AMD MI325X vs MI350X Cloud: VRAM & CDNA 4 Specs Comparison

AMD MI325X vs MI350X cloud: quick verdict

Evaluating an AMD MI325X vs MI350X cloud deployment requires understanding how memory capacity, memory bandwidth, and microarchitecture changes impact large language model workloads. The AMD Instinct MI325X builds on the CDNA 3 architecture by introducing 256GB of high-speed HBM3e VRAM operating at 6.0 TB/s memory bandwidth.^[source] In comparison, the next-generation AMD Instinct MI350X transitions to the CDNA 4 architecture on a 3nm semiconductor process, pushing memory capacity up to 288GB of HBM3e and memory bandwidth up to 8.0 TB/s while adding native FP4 and FP6 low-precision tensor compute operations.^[source] Choosing between these accelerators depends on model weight size, context window length, and precision requirements.

Select the AMD Instinct MI325X if you serve large models such as Llama 3 70B or 405B in FP8 or FP16 precision today, where 256GB VRAM per GPU reduces the total node count required for inference deployments.^[source] Select the AMD Instinct MI350X when your workload demands maximum compute throughput, sub-8-bit FP4 quantization support, or scaling massive pre-training clusters with up to 9.2 PFLOPS of tensor compute density.^[source]

Side-by-side spec comparison

The table below summarizes the core technical specifications published by AMD for the Instinct MI325X and Instinct MI350X accelerators.

Cloud pricing and provider availability

Cloud infrastructure providers position AMD Instinct accelerators as memory-dense alternatives to NVIDIA Hopper and Blackwell instances. Specialized cloud platforms like CoreWeave publish instance catalogs with on-demand and reserved availability.^[source]

Unlike legacy cloud instances that restrict memory capacity, AMD Instinct nodes allow teams to run massive models on fewer physical GPUs. For instance, hosting a 405B parameter model in FP8 requires 410GB of raw weights plus context cache memory. On traditional 80GB GPUs, this workload requires an 8-GPU cluster. On 256GB MI325X instances, two GPUs provide 512GB of combined VRAM, cutting inter-GPU interconnect overhead and lowering per-node operational expense.^[source]

To compare live hourly rental rates, spot discounts, and geographic availability across leading cloud providers, explore our interactive GPU Picks Lookup tool. You can also examine our CoreWeave review to analyze specialized GPU cloud provisioning.

Architectural differences: CDNA 3 vs CDNA 4

The fundamental difference between the MI325X and the MI350X lies in the transition from the CDNA 3 architecture to CDNA 4.^{[source][source]}

CDNA 3 architecture in MI325X

The MI325X uses AMD's CDNA 3 architecture built on 5nm and 6nm chiplet technology.^[source] It features matrix cores engineered for FP16, BF16, FP8, and INT8 operations, delivering 1.3 PFLOPS of peak FP16 compute performance.^[source] While CDNA 3 shares its execution engine design with the earlier MI300X, the MI325X upgrades the onboard memory controllers to support 256GB of high-frequency HBM3e memory.^[source] For a broader view of earlier CDNA 3 cloud deployments, refer to our AMD MI300X cloud rentals guide.

CDNA 4 architecture in MI350X

The MI350X introduces the CDNA 4 architecture manufactured on an advanced 3nm process node.^[source] CDNA 4 redesigns the underlying compute units to deliver up to 4.6 PFLOPS of FP16 and FP8 tensor performance, representing a 3.5x increase in raw compute density over CDNA 3.^{[source][source]} Additionally, CDNA 4 adds dedicated hardware instructions for FP4 and FP6 data types, enabling high-throughput execution for quantized neural networks.^[source]

Memory bandwidth and VRAM footprint expansion

Large language model inference is heavily constrained by memory bandwidth during the autoregressive token generation phase. During decoding, model weights must be loaded from VRAM into matrix compute units for every generated token.

MI325X memory specifications

The MI325X features 256GB of HBM3e memory operating across a wide bus to deliver 6.0 TB/s of peak memory bandwidth.^[source] This 256GB memory capacity allows an entire 70B model in FP16 precision (requiring approximately 140GB VRAM) to fit on a single GPU socket with ample headroom for key-value (KV) context caching.^[source]

MI350X memory specifications

The MI350X expands memory capacity up to 288GB of HBM3e while pushing memory bandwidth up to 8.0 TB/s.^[source] The 33.3 percent increase in memory bandwidth over the MI325X accelerates weight transfer rates, directly reducing token decoding latency for multi-tenant inference services.^{[source][source]} This memory subsystem makes MI350X ideal for serving multi-hundred-billion parameter models with large batch sizes.

Quantization support: FP16 and FP8 vs native FP4

Precision format support plays a central role in determining model latency and cluster hardware requirements.

FP16 and FP8 precision on MI325X

The MI325X supports standard FP16, BF16, and FP8 formats.^[source] Running models in FP8 precision halves memory footprint and doubles tensor math performance compared to FP16. Because MI325X offers 256GB VRAM per socket, teams can run unquantized FP16 70B models on one GPU or FP8 405B models across two GPUs.^[source]

Native FP4 and FP6 support on MI350X

The MI350X introduces native FP4 and FP6 block floating point execution.^[source] FP4 quantization compresses model weights into 4-bit representations, effectively reducing VRAM requirements by 50 percent compared to FP8. Supported by CDNA 4 matrix units, FP4 compute throughput reaches up to 9.2 PFLOPS on MI350X, unlocking massive token generation speeds without requiring multi-node model parallel setups.^[source]

Category decisions: when to choose MI325X vs MI350X

Choose AMD Instinct MI325X if:

• You deploy production 70B to 405B LLM inference workloads using FP8 or FP16 precision today.^[source]
• Your primary bottleneck is VRAM capacity per socket, and 256GB HBM3e satisfies your context caching budget.^[source]
• You want immediate cloud availability on specialized platforms like CoreWeave without waiting for 3nm production allocation.^[source]
• You seek lower power consumption per node, staying within a 750W TDP per module.^[source]

Choose AMD Instinct MI350X if:

• You plan to deploy ultra-low-bit FP4 or FP6 quantized LLM pipelines for maximum inference throughput.^[source]
• You require maximum raw compute TFLOPS for distributed pre-training of massive foundation models.^[source]
• Your workload demands up to 8.0 TB/s memory bandwidth to minimize token latency under high concurrent user load.^[source]
• Your facility or cloud environment supports higher thermal power limits up to 1000W per module.^[source]

Who should choose each AMD accelerator

Production LLM serving teams

Engineering teams serving high-concurrency commercial APIs for 70B parameter models should select the MI325X for current deployments. The 256GB HBM3e capacity allows single-GPU hosting with large KV cache allocations, lowering host node overhead.^[source] Teams planning next-year architecture refreshes can transition to MI350X to leverage 8.0 TB/s bandwidth and FP4 quantization.^[source] Learn more about inference placement strategies in our best GPU cloud for LLM inference guide.

Foundation model training clusters

Research organizations building large-scale pre-training pipelines should target MI350X clusters. The 3.5x compute throughput jump to 4.6 PFLOPS in FP8/FP16 significantly accelerates training convergence per node.^{[source][source]} For existing training cluster setups, consult our best GPU cloud for ML training guide.

Budget-focused AI startups

Startups seeking to minimize cloud infrastructure spending should evaluate per-token generation costs across both platforms. Deploying FP8 models on MI325X offers immediate memory efficiency, whereas upgrading to MI350X will optimize per-token costs once FP4 model pipelines mature. Compare hardware trade-offs in our how to choose a GPU cloud guide and our H100 vs H200 cloud pricing guide.

Alternatives to AMD MI325X and MI350X

If your team is assessing alternative data center GPU hardware, consider these options:

• NVIDIA H200: Features 141GB of HBM3e memory operating at 4.8 TB/s bandwidth on the Hopper architecture, ideal for single-node 70B model serving.^[source]
• NVIDIA B200 (Blackwell): Offers 192GB VRAM and high compute density, competing directly with high-end data center accelerators for enterprise AI workloads.
• AMD Instinct MI300X: The predecessor CDNA 3 GPU providing 192GB HBM3 VRAM at 5.3 TB/s bandwidth, widely available across specialized neoclouds.^[source]

Pros and cons

Methodology and limitations

GPU Picks compiled this comparison using technical datasheets published by AMD and official pricing catalogs from specialized cloud providers including CoreWeave.^{[source][source][source]} GPU Picks did not perform hands-on hardware benchmarking, latency measurements, or 30-day provider uptime testing.

Cloud instance availability, spot pricing, and regional allocations vary over time. Always verify live pricing and specifications on official provider dashboards prior to provisioning enterprise infrastructure. Read our full editorial methodology to learn about our source verification standards.

Frequently asked questions