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GPU × use-case guide · Reasoning / agentic

Is the Instinct MI300X a good GPU for reasoning / agentic?

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. Reasoning / agentic workloads care most about long output generations (chain-of-thought), so decode throughput and KV-cache headroom dominate. Here's how the Instinct MI300X measures up.

VRAM
192 GB
Bandwidth
5300 GB/s
BF16
1307 TFLOPS
Cheapest
$2.39/hr

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.

ModelBF16FP8INT4
Llama 3.1 8B
Qwen 2.5 14B
Gemma 2 27B
Mixtral 8x7B (MoE)
Llama 3.3 70B
Qwen 2.5 72B
Llama 3.1 405B

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 reasoning / agentic, specifically

Reasoning / agentic is context-heavy, so the KV cache — not the weights — is what fills the 192 GB. On the Instinct MI300X you'll trade context length against batch size: long prompts mean fewer concurrent requests. 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

ProviderOn-demand $/hrReserved $/hr
fluidstack$2.39
lambda$2.49
tensordock$2.69
vast_ai$2.79
coreweave$3.39$2.49
runpod$3.49

Verdict

With 192 GB, the Instinct MI300X is a data-center-class card that comfortably handles reasoning / agentic 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.

See full Instinct MI300Xspecs & pricing, size your model on the calculator, or compare every GPU on the GPU list.