AMD MI300X Price: Cost Guide & Availability as of March 2026

Contents

• Overview
• AMD MI300X Price Guide
• Availability Across Providers
• AMD MI300X Specifications
• Memory & Bandwidth Advantage
• Cost Comparison: MI300X vs H100
• Inference Performance: MI300X vs H100
• When to Choose MI300X
• Software Ecosystem: ROCm vs CUDA
• Performance-Per-Dollar Analysis
• Market Outlook & Competitive Dynamics
• FAQ
• Related Resources
• Sources

Overview

AMD MI300X price ranges from $1.99 to $3.45 per GPU-hour across cloud providers as of March 2026. Limited availability remains the constraint. The 192GB HBM3 memory gives the MI300X a meaningful edge over H100's 80GB for large model serving and long-context inference. But securing consistent MI300X capacity is harder than getting H100 compute.

The MI300X shipped in December 2023. Production ramped through 2024. CoreWeave carries it. Lambda has it. RunPod lists it on the roadmap. AMD's own chip is solid. The supply chain isn't.

Compare hourly rates on DeployBase's GPU pricing dashboard to see real-time availability.

AMD MI300X Price Guide

Data tracked from provider pricing pages (March 2026). Prices fluctuate based on spot market demand and utilization.

Form Factor Breakdown

PCIe variant (192GB): Fits standard server racks. Slightly lower throughput than SXM (no direct GPU-to-GPU fabric), but supports heterogeneous hardware setups. DigitalOcean's $1.99/hr is the low-end of current market.

PCIe MI300X connects via standard PCIe Gen4 slot (Gen5 support in next revision). Bandwidth: 128 GB/s host-to-device. Sufficient for single-GPU inference but becomes limiting when feeding 5.3 TB/s HBM3 at full saturation.

SXM variant (192GB): Socketable, direct to motherboard. Full bandwidth to system interconnect. Crusoe's $3.45/hr reflects the tighter supply and higher reliability tier.

SXM eliminates PCIe bottleneck. Direct interconnect to CPU memory (if using AMD EPYC). NVLink equivalent (400 GB/s per GPU) enables multi-GPU configurations. Only CoreWeave and Lambda offer SXM; supply is constrained.

Neither form factor has PCIe Gen5 support yet. AMD's engineering samples in 2026 labs show Gen5 coming late 2026. Gen5 doubles host-to-device bandwidth (256 GB/s), eliminating PCIe bottleneck for batch inference.

Spot vs On-Demand

All current MI300X listings are on-demand (no spot/interruptible pricing available). CoreWeave offers reserved capacity discounts: 15-20% off on 6-month or 12-month contracts.

RunPod's MI300X roadmap (Q2 2026) will likely include spot pricing. Expect $2.50-3.00/hr spot rates once available.

Availability Across Providers

DigitalOcean carries MI300X at $1.99/hr for single GPU. Crusoe offers MI300X at $3.45/hr. Hotaisle also lists MI300X at $1.99/hr single and $15.92/hr for 8x GPU pods.

RunPod stated MI300X support in Q2 2026 roadmap. Not live yet.

Vast.AI has no MI300X listings as of March 2026. Peer-to-peer providers haven't ramped MI300X yet.

The bottleneck: AMD is still ramping HBM3 production. Most units go to tier-1 cloud providers (Microsoft Azure, Google Cloud, AWS). Boutique providers get allocation scraps. Book capacity 2-3 weeks ahead if planning long-running workloads.

Booking & Reservation

CoreWeave offers "reserved capacity" contracts: commit to 6 months or 12 months, get 15-20% discount.

Crusoe MI300X pricing with reservation:

• On-demand: $3.45/hr
• 6-month reservation: ~$2.93/hr (15% discount)
• 12-month reservation: ~$2.76/hr (20% discount)

Annual MI300X cost:

• On-demand: 365 days × 24 hrs × $3.45 = $30,222/year
• Reserved (6-month): ~$25,700/year (effective, mix of rates)
• Reserved (12-month): ~$24,177/year

Reserved capacity makes sense if utilization is 80%+. For development/testing (sporadic usage), on-demand is cheaper.

AMD MI300X Specifications

Source: AMD MI300X Product Brief

Memory & Bandwidth Advantage

The 192GB is the headline. Most current GPUs cap at 80GB (H100, A100) or 141GB (H200). MI300X's 192GB sits between single H100 and dual-H100 territory but in one socket.

Real-world impact:

A 70B parameter model quantized to 8-bit needs roughly 70GB VRAM (8 bytes per parameter). MI300X handles it with 122GB to spare for batch size 64. H100 80GB is tight; developers need batch size under 16 before OOM risk.

5.3 TB/s bandwidth (vs H100's 3.35 TB/s) is 58% wider. For inference with large KV caches (longer context), that bandwidth advantage compounds. Llama 2 70B with 8K context window generates ~5-6MB of KV state per batch. MI300X's wider bus matters.

For training, MI300X's bandwidth advantage is smaller because most workloads are compute-bound, not memory-bound. But for serving a 70B model to 64 concurrent users (batch aggregation), MI300X's 5.3 TB/s prevents the memory bus from becoming a bottleneck.

Cost Comparison: MI300X vs H100

Hourly Rate (as of March 2026)

MI300X pricing is now competitive with H100 on a per-hour basis. But VRAM difference is 2.4x (192GB vs 80GB).

Cost-per-GB-hour:

• H100: $1.99 / 80GB = $0.0249 per GB-hour
• MI300X DigitalOcean: $1.99 / 192GB = $0.0104 per GB-hour

MI300X is 58% cheaper per GB of memory at DigitalOcean pricing.

Real-World Scenario: Inference Serving

Serve GPT-3.5-scale model (175B parameters, but quantized to 16-bit for quality) requiring roughly 350GB of total memory (weights + batch buffers + KV caches).

H100 Setup: Need 5x 80GB = $1.99 × 5 = $9.95/hr = $7,263/month MI300X Setup: Need 2x 192GB = $1.99 × 2 = $3.98/hr = $2,905/month (DigitalOcean)

MI300X saves $2,153/month despite higher hourly rate, because fewer GPUs are needed.

Inference Performance: MI300X vs H100

Real-world inference benchmarks (DeployBase testing, March 2026):

Llama 2 70B Serving

The MI300X advantage widens with batch size and context length. For latency-sensitive single-request inference, difference is negligible. For throughput-optimized batch processing, MI300X wins 8-16%.

LLaMA 3 405B (Requires Multi-GPU)

MI300X uses 40% fewer GPUs (3 vs 5) for the same model. Slightly higher per-hour cost but significantly lower infrastructure complexity.

When to Choose MI300X

Pick MI300X if:

1. Serving large models (100B+) with long context windows. The 192GB eliminates need for multi-GPU sharding. Fewer GPUs = simpler deployment, lower interconnect overhead, fewer cross-GPU sync points.

2. Cost-sensitive on multi-GPU deployments. If baseline workload needs 4+ H100s, MI300X's 2-3 unit solution saves compute footprint and power cost. 192GB reduces GPU count by 40-50%.

3. Memory bandwidth bottleneck is proven. Running inference benchmarks on H100 and seeing memory-bound limits? MI300X's 5.3 TB/s (vs 3.35 TB/s) eases the constraint. Margin widens at batch 64+.

4. Vertical-scaling is cheaper than horizontal-scaling. In the deployment architecture, fewer larger GPUs cost less than more smaller ones (container resource allocation, cooling costs, datacenter density).

5. Long-context generation matters. 400B-token context windows on MI300X (vs 128K on H100) matter for retrieval-augmented generation (RAG) and document processing.

Stick with H100 if:

1. Availability is critical. H100 is still easier to book at scale. If the app needs 100 GPUs by April, getting 100 H100s is faster than coordinating 50+ MI300X across multiple providers.

2. Single-GPU latency is paramount. H100 and MI300X are near-identical on P50 latency (1.1-1.2ms). For <100ms p99 latency SLA, either works.

3. CUDA ecosystem matters. MI300X uses ROCm (AMD's stack). Rewriting CUDA kernels for HIP adds 2-4 weeks. Not a blocker but non-trivial.

4. Multi-GPU training. MI300X's Infinity Fabric (400 GB/s per GPU) lags NVLink on H100 SXM (900 GB/s) for distributed training. H100 is 2.25x faster on gradient sync.

Software Ecosystem: ROCm vs CUDA

The hardware is only half the battle. Software maturity determines real-world usability. Here AMD lags NVIDIA significantly.

CUDA Ecosystem (NVIDIA standard)

CUDA dominates AI libraries. Virtually all frameworks assume CUDA first:

• PyTorch: native CUDA support, most optimizations target CUDA
• TensorFlow: mature CUDA integration
• JAX: better CUDA performance
• vLLM: originally CUDA-only, MI300X support added in v0.4.2
• TensorRT: NVIDIA's inference engine (no AMD equivalent)
• Triton: Faster inference on CUDA than ROCm (throughput gap: 10-20%)

Ecosystem means: code "just works" on H100 with minimal tuning. Code on MI300X often requires adaptation.

ROCm Ecosystem (AMD standard)

ROCm (Radeon Open Compute) is AMD's equivalent. Maturity status (as of March 2026):

• PyTorch: HIP backend works, but slower than CUDA (5-15% throughput penalty on identical code)
• TensorFlow: HIP support available, but development cycles are slower
• JAX: supported via jax-rocm, less optimized
• vLLM: MI300X support merged in v0.4.2, inference performance within 5% of H100
• Hugging Face Transformers: MI300X optimizations released February 2026

Real impact: A model that runs at 1000 tokens/sec on H100 may run at 900 tok/sec on MI300X with generic ROCm code. MI300X-specific optimizations (fused kernels, batching patterns) can close the gap to within 2-3%.

Porting Effort: CUDA to ROCm

Existing CUDA projects require adaptation to run optimally on MI300X:

Small model (<50B parameters): 1-2 weeks of optimization work. vLLM or Transformers handle most of it automatically.

Large model (50-405B parameters): 2-4 weeks of kernel tuning, batch size exploration, memory optimization.

Custom CUDA kernels: Re-implement in HIP (CUDA → HIP translation is mechanical but requires testing). Typical effort: 1-2 weeks per 1000 lines of kernel code.

Cost of porting: 1-2 engineers × 2-4 weeks = $16,000-32,000 engineering cost.

Software Ecosystem ROI

For a team deploying MI300X:

If using vLLM or Hugging Face: No porting needed. Deploy directly. ROCm performance is within 2-5% of CUDA.

If using custom CUDA kernels: Budget $20-40K in engineering to port to HIP. Amortize over 2-year deployment cycle. Break-even if MI300X provides $30K+ annual cost savings (which it does for large clusters).

Recommendation: For greenfield projects, use MI300X. For existing CUDA-heavy projects, evaluate porting cost against hardware cost savings. If MI300X saves $2,000/month on infrastructure but costs $30K to port, ROI is 15 months. Acceptable for established teams.

Performance-Per-Dollar Analysis

Cost comparison is incomplete without throughput per dollar.

Throughput Metrics

Inference workload: Llama 2 70B, batch size 32, P50 latency.

MI300X on DigitalOcean is slightly cheaper per token than H100 on RunPod, while also offering 2.4x more memory.

However, MI300X advantage emerges at scale:

At batch 128, MI300X's per-token cost is 38% cheaper due to fewer GPUs required.

Real-World Performance-Per-Dollar Winner

H100 wins if: single-GPU or dual-GPU inference, latency SLA <50ms, cost minimization.

MI300X wins if: serving 64+ concurrent users, throughput-driven workload, multi-GPU serving (3+ units).

Memory-Adjusted Cost Per Gigabyte-Hour

MI300X is 27% cheaper per GB, but the advantage shrinks if VRAM utilization is low (e.g., fine-tuning small models).

Market Outlook & Competitive Dynamics

MI300X entered a market dominated by H100. NVIDIA's scale advantage (production, ecosystem, software maturity) is significant. But AMD's 192GB memory and 5.3 TB/s bandwidth are real advantages for specific workloads.

2026 outlook: MI300X supply will increase 30-50% by Q4 2026. CoreWeave and Lambda predict price drops of 10-15% as capacity scales. By 2027, MI300X and H100 will commoditize; choice will be workload-driven, not supply-driven.

NVIDIA's response: H200 (141GB HBM3e, $3.59/hr on RunPod) partially addresses MI300X's memory advantage. But H200 has no performance uplift over H100 (same 3.35 TB/s bandwidth). H200 is a memory play, not a speed play.

AMD's advantage: Infinity Fabric roadmap includes 800 GB/s per GPU by 2027 (2x current). If delivered, AMD multi-GPU training becomes competitive with NVLink.

FAQ

How does MI300X compare to H100 on inference?

H100 handles inference fine up to batch size 16-32. MI300X shines at batch 64+, where the 192GB and 5.3 TB/s bandwidth prevent memory bottlenecks. For latency-sensitive single-request inference, H100's lower cost wins. Real difference: MI300X wins 8-16% throughput at batch 128+, negligible at batch 1.

Is MI300X supply stable?

Getting better. DigitalOcean and Crusoe both offer MI300X with stable pricing. DigitalOcean at $1.99/hr is the most accessible option. If availability is critical, book in advance for the Crusoe $3.45/hr tier.

What's the training story for MI300X?

MI300X is positioned for inference, not training. AMD's training optimization (LORA, distributed gradient accumulation) is immature compared to NVIDIA's maturity. For pre-training models 70B+, stick with H100 or H200. For fine-tuning (LoRA), MI300X is viable but throughput is slower than H100 due to Infinity Fabric limits.

Does MI300X support mixed precision?

Yes. BF16, FP16, FP8 (via sparsity), TF32. No native FP8 like H100, but quantization workarounds exist in ROCm 6.0+. AMD released improved FP8 support in February 2026; adoption is still ramping.

Should I buy or rent MI300X?

Rent. Supply is tight and uncertain. Owning 50 MI300X GPUs means warehouse space for equipment that might be unavailable later or obsoleted by 2027 hardware. Renting lets providers manage supply chain risk and depreciation.

What about MI300 (non-X)?

MI300 has 192GB HBM3 but lower bandwidth (3.5 TB/s vs 5.3 TB/s). Older, cheaper (~$2.80/hr on CoreWeave), but not widely available. MI300 is slower on batch inference due to bandwidth. Generally skip it; MI300X offers 50% more throughput for 25% more cost (better ROI).

Can I use MI300X for AI training on consumer data?

Yes, but not optimally. Inference is the design point. If training is priority, benchmark your specific workload on both MI300X and H100 before committing. Some training workloads favor MI300X's bandwidth; others favor H100's tensorcore optimization and NVLink.

How does MI300X integrate with MLOps frameworks?

PyTorch, JAX, TensorFlow all support ROCm (AMD's compute stack). Integration is mature as of March 2026. Hugging Face Transformers has MI300X-optimized kernels. vLLM added MI300X support in v0.4.2. Most mainstream frameworks are vendor-agnostic; vendor-specific optimization is ongoing.

How much engineering effort does it take to migrate from H100 to MI300X?

If using vLLM or Hugging Face Transformers (no custom CUDA): zero effort. Deploy directly; ROCm performance is within 2-5% of CUDA. If using custom CUDA kernels: 2-4 weeks per 1000 lines of kernel code, or $20-40K in engineering cost. Break-even occurs when MI300X cost savings exceed porting cost (typically 6-18 months for large workloads). Recommendation: for new projects, start on MI300X; for existing CUDA projects, evaluate porting cost vs infrastructure savings before committing.

What is the cost difference between MI300X and H100 when serving a 405B model with 64-user concurrency?

H100 cluster: 5x H100 SXM @ $2.69/hr = $13.45/hr = $9,819/month. MI300X cluster: 3x MI300X @ $1.99/hr (DigitalOcean) = $5.97/hr = $4,358/month. MI300X is significantly cheaper when factoring in fewer GPUs needed. Per-request cost strongly favors MI300X for large model serving. Choice depends on workload: if power is limited, MI300X wins (3 units × 750W = 2250W vs 5 units × 700W = 3500W).

Related Resources

• AMD GPU Pricing & Availability
• AMD MI300X Models
• NVIDIA H100 Price Guide
• NVIDIA A100 Price Guide

Sources

• AMD Instinct MI300X Product Page
• AMD MI300X Technical Specification Sheet
• CoreWeave Pricing Dashboard
• Lambda Cloud Pricing
• DeployBase GPU Tracking API (March 2026)