-accelerator UBB 2.0 platform, while cloud evidence identifies eight-GPU configurations such as OCI bare-metal deployments. A custom quote or live provider console remains necessary because the supplied official AMD sources publish hardware specifications, not a current universal cloud price (MI300X platform specification↗). Do not compare an advertised per-GPU-hour figure with a whole-instance-hour price.

Treat MI325X procurement as an OEM or platform quotation. AMD confirms eight-accelerator platform availability and MI300X platform compatibility, but the official MI325X data sheet↗ provides no public cloud rate.

Procurement rule: Never compare a per-GPU-hour cloud rate with a whole-instance-hour price, and never accept a theoretical FLOPS figure as a proxy for tokens-per-second throughput. Require a proof-of-concept on your exact model, context length, and concurrency target before committing to any enterprise accelerator platform.

TCO and decision matrix

Model TCO as compute charges or depreciation plus facilities power, cooling, fabric switches, optics, storage, support and migration engineering. Divide by completed training runs or quality-compliant tokens—not theoretical FLOPS. Higher HBM may reduce model partitioning, while Gaudi’s integrated Ethernet may reduce separate networking requirements; both benefits must be demonstrated with the target workload.

| Best fit | Recommendation | Principal reason | Main procurement risk | |---|---|---|---| | Ethernet-centered multi-node training or inference | Gaudi 3 | Integrated 24-port RoCE networking and prescriptive all-Ethernet cluster design | Smaller 128 GB HBM pool; Gaudi-specific software optimization; no equivalent public MLPerf comparison | | Established ROCm deployment balancing memory and power | MI300X | 192 GB HBM3, 5.3 TB/s peak bandwidth and broad current ROCm OS coverage | OEM network design and migration effort can dominate realized scaling | | Largest models and memory-bound serving or training | MI325X | 256 GB HBM3E and 6 TB/s peak bandwidth | 1,000 W per accelerator and quote-based platform economics |

When an RTX 5090 is genuinely better

An RTX 5090 can be the better-value choice for a workstation, lab, small inference service or single-node development system when the model and KV cache fit within its 32 GB memory—commonly smaller models or approximately 32-billion-parameter models under suitable quantization. It can avoid the cost and operational overhead of an eight-accelerator enterprise node.

RTX 5090 is the right call when:

• Your model fits in 32 GB GDDR7 — Llama 3 8B, Mistral 7B, Qwen 14B, or quantized 32B models all run comfortably without memory partitioning.
• You need a single-developer workstation for experimentation, fine-tuning with LoRA/QLoRA, or rapid prototyping before committing to enterprise hardware.
• Your budget is under $5,000 for the full system — an RTX 5090 card retails around $1,999–$2,499, versus six-figure enterprise node quotes for Gaudi 3 or MI300X clusters.
• You want CUDA ecosystem compatibility without porting effort — every major framework, library, and serving stack runs natively on NVIDIA hardware.

It is not an equivalent production-cluster substitute. The supplied evidence identifies no ECC memory, MIG partitioning or NVLink; multi-GPU communication therefore relies on PCIe. Its 575 W power demand still requires deliberate chassis and cooling design, and consumer-card support does not provide the same enterprise reliability framework. Use it for development, quantized inference and independent replicas—not as a replacement for production multi-node training, large unquantized models, or deployments requiring accelerator partitioning and enterprise support.