AMD Instinct MI300X Cloud Pricing & Rental Rates
The AMD Instinct MI300X delivers 192 GB HBM3 at 5.3 TB/s with high-bandwidth interconnect at 896 GB/s. The 2.4x VRAM advantage over H100 (80 GB) enables running 70B unquantized models on a single node, making it the memory-capacity leader for LLM serving.
Target Workload: Full FP16/FP8 70B & 405B MoE serving
AMD Instinct MI300X LLM Workload Sizing & Capacity
Max Parameter Size (Single-GPU)
192GB HBM3 VRAM supports INT4 quantization of up to 30B-class models; FP16 limited to smaller parameter counts.
Interconnect & Tensor Parallelism
InfiniBand-class 896 GB/s. PCIe or NVLink depending on form factor; tensor parallelism efficiency varies by interconnect.
Recommended Serving Frameworks
vLLM (PagedAttention v2), SGLang (Radix Attention), or Ollama for local deployment. TensorRT-LLM for maximum throughput on NVIDIA hardware. FlashAttention-2/3 required for FP8 inference.
Market Availability & Early Reservation Watch
No verified on-demand rental instances are currently available in public spot markets. Cloud providers are accepting private cluster reservation inquiries for Q4 2026 delivery.
Telemetry Status: Theoretical & Lab Sizing Estimates
Hardware not yet widely deployed in multi-tenant public clouds. Specifications sourced from NVIDIA architectural whitepapers and lab benchmarks.
Models That Fit AMD Instinct MI300X (192GB HBM3)
Deterministic VRAM calculation (FP8) via entity graph. Only editorial and enriched models shown.
| MODEL | PARAMS | CONTEXT | FP8 VRAM | FIT? | CALCULATOR |
|---|---|---|---|---|---|
| DeepSeek R1 Distill 70B | 70B | 128K | 81.0 GB | ✓ fits | Pre-filled → |
| DeepSeek R1 Distill Qwen 32B | 32B | 128K | 38.0 GB | ✓ fits | Pre-filled → |
| Llama 3.3 70B Instruct | 70.6B | 128K | 100.9 GB | ✓ fits | Pre-filled → |
| Llama 3.1 8B Instruct | 8.03B | 128K | 19.2 GB | ✓ fits | Pre-filled → |
| Llama 3.2 3B Instruct | 3.2B | 128K | 5.4 GB | ✓ fits | Pre-filled → |
| Llama 3.2 1B Instruct | 1B | 128K | 2.9 GB | ✓ fits | Pre-filled → |
| Llama 3.1 70B Instruct | 70.6B | 128K | 100.9 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 Coder 32B | 32.5B | 128K | 54.7 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 Coder 14B | 14.7B | 128K | 18.4 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 Coder 7B | 7.61B | 128K | 10.4 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 72B Instruct | 72.7B | 128K | 103.2 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 14B Instruct | 14.7B | 128K | 18.4 GB | ✓ fits | Pre-filled → |
Inference & Serving Capacity
Practical model feasibility, max batch sizes, and KV-cache retention limits for AMD Instinct MI300X.
Llama 3.3 70B
FEASIBLERequires tensor parallelism on multi-GPU
DeepSeek 671B
OOMRequires 4-8 GPU cluster with expert parallelism
Qwen 2.5 32B
FEASIBLEFits comfortably with INT4 quantization
vLLM Throughput (FP8)
~130 tok/s (vLLM, Llama 70B FP8, batch=1)
Estimated tokens/second, single GPU, Llama-class model
Max Context Window (Llama 70B)
128k+ tokens (FP8) — single-GPU, full 70B model + full context headroom
Maximum context length before KV-cache eviction
Hardware Bottleneck Analysis
Whether AMD Instinct MI300X is compute-bound (TFLOPS) or memory-bandwidth bound (GB/s) across workloads.
Bottleneck Classification
Memory-bandwidth bound — 5.3 TB/s HBM3 matches CDNA 3 throughput
Recommended Quantization
FP8 / FP16 native — 192 GB fits 70B FP16 + full KV-cache on one GPU
Best Cluster Topology
8-way InfiniBand-class interconnect (896 GB/s per GPU)
Deep Analysis
MI300X 192 GB HBM3 VRAM Capacity Advantage: The MI300X's 192 GB HBM3 provides 2.4x the VRAM of H100's 80 GB. Running Llama 70B FP16 (~140 GB) on a single MI300X leaves ~52 GB for KV-cache — enabling 128k+ context windows on a single GPU. On 2x H100s (TP=2), the same 70B FP16 model requires splitting across GPUs, introducing NVLink latency. The MI300X runs the full model on one GPU with native FP16 precision. InfiniBand-class at 896 GB/s provides sufficient inter-GPU bandwidth for 8-way tensor parallelism, though software maturity for multi-node MI300X clusters is still evolving. The ROCm 6.x + vLLM integration has matured significantly — Llama 70B FP8 inference is now fully supported on MI300X.
Architecture & Die Breakdown
Architecture
CDNA 3 — 5nm TSMC
TDP
750W
Memory Subsystem
192GB HBM3 at 5.3 TB/s bandwidth. High Bandwidth Memory provides the throughput needed to keep tensor cores fed during large batch inference.
Interconnect
InfiniBand-class 896 GB/s. Standard PCIe bus. Suitable for single-GPU workloads or multi-GPU training with gradient accumulation.
Precision Performance
| Precision | TFLOPS | Use Case |
|---|---|---|
| FP8 | 0 | Training & inference with mixed-precision |
| FP16 | 0 | Full-precision training, fine-tuning, evaluation |
Break-Even ROI Calculator
Monthly hours where reserved pricing beats spot for AMD Instinct MI300X. Above the break-even point, reserve commits save money.
Spheron
RunPod
Lambda Labs
Reserved discount: ~15% vs on-demand (range: 10–20% depending on commitment duration and provider). Break-even at 290 hours/month: if you run AMD Instinct MI300X more than 290 hours per month, reserved pricing saves money. At 720 hours/month (24/7), reserved saves $0+/mo vs on-demand.
Why these numbers? ▾
Spot rates sourced from public cloud provider APIs (Spheron, RunPod, Lambda Labs). Verified September 2026.
Reserved discount: ~15% (range: 10–20%) — observed from provider commitment pricing. See methodology.
Break-even hours derived from observed spot-to-reserved spread across providers.
ℹ️ Why AMD Instinct MI300X break-even hours: 290 hours/month CALCULATED · MEDIUM▾
Derived from observed spot-to-reserved price spread across Spheron, RunPod, Vast.ai, and Lambda Labs. Break-even = reserved_commitment_cost / (spot_rate - reserved_rate). Reserved discount is ~15% on average (range: 10–20% depending on commitment duration).
Source: AMD Instinct MI300X Architecture Whitepaper + ROCm/vLLM Benchmarks · Verified: 2026-09-26T00:00:00Z · Refreshed daily from provider APIs and market scraping
Methodology: Derived from observed spot-to-reserved price spread across Spheron, RunPod, Vast.ai, and Lambda Labs. Break-even = reserved_commitment_cost / (spot_rate - reserved_rate). Range: 10–20% reserved discount.
ℹ️ Why AMD Instinct MI300X FP8 throughput: ~130 tok/s (vLLM, Llama 70B FP8, batch=1) BENCHMARK · MEDIUM▾
Measured via vLLM v0.6.x with PagedAttention v2 and FlashAttention-3 on Ubuntu 24.04 + CUDA 12.4. Llama-class model served at batch=1. Throughput varies with context length, batch size, KV-cache size, and engine configuration.
Source: AMD Instinct MI300X Architecture Whitepaper + ROCm/vLLM Benchmarks · Verified: 2026-09-26T00:00:00Z · Benchmark testing baseline: Ubuntu 24.04, CUDA 12.4, vLLM v0.6.x (PagedAttention v2, FlashAttention-3)
Methodology: Measured via vLLM v0.6.x with PagedAttention v2 and FlashAttention-3. Throughput varies with context length, batch size, KV-cache size, and engine configuration.
Related GPUs
Frequently Asked Questions
How much does it cost to rent an AMD Instinct MI300X per hour?▾
What is the monthly reserved pricing for AMD Instinct MI300X?▾
Can an AMD Instinct MI300X run 70B parameter LLMs?▾
Is spot pricing reliable for distributed AMD Instinct MI300X training?▾
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