MI300X vs B200—The Next‑Gen GPU Battle

Introduction—The GPU Arms Race

Generative AI applications exploded in late‑2023 and 2024, driving record demand for GPUs and exposing a split between memory‑rich accelerators and latency‑oriented chips. By the end of 2025, two competitors dominate the data‑center conversation: AMD’s Instinct MI300X and NVIDIA’s Blackwell B200. Each represents a different philosophy: memory capacity and value vs raw compute and ecosystem maturity. Meanwhile, AMD announced MI355X and MI325X road‑map entries, promising larger HBM3E stacks and new low‑precision math modes. This article synthesizes research, independent benchmarks, and industry commentary to help you pick the best GPU, with a particular focus on Clarifai’s multi‑cloud inference and orchestration platform.

Quick Digest – What You’ll Learn

Expert Insights:

• Lisa Su on open benchmarking: The chair and CEO of AMD praised open InferenceMAX benchmarks for providing transparent, nightly results and underscoring the competitive performance of MI300, MI325X and MI355X. Such transparency builds trust and highlights the importance of real‑world measurements.
• TensorWave commentary: Independent cloud provider TensorWave noted that MI355X consistently beat competing GPUs on total cost of ownership (TCO) across vLLM workloads and delivered a ~3× better tokens‑per‑megawatt improvement over previous generations. They also emphasized the growing maturity of AMD’s software stack.
• Research on MI300X vs H100: Analysis from 2025 shows MI300X often achieves only 37–66 % of H100/H200 performance due to software overhead but excels in memory‑bound tasks, sometimes doubling throughput when inference workloads saturate memory bandwidth. This nuance underscores the importance of workload matching.

With these high‑level findings in mind, let’s dive into the architectures, performance data, economics, software ecosystems, use cases and future outlook for MI300X, MI325X, MI355X, and B200—and explain how Clarifai’s compute orchestration can help you build a flexible, cost‑efficient GPU stack.

Architecture Deep Dive – CDNA 3/4 vs Blackwell

How Do the Architectures Differ?

The MI300X and its successors (MI325X, MI355X) are built on AMD’s CDNA 3 and CDNA 4 architectures, which use chiplet‑based designs to pack compute and memory into a single accelerator. Each chiplet, or XCD, is fabricated on a 3 nm or 4 nm process (depending on generation), and multiple chiplets are stitched together via the Infinity Fabric. This allows AMD to stack 192 GB of HBM3 (MI300X) or 256 GB (MI325X) or 288 GB of HBM3E (MI355X) around compute dies, delivering 5.3 TB/s to 8 TB/s of bandwidth. The memory sits close to compute, reducing DRAM round‑trip latency and enabling large language models to run on a single device without sharding.

The B200, by contrast, uses NVIDIA’s Blackwell architecture, which adopts a dual‑die package. Two reticle‑limit dies share a 10 TB/s interconnect and present themselves as a single logical GPU, with up to 180 GB or 192 GB of HBM3E memory and approximately 8 TB/s of bandwidth. NVIDIA pairs these chips with NVLink‑5 switches to build systems like the NVL72, where 72 GPUs act as one with a unified memory space.

Spec Comparison Table (Numbers Only)

Why the Differences Matter: MI355X’s 288 GB of memory can hold models with 500+ billion parameters, reducing the need for tensor parallelism and minimizing communication overhead. The MI355X’s support for FP6 yields up to 20 PFLOPS of ultra‑low precision throughput, roughly doubling B200’s capacity in this mode. Meanwhile, the B200’s dual‑die design simplifies scaling and, paired with NVLink‑5, forms a unified memory space across dozens of GPUs. Each approach has implications for cluster design and developer workflow, which we explore next.

Interconnects and Cluster Topology

In multi‑GPU systems, the interconnect often determines how well tasks scale. NVIDIA uses NVLink‑5 and NVSwitch fabric; the NVL72 system interconnects 72 GPUs and 36 CPUs into a single pool, delivering around 1.4 EFLOPS of compute and a unified memory space. AMD’s alternative is Infinity Fabric, which links up to eight MI300X or MI355X GPUs in a fully connected mesh with seven high‑speed links per card. Each pair of MI355X cards communicates directly at roughly 153 GB/s, yielding about 1.075 TB/s total peer‑to‑peer bandwidth.

Expert Insights (Architecture)

• Memory capacity vs compute: Analysts note that the MI355X’s 288 GB HBM3E provides 1.6× the memory of B200. This allows single‑GPU inference for models exceeding 500 B parameters, reducing off‑chip communication and enabling simpler scaling.
  
  
• Precision innovations: AMD’s introduction of FP6/FP4 modes yields up to 20 PFLOPS throughput—about twice the ultra‑low precision performance of B200. For double precision, MI355X offers 79 TFLOPS, roughly double the B200’s FP64 performance, benefiting mixed HPC+AI workloads.
  
  
• Energy trade‑off: The MI355X’s 1.4 kW TDP is high, but energy per token improves; runs of Llama‑3 FP4 show 30 % more tokens per watt compared with MI300X. This suggests that the extra power draw yields more work per joule.
  
  
• Cluster design: Infinity Fabric’s fully‑connected mesh offers ~1.075 TB/s per card, whereas NVLink‑5 uses switch fabrics. AMD’s approach reduces the need for external switches but relies on external CPUs, while NVLink‑coupled systems integrate Grace CPUs for tighter coupling.
  
  
• Road‑map differentiation: MI325X sits between MI300X and MI355X with 256 GB memory and 6 TB/s bandwidth. It’s aimed at customers who want more memory than MI300X but cannot accommodate the power and cooling requirements of MI355X.

Performance Benchmarks – Latency, Throughput & Scaling

Real‑World Benchmark Data

Single‑GPU inference: In independent MLPerf‑inspired tests, MI300X delivers 18 752 tokens per second on large language model inference, roughly 74 % of H200’s throughput. Latency scales at around 4.20 ms for an eight‑GPU MI300X cluster, compared with 2.40 ms on competing platforms. The lower efficiency arises from software overheads and slower kernel optimizations in ROCm compared with CUDA.

Training performance: On the Llama‑2 70B LoRA FP8 workload, the MI355X slashes training time from ~28 minutes on MI300X to just over 10 minutes. This represents a 2.8× speed‑up, attributable to enhanced HBM3E bandwidth and ROCm 7.1 improvements. When compared to the average of industry submissions using the B200 or GB200, the MI355X’s FP8 training times are within ~10 %—showing near parity.

InferenceMax results: An open benchmarking initiative running vLLM workloads across multiple cloud providers concluded that the MI355X matches or beats competing GPUs on tokens per dollar and offers a ~3× improvement in tokens per megawatt compared with previous AMD generations. The same report noted that MI325X surpasses the H200 on TCO for summarization tasks, while MI300X sometimes outperforms the H100 in memory‑bound regimes.

Latency vs throughput: The MI355X emphasises memory capacity over minimal latency; early engineering samples show inference throughput improvements of 2× compared with B200 on 400 B+ parameter models using FP4 precision. However, the B200 typically maintains a latency advantage for smaller models and real‑time applications.

Scaling considerations: Multi‑GPU efficiency depends on both hardware and software. The MI300X and MI325X scale well for large batch sizes but suffer when many small requests stream in—a common scenario for chatbots. The MI355X’s larger memory reduces the need for pipeline parallelism and thus reduces communication overhead, enabling more consistent scaling across workloads. NVLink‑5’s unified memory space in NVL72 systems provides superior scaling for extremely large models (>400 B), albeit at high cost and power consumption.

Expert Insights (Performance)

• Independent latency studies: Researchers have found MI300X’s 4.20 ms eight‑GPU latency to be 37–75 % higher than H200’s latency, underscoring the current maturity gap in ROCm’s kernel optimizations.
• Throughput leadership at scale: Despite slower kernels, MI300X’s memory allows it to saturate throughput for huge context windows, sometimes doubling H100/H200 performance on memory‑bound tasks. MI355X extends this by delivering near‑parity FP8 training performance relative to aggregated competitor submissions.
• Open benchmarks on TCO: Independent InferenceMAX benchmarks highlight MI355X’s TCO advantage and note that MI325X beats H200 on cost across all interactivity levels. The report also emphasises the software maturity of ROCm, citing fewer bugs and easier fixes.
• Clarifai’s experience: Clarifai’s own engineers observe that MI300X achieves only 37–66 % of the performance of H100/H200 due to software overhead but can outperform H100 in memory‑bound scenarios, delivering up to 40 % lower latency and doubling throughput for certain models. They recommend dynamic batching and memory‑aware scheduling to exploit the GPU’s strengths.
  
  

Economics – Cost, Power & Carbon Footprint

Price and Power Comparison

Card price: According to market surveys, the B200 retails for US$35–40 k, while the MI300X sells for US$10–15 k. MI325X is expected around US$16–20 k (unofficial), and MI355X is projected at US$25–30 k. These price differentials reflect not just chip cost but also memory volume, packaging complexity and vendor premiums.

Power consumption: The B200 draws roughly 1 kW per card, whereas the MI300X draws ~750 W. MI355X raises the TDP to ~1.4 kW, requiring liquid cooling. Despite the higher power draw, early data shows a 30 % tokens‑per‑watt improvement compared with MI300X. Energy‑aware schedulers can exploit this by running MI355X at high utilization and powering down idle chips.

Training pod costs: AI‑Stack’s economic analysis estimates that an eight‑GPU MI300X pod costs around US$3 M including infrastructure, while a B200 pod costs ~US$9 M due to higher card prices and higher power consumption. This translates to lower capital expenditure (CAPEX) and lower operational expenditure (OPEX) for MI300X, albeit with some performance trade‑offs.

Tokens per megawatt: Independent benchmarks found that MI355X delivers a ~3× higher tokens‑per‑megawatt score than its predecessor, a critical metric as electricity costs and carbon taxes rise. Tokens per watt matters more than raw FLOPS when scaling inference services across thousands of GPUs.

Carbon and Regulation Considerations

The EU AI Act and similar regulations emerging worldwide include provisions to track energy use and carbon emissions of AI systems. Data centers already consume over 415 TWh annually, with projections to reach ~945 TWh by 2030. A single NVL72 rack can draw 120 kW, and a rack of MI355X modules can exceed 11 kW per 8 GPUs. Selecting GPUs with lower power and higher tokens per watt becomes essential—not only for cost but also for regulatory compliance. Clarifai’s energy‑aware scheduler helps customers monitor grams of CO₂ per prompt and allocate workloads to the most efficient hardware.

Expert Insights (Economics)

• Cost‑per‑token leadership: Analysts from independent blogs highlight that MI355X delivers 30–40 % more tokens per dollar than B200 for FP4 inference workloads. This is due to the combination of lower acquisition cost and high throughput.
  
  
• CAPEX differences: An eight‑GPU MI300X pod costs ~US$3 M vs ~US$9 M for a comparable B200 pod. This difference scales when building clusters of hundreds or thousands of GPUs.
  
  
• Power vs memory trade‑off: MI355X requires liquid cooling and draws ~1.4 kW, but its 30 % tokens‑per‑watt improvement over MI300X means that energy costs per token may still be favourable.
  
  
• Sustainability mandates: Data center power consumption is rising sharply. Tighter carbon regulations will incentivize tokens‑per‑watt metrics and may make lower‑power GPUs (MI300X, MI325X) attractive despite lower peak throughput.
  
  

Software Ecosystems – CUDA vs ROCm & Developer Experience

CUDA’s Mature Ecosystem

CUDA remains the most widely adopted GPU programming framework. It offers TensorRT‑LLM for optimized inference, a comprehensive debugger, and a large library ecosystem. Developers benefit from extensive documentation, community examples and faster time‑to‑production. NVIDIA’s Transformer Engine 2 provides FP4 quantization routines and features like Multi‑Transformer for merging attention blocks.

ROCm’s Rapid Progress

AMD’s open‑source ROCm has matured rapidly. In ROCm 7, AMD added graph capture primitives aligned with PyTorch 2.4, improved kernel fusion, and introduced support for FP4/FP6 datatypes. Upstream frameworks (PyTorch, TensorFlow, JAX) now support ROCm out of the box, and container images are available within 24 hours of new releases. HIP tools now cover about 92 % of CUDA 12.5 device APIs, easing migration.

Reports from independent benchmarking teams indicate that the ROCm/vLLM stack exhibits fewer bugs and easier fixes than competing stacks. This is due in part to open‑source transparency and rapid iteration. ROCm’s open nature also allows the community to contribute features like Flash‑Attention 3, which is now available on both CUDA and ROCm.

Developer Productivity and Debugging

The CUDA moat is still real: developers commonly find it easier to debug and optimize workloads on CUDA due to mature profiling tools and a rich plugin ecosystem. ROCm’s debugging tools are improving, but there remains a learning curve, and patching issues may require deeper domain knowledge. On the positive side, ROCm’s open design means that community bug fixes can land quickly. Engineers interviewed by independent news sources note that AMD’s software issues often revolve around kernel tuning rather than fundamental bugs, and many report that ROCm’s improvements have narrowed the performance gap to within 10–20 % of CUDA.

Expert Insights (Software)

• Rapid ROCm improvements: Research notes that ROCm’s performance lag vs CUDA has shrunk from 40–50 % to 10–30 % for most workloads. The stack still lags in some kernels, but the gap is narrowing.
• Cost vs convenience: ROCm hardware is typically 15–40 % cheaper than CUDA‑equipped systems, but installation and setup may require more expertise. This trade‑off is important for teams with limited budgets or a desire for vendor independence.
• Open‑source momentum: The community has added features like Flash‑Attention 3 and Paged‑Attention to ROCm quickly, enabling comparable features to TensorRT‑LLM. Clarifai engineers note that many of their inference pipelines run identically on ROCm and CUDA with minimal code changes.
• Clarifai’s platform support: Clarifai’s compute orchestration platform supports both CUDA and ROCm clusters. It abstracts away hardware differences, enabling developers to run inference and fine‑tuning across mixed GPU fleets. Integrated scheduling automatically chooses the most cost‑efficient hardware, factoring in latency requirements, memory needs and carbon considerations.

Use Cases & Real‑World Applications

Where Each GPU Excels

MI300X and MI325X

• Large language model inference: With 192–256 GB memory, these GPUs can run 70–110 billion‑parameter models on a single card. This enables single‑GPU inference for ChatGPT‑class models and retrieval‑augmented generation (RAG) pipelines without splitting the model across multiple devices. Clarifai’s platform uses MI300X for memory‑heavy inference and dynamic batch scheduling.
  
  
• RAG pipelines: The extra memory allows the query encoder, retriever and generator to reside on one GPU. Combined with Clarifai’s multimodal search and Federated Query tools, this reduces latency and simplifies deployment.
  
  
• Cost‑sensitive inference: At roughly one‑third the price of B200, MI300X offers cost‑efficient inference at scale. For high‑throughput endpoints where response times above 50 ms are acceptable, MI300X can halve operating costs.
  
  
• Memory‑bound HPC tasks: Mixed HPC/AI workloads (e.g., seismic inversion with a transformer surrogate) benefit from the high FP64 throughput of MI355X (79 TFLOPS) and the large memory of MI325X/MI355X.
  
  

B200

• Ultra‑low latency applications: The B200 leads in sub‑100 ms latency due to its mature CUDA stack and optimized kernel libraries. Real‑time copilots, voice assistants and streaming models requiring instantaneous responses benefit from the B200’s lower latency and higher single‑GPU throughput.
  
  
• Massive pre‑training: When training models with 400 B+ parameters, NVL72 or multi‑B200 clusters provide unmatched compute density and a unified memory space via NVLink‑5. The high price and power draw are offset by time‑to‑train savings for mission‑critical workloads.
  
  
• Mature ecosystem: Many pretrained models and fine‑tuning examples are developed on CUDA first. Organisations with existing CUDA expertise may prefer B200 for developer productivity and easier debugging.
  
  

MI355X

• Giant model inference and HPC: The 288 GB memory allows models up to 500 B parameters to fit on a single card. This eliminates tensor parallelism for extremely large MoE models (e.g., Mixtral 8×7B or DeepSeek R1). Early engineering results show 2× throughput over B200 on models like Llama 3.1 405B in FP4 precision.
  
  
• Mixed precision training: MI355X’s support for FP4, FP6, and FP8 modes, with 20 PFLOPS FP6/FP4 throughput, enables both efficient inference and training. In MLPerf 5.1, MI355X finished Llama‑2 70B LoRA training in 10.18 minutes, within ~10 % of average competitor submissions.
  
  
• HPC+AI workloads: With 79 TFLOPS FP64 throughput, MI355X is well‑suited for scientific computing plus AI surrogates—think CFD, weather modeling or financial simulations where double precision is vital.
  
  
• Energy‑aware inference: Despite its high TDP, MI355X’s large memory reduces off‑chip transfers and shows 30 % more tokens per watt than MI300X. Combined with Clarifai’s energy scheduler, this can yield lower CO₂ per prompt.
  
  

Regional Availability and Local Cloud Options

For readers in India (notably Chennai), availability matters. Major Indian cloud providers are starting to offer MI300X and MI325X instances via local data centers. Some decentralized GPU marketplaces also rent MI300X and B200 capacity at lower cost. Clarifai’s Universal GPU API integrates with these platforms, allowing you to deploy retrieval‑augmented systems locally while maintaining centralised management.

Expert Insights (Use Cases)

• Tokens per watt improvements: Early tests show 30 % more tokens per watt on MI355X vs MI300X for Llama‑3 FP4 inference. This efficiency is crucial for providers operating under energy caps.
  
  
• Single‑GPU inference for giant models: MI355X’s 288 GB memory enables 400–500 B parameter models to run without sharding, which drastically reduces network complexity and latency.
  
  
• HPC + AI synergy: The 79 TFLOPS FP64 throughput and high memory bandwidth of MI355X make it ideal for simulations that incorporate neural components, such as seismic inversion or climate modeling.
  
  
• Clarifai case study: Clarifai reports that using MI300X for RAG pipelines reduced inference cost by ~40 % versus using H100, thanks to memory‑rich single‑GPU inference and dynamic batching.
  
  

Future Outlook – Emerging GPUs & Roadmap

MI325X, MI350 and MI355X

AMD’s roadmap fills the gap between MI300X and MI355X with MI325X, featuring 256 GB HBM3E and 6 TB/s bandwidth. Independent analyses suggest MI325X matches or slightly surpasses H200 for LLM inference and offers 40 % faster throughput and 30 % lower latency on certain models. MI355X, the first CDNA 4 chip, takes the memory up to 288 GB, adds FP6 support and boasts 20 PFLOPS FP6/FP4 throughput, with double‑precision performance at 79 TFLOPS. AMD claims MI355X offers up to 4× theoretical compute over MI300X and up to 1.2× higher inference throughput than B200 on certain vLLM workloads.

Grace‑Blackwell, GB200 and B300

NVIDIA’s roadmap includes Grace‑Blackwell (GB200), a CPU‑GPU superchip that connects a B200 with a Grace CPU via NVLink‑C2C, forming a unified package. GB200 systems promise 1.4 EFLOPS of compute across 72 GPUs and 36 CPUs and are targeted at training models over 400 B parameters. The B300 (Hopper refresh) is expected to deliver FP4/FP8 efficiency improvements and integrate with the Grace ecosystem.

Supply Chain and Sustainability Issues

Supply constraints for HBM memory remain a limiting factor. Experts warn that advanced process nodes and 3D stacking techniques will keep memory scarce until 2026. Regulatory pressures like the EU AI Act are pushing companies to track carbon per prompt and adopt energy‑efficient hardware. Expect tokens‑per‑watt and cost‑per‑token metrics to drive purchasing decisions more than peak FLOPS.

Expert Insights (Outlook)

• Performance parity with H200: Independent analysts report that MI325X is on par with H200 and sometimes outperforms it for inference. MI355X aims to deliver a 20–30 % throughput advantage over B200 in some vLLM workloads.
  
  
• Software cadence: The success of these chips will depend on ROCm and CUDA roadmaps. AMD’s open ecosystem may accelerate innovations like FP4 training, while NVIDIA’s proprietary stack may continue to dominate in early adopters.
  
  
• HBM supply constraints: Memory capacity increases will strain supply chains, potentially making the MI355X more expensive or limited in availability until the second half of 2026.
  
  
• Sustainability regulation: Carbon taxes and energy reporting requirements will push enterprises toward energy‑aware schedulers and tokens‑per‑watt metrics. Clarifai’s platform already offers energy‑aware scheduling to optimize for carbon footprint.
  
  

Decision Matrix & Buyer’s Guide – Choosing the Right GPU

Step‑by‑Step Evaluation Process

1. Identify the workload type. Are you serving inference, performing fine‑tuning, or training from scratch? Memory‑bound inference benefits from MI300X/MI325X/MI355X, while latency‑sensitive real‑time inference may justify the B200.
   
   
2. Determine model size and memory requirements. For models ≤70 B parameters, MI300X suffices; for 70–110 B, MI325X offers headroom; for >110 B or multi‑MoE architectures, MI355X or NVL72 systems are required. Memory size influences how many tensor parallelism shards are needed.
   
   
3. Set latency and throughput targets. Real‑time assistants needing <100 ms latency favour B200. Batch workloads tolerant of 150–300 ms latency can leverage MI300X’s cost advantage. Throughput per card matters for high‑traffic APIs.
   
   
4. Estimate cost per token and power budget. Multiply GPU price by required quantity; factor in power draw (kW) and local electricity rates. MI355X has a high TDP but may deliver the lowest cost per token due to throughput.
   
   
5. Assess software maturity and ecosystem. Teams heavily invested in CUDA may prefer B200 for productivity. Organisations seeking open ecosystems and cost savings might adopt MI300X/MI325X/MI355X. Clarifai’s orchestration layer mitigates software differences by providing uniform APIs and automated tuning.
   
   
6. Consider sustainability and regulation. Evaluate grams of CO₂ per prompt, local carbon taxes and cooling infrastructure. High‑power GPUs may require liquid cooling and face restrictions in certain regions. Use Clarifai’s energy‑aware scheduler to allocate workloads to lower‑carbon hardware.
   
   

Pro/Con Lists:

Questions to Ask Your Cloud Provider

• What is the availability of MI300X/MI355X in your region? Are there waitlists?
  
  
• What are the power requirements and cooling methods? Do you support liquid cooling for MI355X?
  
  
• How does the provider measure cost per token and grams CO₂ per prompt? Are there energy‑aware scheduling options?
  
  
• What support exists for ROCm? Does the provider maintain tuned container images for frameworks like vLLM and SGLang?
  
  
• Can you provision heterogeneous clusters mixing MI300X, H100/H200 and B200? Does the orchestration layer abstract the differences?
  
  

Expert Insights (Decision Guidance)

• Latency vs cost matrix: Analysts suggest using B200 for tasks requiring <100 ms latency, MI300X or MI325X for budget‑constrained inference, and MI355X or NVL72 for extremely large models and HPC workloads.
  
  
• TCO matters: A cost‑per‑token advantage of 30–40 % on MI355X may outweigh a 10 % latency penalty for many enterprise workloads. Clarifai’s orchestration can help by routing low‑latency traffic to B200 and high‑throughput tasks to MI355X.
  
  
• Mixed‑fleet strategy: There is no single champion GPU; the optimal configuration often mixes memory‑rich and compute‑rich hardware. Clarifai’s platform supports heterogeneous clusters and provides a Universal GPU API to streamline development.
  
  

Conclusion – No Single Champion, Only Best‑Fit Solutions

The race between MI300X, MI325X, MI355X and B200 underscores a broader truth: the “best” GPU depends on your workload, budget, and sustainability goals. MI300X offers an affordable path to memory‑rich inference but trails in raw throughput. MI325X bridges the gap with more memory and bandwidth, edging out the H200 in some benchmarks. MI355X takes memory capacity and ultra‑low precision compute to the extreme, delivering high tokens per watt and cost‑per‑token leadership but requiring significant power and advanced cooling. B200 remains the latency king and boasts the most mature software ecosystem, yet comes at a premium price and offers less double‑precision performance.

Rather than choosing a single winner, modern AI infrastructure embraces heterogeneous fleets. Clarifai’s compute orchestration and multi‑cloud deployment tools allow you to run the right model on the right hardware at the right time. Energy‑aware scheduling, retrieval‑augmented generation, and cost‑per‑token optimization are built into the platform. As GPUs continue to evolve—with MI400 and Grace‑Blackwell on the horizon—flexibility and informed decision‑making will matter more than ever.

Frequently Asked Questions (FAQs)

Q1: Is MI355X available now, and when will it ship?
AMD announced MI355X for late‑2025 with limited availability through partner programs. Full production is expected in mid‑2026 due to HBM supply constraints and the need for liquid cooling infrastructure. Check with your cloud provider or Clarifai for current inventory.

Q2: Can I mix MI300X and B200 GPUs in the same cluster?
Yes. Clarifai’s Universal GPU API and orchestrator support heterogeneous clusters. You can route latency‑critical workloads to B200 while directing memory‑bound or cost‑sensitive tasks to MI300X/MI325X/MI355X. Data parallelism across different GPU types is possible with frameworks like vLLM that support mixed hardware.

Q3: How do FP6 and FP4 modes improve performance?
FP6 and FP4 are low‑precision formats that reduce memory footprint and increase arithmetic density. On MI355X, FP6/FP4 throughput reaches 20 PFLOPS, roughly 2× higher than B200’s FP6/FP4 capacity. These modes allow larger batch sizes and faster inference when precision loss is acceptable.

Q4: Do I need liquid cooling for MI355X?
Yes. The MI355X has a TDP around 1.4 kW and is designed for OAM/UBB form factors with direct‑to‑plate liquid cooling. Air‑cooled variants may exist (MI350X) but have reduced power limits and throughput.

Q5: What about the software learning curve for ROCm?
ROCm has improved significantly; over 92 % of CUDA APIs are now covered by HIP. However, developers may still face a learning curve when tuning kernels and debugging. Clarifai’s platform abstracts these complexities and provides pre‑tuned containers for common workloads.

Q6: How does Clarifai help optimize cost and sustainability?
Clarifai’s compute orchestration automatically schedules workloads based on latency, memory and cost constraints. Its energy‑aware scheduler tracks grams of CO₂ per prompt and chooses the most energy‑efficient hardware, while the Federated Query service allows retrieval across multiple data sources without vendor lock‑in. Together, these capabilities help you balance performance, cost and sustainability.