AI Inference at Scale: Reducing Latency and Cost with MI325X

For AI engineers building real-world applications, training is only half the battle. Inference, the process of running trained models in production, is where user experience, system performance, and cost efficiency collide.

When you're serving millions of requests per day with large language models (LLMs), every millisecond of latency and every dollar of compute spend matters.

That’s where AMD’s Instinct™ MI325X comes in.

With 256GB of high-bandwidth memory, massive inference throughput, and support for FP8/INT8 precision, the MI325X is optimized not just for model building, but for model serving at scale.

Why Inference at Scale Is Hard

Inference workloads are fundamentally different from training:

• Latency-sensitive: Users expect near-instant responses.
• Throughput-dependent: You’re serving many requests per second.
• Memory-hungry: Models like Llama 2–70B or Mixtral-8x7B need massive memory to load efficiently.

Traditional GPUs like the H100 (with 80GB VRAM) often require model parallelism to run large models, increasing both complexity and latency. And if you exceed memory limits, you’re stuck offloading to CPU or slower memory tiers which is killing performance.

How MI325X Fixes the Bottlenecks

1. Single-GPU Inference for Large Models

With 256GB HBM3E, the MI325X can host full 70B–100B+ parameter models on a single GPU. That means:

• No sharding
• Lower overhead
• Simpler deployment

You get faster cold-start times, lower latency, and higher throughput without needing to orchestrate multi-GPU systems.

2. High-Throughput Precision Modes

The MI325X supports FP8 and INT8 with dedicated matrix cores and structured sparsity. This enables:

• 2.6 PFLOPS of INT8 performance
• Up to 2× throughput boost on quantized models

Pair that with AMD’s ROCm libraries for optimized Transformer kernels, and the result is real-world inference performance that competes with—and often exceeds—H100s.

3. Memory Bandwidth = Response Speed

With 6TB/s memory bandwidth, the MI325X moves data faster than any other production GPU. That’s key for:

• Fast token generation in LLMs
• Reduced time-to-first-token
• High batch throughput in chatbots and copilot apps

Whether you're using large context windows or multi-modal models, memory bandwidth is the hidden hero that determines how fast you can serve results.

Running Inference on TensorWave

At TensorWave, we’ve optimized our MI325X clusters for production-grade inference:

• Liquid-cooled GPUs running at full power 24/7
• ROCm-native environments for PyTorch, ONNX, vLLM, and Triton
• Auto-scaling support for dynamic workloads

We also give you dedicated access – no noisy neighbors, no surprise throttling. Just pure, deterministic performance.

🔗 Learn more about our AI inference infrastructure: tensorwave.com/bare-metal

Real-World Results

TensorWave customers using MI325X for inference have reported:

• 20–30% lower latency vs H100 on 70B model deployments
• Higher token/sec throughput on both quantized and full-precision models
• Reduced GPU count needed for the same QPS targets

In one case, a customer serving a 65B model dropped from 4× H100s to 1× MI325X—with better latency.

TL;DR: More Memory, Faster Inference, Lower Cost

If you're deploying large models in production, MI325X is the best GPU for inference at scale:

• 256GB = serve big models with fewer GPUs
• 6TB/s bandwidth = faster token generation
• FP8/INT8 = efficient, high-throughput serving

And when deployed on TensorWave, you get:

• ROCm-optimized stack
• Liquid-cooled infrastructure
• Deterministic, low-latency performance

Get access to MI325X for inference today and serve smarter, faster, and cheaper.

About TensorWave

TensorWave is the AMD GPU cloud purpose-built for performance. Powered exclusively by Instinct™ Series GPUs, we deliver high-bandwidth, memory-optimized infrastructure that scales with your most demanding models—training or inference.

Ready to get started? Connect with a Sales Engineer.