GPU × use-case guide · Speech-to-text

Is the Instinct MI300X a good GPU for speech-to-text?

The Instinct MI300X is a cdna3 AMD GPU with 192 GB HBM3 (5300 GB/s), 1307 TFLOPS BF16 / 2614 TFLOPS FP8, and a 750 W TDP. Speech-to-text workloads care most about encoder throughput and real-time factor; models are small so mid-range cards are plenty. Here's how the Instinct MI300X measures up.

What models fit on a single Instinct MI300X?

Weights only, reserving ~25% of the 192 GB for KV cache, activations and fragmentation. ✓ = fits on one card.

Largest single-card fit: Qwen 2.5 72B at BF16, Qwen 2.5 72B at FP8, Qwen 2.5 72B at INT4. Bigger models need tensor-parallel across 8 cards.

Instinct MI300X for speech-to-text, specifically

Speech-to-text is throughput-bound rather than VRAM-bound, so the Instinct MI300X's 5300 GB/s of bandwidth and 1307 TFLOPS matter more than raw capacity. Because it's latency-sensitive, run it at low batch sizes on a fast framework (vLLM ≈ 85% MFU) rather than maximising batch. Size it precisely on the calculator.

Instinct MI300X pricing across providers

Verdict

With 192 GB, the Instinct MI300X is a data-center-class card that comfortably handles speech-to-text for models up to Qwen 2.5 72B at full precision on a single card — a strong pick if your budget supports ~$2.39/hr.