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AMD announced the Instinct MI300X on June 13, 2023 as the GPU-only member of its MI300 accelerator family. Its defining feature is up to 192GB of HBM3 memory per accelerator, designed for large language models and other memory-intensive AI workloads. The MI300X is not a consumer graphics card: it is an enterprise OAM module deployed in specialized servers, cloud instances, and multi-GPU systems.
As of August 18, 2026, access is available through infrastructure providers and evaluation programs, but AMD has not published a consumer-style retail price.
What AMD announced
At its June 13, 2023 Data Center and AI Technology Premiere, AMD introduced several related products and technologies:
- The MI300 accelerator family.
- The MI300X, a GPU-only accelerator aimed heavily at generative AI.
- The MI300A, a CPU-and-GPU APU for high-performance computing and AI.
- An eight-GPU MI300X platform with approximately 1.5TB of aggregate HBM3 memory.
- ROCm software and ecosystem work involving platforms such as PyTorch and Hugging Face.
AMD said the MI300X would begin sampling to key customers in the third quarter of 2023. That wording described customer sampling and planned availability, not a consumer retail launch. AMD’s announcement also highlighted generative-AI training and inference as primary use cases.
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MI300X versus MI300A
| Feature | MI300X | MI300A |
|---|---|---|
| Design | GPU-only accelerator | CPU-plus-GPU APU |
| Architecture | Eight GPU accelerator-complex dies, or XCDs | Mixed CPU and GPU chiplet design |
| Primary focus | Generative AI, large-model inference and training | HPC and workloads benefiting from integrated CPU and GPU resources |
| Host CPU | Required in the server | CPU resources are integrated into the package |
| Deployment | Discrete data-center accelerator platform | Shared-memory APU-style platform |
“GPU-only” does not mean standalone. The MI300X still needs host CPUs, system memory, storage, networking, power delivery, cooling, firmware, and a compatible server platform. Removing the CPU portion allows more package area and power budget to be directed toward GPU compute and memory.
Why 192GB of HBM3 matters
For AI workloads, memory capacity can be as important as compute performance. Model weights, activations, temporary tensors, communication buffers, and the inference key-value cache all consume accelerator memory.
A 40-billion-parameter model stored in FP16 requires roughly 80GB for weights alone. That leaves less than the advertised 192GB for runtime overhead, activations, KV cache, and other data. Quantization can reduce weight memory, while longer context windows and higher concurrency can substantially increase KV-cache requirements.
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More memory can nevertheless provide important benefits:
- A larger model may fit on one accelerator instead of being split across several.
- Fewer GPUs can reduce model-parallel communication.
- Inference systems may support larger batches or longer contexts.
- Memory-bound workloads may perform better even when raw compute is not the limiting factor.
AMD said a 40-billion-parameter Falcon model fit on one MI300X in its FP16 test configuration. That is an AMD-specific result, not a guarantee that every 40B model will fit comfortably. Framework overhead, model implementation, precision, batch size, sequence length, and KV-cache settings all matter.
MI300X specifications
| Specification | MI300X detail |
|---|---|
| Architecture | AMD CDNA 3 |
| Chiplet process | 5nm/6nm FinFET |
| GPU dies | Eight XCDs |
| Memory | 192GB HBM3 per accelerator |
| Peak memory bandwidth | 5.325TB/s theoretical |
| Module power | 750W |
| GPU interconnect | Up to eight Infinity Fabric links |
| Peer-to-peer transport | Up to 1,024GB/s aggregate theoretical bandwidth per OAM module |
| Form factor | OAM enterprise accelerator module |
| Eight-GPU platform | 1.5TB, or 1,536GB, aggregate HBM3 |
The 5.325TB/s figure is a peak theoretical result based on an 8,192-bit memory interface and 5.2Gbps memory data rate. Actual application throughput depends on software, access patterns, kernels, and workload configuration. Current product information is available on AMD’s MI300 page.
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- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
What the eight-GPU platform means
AMD’s platform combines eight MI300X accelerators, each with 192GB of HBM3. That equals 1,536GB, commonly described as 1.5TB, across the system.
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MI300X versus Nvidia H100
AMD’s original comparison emphasized memory capacity and bandwidth:
| Specification | MI300X | H100 cited by AMD |
|---|---|---|
| HBM memory | 192GB HBM3 | 80GB HBM3 |
| Peak theoretical memory bandwidth | 5.325TB/s | 3.35TB/s |
These are figures from AMD’s selected comparison and should not be treated as proof that MI300X is faster in every AI workload. Real results depend on matrix-compute throughput, precision, kernels, framework maturity, batch size, model architecture, interconnect behavior, system cost, and availability. AMD lists theoretical FP16 and BF16 performance of 1,307.4 TFLOPS for the MI300X, but theoretical figures are not substitutes for independent application benchmarks.
ROCm is central to deployment
The MI300X depends on AMD’s ROCm software stack, which includes compilers, runtimes, mathematical libraries, programming tools, and machine-learning support. AMD provides MI300X guidance for AI and HPC workloads, along with framework and inference documentation.
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- ROCm and PyTorch version compatibility.
- Support for the intended inference framework, such as vLLM, SGLang, or Triton.
- Availability of required kernels and quantization paths.
- Compatibility of custom CUDA extensions.
- Container images, communication libraries, profiling, and monitoring tools.
AMD’s MI300X performance guidance can help with tuning, but teams should test their own models and deployment configurations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to access MI300X in 2026
The MI300X is generally accessed through enterprise infrastructure rather than bought as a desktop component.
Microsoft Azure
AMD’s Azure documentation lists these MI300X virtual-machine sizes:
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Standard_ND96isr_MI300X_v5Each VM has eight MI300X GPUs. The r variant includes InfiniBand networking for distributed workloads. Availability depends on region, quota, subscription, and capacity. A region check can be performed with:
regions=("westus" "francecentral" "uksouth")
for region in "${regions[@]}"; do
echo "$region"
az vm list-sizes
--location "$region"
--query "[?contains(name, 'MI300X')]"
--output table
doneCheck the current AMD Azure guide before deployment because images, regions, and VM availability change.
Oracle Cloud Infrastructure
AMD identifies OCI’s BM.GPU.MI300X.8 as an eight-MI300X bare-metal offering. Pricing and capacity should be confirmed through OCI’s live tools or a sales quote.
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- PCIe 5.0 x16 interface - fast data connection with modern systems
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AMD Developer Cloud
AMD’s Developer Cloud provides access through a third-party cloud provider. AMD says qualified applicants may receive an initial 25 hours of complimentary credit, described as approximately $50, with a valid credit card required. The credit expires 10 days after deposit. AMD also warns that billing can continue while an instance remains powered on until it is destroyed. See the AMD Developer Cloud information for current terms.
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Evaluation programs
Companies can also apply to AMD’s Instinct GPU Evaluation Program. Partner-based testing can help validate ROCm compatibility, model performance, and operational requirements before a production purchase or cloud contract.
Who should consider the MI300X?
The MI300X is most relevant to organizations running large-model inference, memory-intensive AI, HPC, or distributed training workloads. It may also appeal to teams seeking an alternative to a CUDA-centered infrastructure strategy.
It is a weaker fit for desktop users, small workloads that do not need 192GB of HBM, or CUDA-heavy applications that rely on unported custom extensions. Buyers should compare the complete cost of servers or cloud instances, networking, support, engineering migration, and operations—not just an advertised accelerator specification.
What buyers should check first
- Model fit: Determine whether the model fits on one GPU or requires sharding.
- Precision: Compare FP16, BF16, FP8, INT8, and quantized deployment requirements.
- Inference shape: Account for batch size, concurrency, sequence length, and KV-cache growth.
- Training memory: Include gradients, optimizer states, activations, and temporary buffers.
- Software: Test ROCm, framework versions, kernels, containers, and custom extensions.
- Interconnect: Validate GPU-to-GPU and node-to-node communication for distributed jobs.
- Availability: Check region, quota, reservation, and capacity constraints.
- Economics: Compare the entire server or VM configuration and expected utilization.
Bottom line
The MI300X’s important innovation was not simply a claim of universally superior GPU compute. Its core proposition was the combination of 192GB of HBM3 per accelerator, very high memory bandwidth, and an eight-GPU platform designed for large AI models. That can reduce memory sharding for some workloads, but the benefits depend on ROCm support, model optimization, distributed-system design, availability, and total cost.
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