Best GPUs for Running GPT‑OSS Models in 2025

Building and scaling open‑source reasoning models like GPT‑OSS isn’t just about having access to powerful code—it’s about making strategic hardware choices, optimizing software stacks, and balancing cost against performance. In this comprehensive guide, we explore everything you need to know about choosing the best GPU for GPT‑OSS deployments in 2025, focusing on both 20 B‑ and 120 B‑parameter models. We’ll pull in real benchmark data, insights from industry leaders, and practical guidance to help developers, researchers, and IT decision‑makers stay ahead of the curve. Plus, we’ll show how Clarifai’s Reasoning Engine pushes standard GPUs far beyond their typical capabilities—transforming ordinary hardware into an efficient platform for advanced AI inference.

Quick Digest: A Roadmap to Your GPU Decision

Before we dive into the deep end, here’s a concise overview to set the stage for the rest of the article. Use this section to quickly match your use case with the right hardware and software strategy.

How Do GPT‑OSS Models Work and What Hardware Do They Need?

Quick Summary: What are GPT‑OSS models and what are their hardware requirements?

 GPT‑OSS includes two open‑source models—20 B and 120 B parameters—that use a mixture‑of‑experts (MoE) architecture. Only ~5.1 B parameters are active per token, which makes inference feasible on high‑end consumer or data‑centre GPUs. The 20 B model runs on 16 GB VRAM, while the 120 B version requires ≥80 GB VRAM and benefits from multi‑GPU setups. Both models use MXFP4 quantization to shrink their memory footprint and run efficiently on available hardware.

Introducing GPT‑OSS: Open‑Weight Reasoning for All

GPT‑OSS is part of a new wave of open‑weight reasoning models. The 120 B model uses 128 experts in its Mixture‑of‑Experts design. However, only a few experts activate per token, meaning much of the model remains dormant on each pass. This design is what enables a 120 B‑parameter model to fit on a single 80 GB GPU without sacrificing reasoning ability. The 20 B version uses a smaller expert pool and fits comfortably on high‑end consumer GPUs, making it an attractive choice for smaller organizations or hobbyists.

Memory and VRAM Considerations

The main constraint is VRAM. While the GPT‑OSS‑20B model runs on GPUs with 16 GB VRAM, the 120 B version requires ≥80 GB. If you want higher throughput or concurrency, consider multi‑GPU setups. For example, using 4–8 GPUs provides higher tokens‑per‑second rates compared to a single card. Clarifai’s services can manage such setups automatically via Compute Orchestration, making it easy to deploy your model across available GPUs.

Why Quantization Matters

GPT‑OSS leverages MXFP4 quantization, a 4‑bit precision technique, reducing the memory footprint while preserving performance. Quantization is central to running large models on consumer hardware. It not only shrinks memory requirements but also speeds up inference by packing more computation into fewer bits.

Expert Insights

• MoE Architectural Advantage: Because only a few experts activate per token, GPT‑OSS uses memory more efficiently than dense models.
  
  
• Active vs. Total Parameters: GPT‑OSS‑120B has 117 B total parameters but only 5.1 B active, so its resource needs are lower than the number might suggest.
  
  
• Community Momentum: Open‑weight models encourage collaboration, innovation, and rapid improvements as more developers contribute. They also spark competition, driving performance optimizations like those found in Clarifai’s Reasoning Engine.
  
  
• Model Flexibility: GPT‑OSS allows developers to adjust reasoning levels. Lower reasoning provides faster output, while higher reasoning engages more experts and longer chains of thought.
  
  

How Do B200, H200, H100, and MI300X Compare for GPT‑OSS?

Quick Summary

Question: What are the strengths and weaknesses of the main data-centre GPUs available for GPT‑OSS?
Answer: NVIDIA’s B200 is the performance leader with 192 GB memory, 8 TB/s bandwidth, and dual-chip architecture. It provides 15× faster inference over the H100 and uses FP4 precision to drastically lower energy per token. H200 bridges the gap with 141 GB memory and ~2× the inference throughput of H100, making it a great choice for memory-bound tasks. H100 remains a cost‑effective option for models ≤70 B, while AMD’s MI300X offers 192 GB memory and competitive scaling but has slightly higher latency.

B200 – The New Standard

The NVIDIA B200 introduces a dual‑chip design with 192 GB HBM3e memory and 8 TB/s bandwidth. In real-world benchmarks, a single B200 can replace two H100s for many workloads. When using FP4 precision, its energy consumption drops dramatically, and the improved tensor cores boost inference throughput up to 15× over the previous generation. The one drawback? Power consumption. At around 1 kW, the B200 requires robust cooling and higher energy budgets.

H200 – The Balanced Workhorse

With 141 GB HBM3e and 4.8 TB/s bandwidth, the H200 sits between B200 and H100. Its advantage is memory capacity: more VRAM allows for larger batch sizes and longer context lengths, which can be essential for memory-bound tasks like retrieval-augmented generation (RAG). However, it still draws around 700 W and doesn’t match the B200 in raw throughput.

H100 – The Proven Contender

Although it launched in 2022, the H100 remains a popular choice due to its 80 GB of HBM3 memory and cost-effectiveness. It’s well-suited for GPT‑OSS‑20B or other models up to about 70 B parameters, and it’s cheaper than newer alternatives. Many organizations already own H100s, making them a practical choice for incremental upgrades.

MI300X – AMD’s Challenger

AMD’s MI300X offers 192 GB memory and competitive compute performance. Benchmarks show it achieves ~74 % of H200 throughput but suffers from slightly higher latency. However, its energy efficiency is strong, and the cost per GPU can be lower. Software support is improving, making it a credible alternative for certain workloads.

Comparing Specifications

Expert Insights

• Energy vs Performance: B200 excels in performance but demands more power. FP4 precision helps mitigate energy use, making it more sustainable than it seems.
  
  
• Memory-Bound Tasks: H200’s larger VRAM can outperform B200 in RAG tasks if memory is the bottleneck.
  
  
• Software Maturity: NVIDIA’s ecosystem (TensorRT, vLLM) is more mature than AMD’s, leading to smoother deployments.
  
  
• Pricing and Availability: B200 units are scarce and expensive; H100s are abundant and inexpensive on secondary markets.
  
  

What Emerging Trends Should You Watch? FP4 Precision, Speculative Decoding & Future GPUs

Quick Summary

Question: What new technologies are changing GPU performance and efficiency for AI?
Answer: The most significant trends are FP4 precision, which offers 3× throughput and 25–50× energy efficiency compared to FP8, and speculative decoding, a generation technique that uses a small draft model to propose multiple tokens for the larger model to verify. Upcoming GPU architectures (B300, GB300) promise even more memory and possibly 3‑bit precision. Software frameworks like TensorRT‑LLM and vLLM already support these innovations.

Why FP4 Matters

FP4/NVFP4 is a game changer. By reducing numbers to 4 bits, you shrink the memory footprint dramatically and speed up calculation. On a B200, switching from FP8 to FP4 triples throughput and reduces the energy required per token from 10 J to about 0.4 J. This unlocks high‑performance inference without drastically increasing power consumption. FP4 also allows more tokens to be processed concurrently, reducing latency for interactive applications.

The Power of Speculative Decoding

Traditional transformers predict tokens sequentially, but speculative decoding changes that by letting a smaller model guess multiple future tokens at once. The main model then validates these guesses in a single pass. This parallelism reduces the number of steps needed to generate a response, boosting throughput. Clarifai’s Reasoning Engine and other cutting-edge inference libraries use speculative decoding to achieve speeds that outpace older models without requiring new hardware.

What’s Next? B300, GB300, MI350

Rumors and early technical signals point to B300 and GB300, which could increase memory beyond 192 GB and push FP4 to FP3. Meanwhile, AMD is readying MI350 and MI400 series GPUs with similar goals. Both companies aim to improve memory capacity, energy efficiency, and developer tools for MoE models. Keep an eye on these releases as they will set new performance baselines for AI inference.

Expert Insights

• Industry Adoption: Major cloud providers are already integrating FP4 into their services. Expect more vendor‑neutral support soon.
  
  
• Software Tooling: Libraries like TensorRT‑LLM, vLLM, and SGLang offer FP4 and MoE support, making it easier to integrate these technologies.
  
  
• Breaking Old Habits: MoE models and low‑precision arithmetic require a new mindset. Developers must optimize for concurrency and memory rather than focusing solely on FLOPS.
  
  
• Sustainability: Reduced precision means less power consumed per token, which benefits the environment and lowers cloud bills.
  
  

How Can You Run GPT‑OSS Locally and on a Budget?

Quick Summary

Question: Is it possible to run GPT‑OSS on consumer GPUs, and what are the trade‑offs?
Answer: Yes. The GPT‑OSS‑20B model runs on high‑end consumer GPUs (RTX 4090/5090) with ≥16 GB VRAM thanks to MXFP4 quantization. Running GPT‑OSS‑120B requires ≥80 GB VRAM—either a single data‑centre GPU (H100) or multiple GPUs (4–8) for higher throughput. The trade‑offs include slower throughput, higher latency, and limited concurrency compared to data‑centre GPUs.

Consumer GPUs: Practical Tips

If you’re a researcher or start‑up on a tight budget, consumer GPUs can get you started. The RTX 4090/5090, for example, provides enough VRAM to handle GPT‑OSS‑20B. When running these models:

• Install the Right Software: Use vLLM, LM Studio, or Ollama for a streamlined setup.
  
  
• Leverage Quantization: Use the 4‑bit version of GPT‑OSS to ensure it fits in VRAM.
  
  
• Start with Small Batches: Smaller batch sizes reduce memory usage and help avoid out‑of‑memory errors.
  
  
• Monitor Temperatures: Consumer GPUs can overheat under sustained load. Add proper cooling or power limits.
  
  

Multi‑GPU Setups

To improve throughput and concurrency, you can connect multiple GPUs. A 4‑GPU rig can offer significant improvements, though the benefits diminish after 4 GPUs due to communication overhead. Expert parallelism is a great approach for MoE models: assign experts to separate GPUs, so memory doesn’t duplicate. Tensor parallelism can also help but may require more complex setup.

Laptop and Edge Possibilities

Modern laptops with 24 GB VRAM (e.g., RTX 4090 laptops) can run the GPT‑OSS‑20B model for small workloads. Combined with Clarifai’s Local Runner, you can develop and test models locally before migrating to the cloud. For edge deployment, look at NVIDIA’s Jetson series or AMD’s small-form GPUs—they support quantized models and enable offline inference for privacy-sensitive use cases.

Expert Insights

• Baseten’s 4 vs 8 GPU Tests: Baseten found that while 8 GPUs improve throughput, the complexity and cost only make sense for very high concurrency.
  
  
• Semafore’s Workstation Advice: For small labs, a high-end workstation GPU (like Blackwell RTX 6000) balances cost and performance.
  
  
• Energy Considerations: Consumer GPUs draw 450–600 W each; plan your power supply accordingly.
  
  
• Scalability: Start small and use Clarifai’s orchestration to transition to cloud resources when needed.
  
  

How Do You Maximise Throughput with Multi‑GPU Scaling and Concurrency?

Quick Summary

Question: What are the best ways to scale GPT‑OSS across multiple GPUs and maximize concurrency?
Answer: Use tensor parallelism, expert parallelism, and pipeline parallelism to distribute workloads across GPUs. A single B200 can deliver around 7,236 tokens/sec at high concurrency, but scaling beyond 4 GPUs yields diminishing returns Combining optimized software (vLLM, TensorRT‑LLM) with Clarifai’s Compute Orchestration ensures efficient load balancing.

Scaling Strategies Explained

• Tensor Parallelism: Splits each layer’s computations across GPUs. It works well for dense models but can be tricky to balance memory loads.
  
  
• Expert Parallelism: Perfect for MoE models—each GPU holds a subset of experts. This method avoids duplicate weights and improves memory utilization.
  
  
• Pipeline Parallelism: Runs different parts of the model on different GPUs, enabling a pipeline where each GPU processes a different stage. This method thrives on large batch sizes but adds latency per batch.
  
  

Concurrency Testing Insights

Clarifai’s benchmarks show that at high concurrency, a single B200 rivals or surpasses dual H100 setups AIMultiple found that H200 has the highest throughput overall, with B200 achieving the lowest latency. However, adding more than 4 GPUs often yields diminishing returns as communication overhead becomes a bottleneck.

Best Practices

• Batch Smartly: Use dynamic batching to group requests based on context length and difficulty.
  
  
• Monitor Latency vs Throughput: Higher concurrency can slightly increase response times; find the sweet spot.
  
  
• Optimize Routing: With MoE models, route short requests to GPUs with spare capacity, and longer queries to GPUs with more memory.
  
  
• Use Clarifai’s Tools: Compute Orchestration automatically distributes tasks across GPUs and balances loads to maximize throughput without manual tuning.
  
  

Expert Insights

• Concurrency Methodology: Researchers recommend measuring tokens per second and time‑to‑first‑token; both matter for user experience.
  
  
• Software Maturity: Framework choice affects scaling efficiency. vLLM provides robust support for MoE models, while TensorRT‑LLM is optimized for NVIDIA GPUs.
  
  
• Scaling in Practice: Independent tests show performance gains taper off beyond four GPUs. Focus on optimizing software and memory usage instead of blindly adding more hardware.
  
  

What Are the Cost and Energy Considerations for GPT‑OSS Inference?

Quick Summary

Question: How do you balance performance against budget and sustainability when running GPT‑OSS?
Answer: Balance hardware acquisition cost, hourly rental rates, and energy consumption. B200 units offer top performance but draw ≈1 kW of power and carry a steep price tag. H100 provides the best cost‑performance ratio for many workloads, while Clarifai’s Reasoning Engine cuts inference costs by roughly 40 %. FP4 precision significantly reduces energy per token—down to ~0.4 J on B200 compared to 10 J on H100.

Understanding Cost Drivers

• Hardware Costs: B200s are expensive and scarce. H100s are more affordable and widely available.
  
  
• Rental vs Ownership: Renting GPUs in the cloud lets you scale dynamically, but long-term use might justify buying.
  
  
• Energy Consumption: Consider both the power draw and the efficiency. FP4 precision reduces energy required per token.
  
  
• Software Licensing: Factor in the cost of enterprise-grade software if you need support, though Clarifai’s Reasoning Engine is bundled into their service.
  
  

Cost Per Million Tokens

One way to compare GPU options is to look at cost per million tokens processed. Clarifai’s service, for example, costs roughly $0.16 per million tokens, making it one of the most affordable options. If you run your own hardware, calculate this metric by dividing your total GPU costs (hardware, energy, maintenance) by the number of tokens processed within your timeframe.

Sustainability Considerations

AI models can be resource-intensive. If you run models 24/7, energy consumption becomes a major factor. FP4 helps by cutting energy per token, but you should also look at:

• PUE (Power Usage Effectiveness): Data-centre efficiency.
  
  
• Renewable Energy Credits: Some providers offset energy use with green energy.
  
  
• Heat Reuse: Emerging trends capture GPU heat for use in building heating.
  
  

Expert Insights

• ROI of H100: Many organizations find the H100’s combination of price, power draw, and performance optimal for a wide range of workloads.
  
  
• Green AI Practices: Reducing energy per token not only saves money but also aligns with environmental goals—a rising concern in the AI community.
  
  
• Budget Tips: Start with H100 or consumer GPUs, then migrate to B200 or H200 when budgets allow or workloads demand it.
  
  
• Clarifai’s Advantage: By boosting throughput and lowering latency, Clarifai’s Reasoning Engine reduces both compute hours and energy consumed, leading to direct cost savings.
  
  

What Is Clarifai’s Reasoning Engine and What Do the Benchmarks Say?

Quick Summary

Question: Why is Clarifai’s Reasoning Engine important and how do its benchmarks compare?
Answer: Clarifai’s Reasoning Engine is a software layer that optimizes GPT‑OSS inference. Using custom CUDA kernels, speculative decoding, and adaptive routing, it has achieved 500+ tokens per second and 0.3 s time‑to‑first‑token, while cutting costs by 40 %. Independent evaluations from Artificial Analysis confirm these results, ranking Clarifai among the most cost‑efficient providers of GPT‑OSS inference

Deconstructing the Reasoning Engine

At its core, Clarifai’s Reasoning Engine is about maximizing GPU efficiency. By rewriting low‑level CUDA code, Clarifai ensures the GPU spends less time waiting and more time computing. The engine’s biggest innovations include:

• Speculative Decoding: This technique uses a smaller “draft” model to propose multiple tokens, which the main model verifies in a single forward pass. It reduces the number of sequential steps, lowers latency, and taps into GPU parallelism more effectively.
  
  
• Adaptive Routing: By monitoring incoming requests and current GPU loads, the engine balances tasks across GPUs to prevent bottlenecks.
  
  
• Custom Kernels: These allow deeper integration with the model architecture, squeezing out extra performance that generic libraries can’t.
  
  

Benchmark Results

Clarifai’s benchmarks show the Reasoning Engine delivering ≥500 tokens per second and 0.3 s time‑to‑first‑token. That means large queries and responses feel snappy, even in high‑traffic environments. Artificial Analysis, an independent benchmarking group, validated these results and rated Clarifai’s service as one of the most cost‑efficient options available, thanks in large part to this optimization layer

Why It Matters

Running large AI models is expensive. Without optimized software, you often need more GPUs or faster (and costlier) hardware to achieve the same output. Clarifai’s Reasoning Engine ensures that you get more performance out of each GPU, thereby reducing the total number of GPUs required. It also future‑proofs your deployment: when new GPU architectures (like B300 or MI350) arrive, the engine will automatically take advantage of them without requiring you to rewrite your application.

Expert Insights

• Software Over Hardware: Matthew Zeiler, Clarifai’s CEO, emphasizes that optimized software can double performance and halve costs—even on existing GPUs.
  
  
• Independent Verification: Artificial Analysis and PRNewswire both report Clarifai’s results without stake in the company, adding credibility to the benchmarks
  
  
• Adaptive Learning: The Reasoning Engine continues to improve by learning from real workloads, not just synthetic benchmarks.
  
  
• Transparency: Clarifai publishes its benchmark results and methodology, allowing developers to replicate performance in their own environments.
  
  

Clarifai Product Integration

For teams looking to deploy GPT‑OSS quickly and cost‑effectively, Clarifai’s Compute Orchestration provides a seamless on‑ramp. You can scale from a single GPU to dozens with minimal configuration, and the Reasoning Engine automatically optimizes concurrency and memory usage. It also integrates with Clarifai’s Model Hub, so you can try out different models (e.g., GPT‑OSS, Llama, DeepSeek) with a few clicks.

Real-World Use Cases & Case Studies

Quick Summary

Question: How are other organizations deploying GPT‑OSS models effectively?
Answer: Companies and research labs leverage different GPU setups based on their needs. Clarifai runs its public API on GPT‑OSS‑120B, Baseten uses multi‑GPU clusters to maximize throughput, and NVIDIA demonstrates extreme performance with DeepSeek‑R1 (671 B parameters) on eight B200s. Smaller labs deploy GPT‑OSS‑20B locally on high‑end consumer GPUs for privacy and cost reasons.

Clarifai API: High-Performance Public Inference

Clarifai offers the GPT‑OSS‑120B model via its reasoning engine to handle public requests. The service powers chatbots, summarization tools, and RAG applications. Because of the engine’s speed, users see responses almost instantly, and developers pay lower per-token costs.

Baseten’s Multi-GPU Approach

Baseten runs GPT‑OSS‑120B on eight GPUs using a combination of TensorRT‑LLM and speculative decoding. This setup scales out the work of evaluating experts across multiple cards, achieving high throughput and concurrency—suitable for enterprise customers with heavy workloads.

DeepSeek‑R1: Pushing Boundaries

NVIDIA showcased DeepSeek‑R1, a 671 B‑parameter model, running on a single DGX with eight B200s. Achieving 30,000 tokens/sec and more than 250 tokens/sec per user, this demonstration shows how GPU innovations like FP4 and advanced parallelism enable truly massive models.

Startup & Lab Stories

• Privacy-Focused Startups: Some startups run GPT‑OSS‑20B on premises using multiple RTX 4090s. They use Clarifai’s Local Runner for private data handling and migrate to the cloud when traffic spikes.
  
  
• Research Labs: Labs often use MI300X clusters to experiment with alternatives to NVIDIA. The slightly higher latency is acceptable for batch-oriented tasks, and the lower cost helps broaden access.
  
  
• Teaching Use: Universities use consumer GPUs to teach students about large-language-model training and inference. They leverage open-source tools like vLLM and LM Studio to manage simpler deployments.
  
  

Expert Insights

• Adapt & Optimize: Real-world examples show that combining optimized software with the right hardware yields better results than simply buying the biggest GPU.
  
  
• Future-Proofing: Many organizations choose hardware and software that can evolve. Clarifai’s platform allows them to swap models or GPUs without rewriting code.
  
  
• Diversity in Infrastructure: While NVIDIA dominates, AMD GPUs are gaining traction. More competition means better pricing and innovation.
  
  

What’s Next? Future Outlook & Recommendations

Quick Summary

Question: How should you plan your AI infrastructure for the future, and what new technologies might redefine the field?
Answer: Choose a GPU based on model size, latency requirements, and budget. B200 leads for performance, H200 offers memory efficiency, and H100 remains a cost-effective backbone. Watch for the next generation (B300/GB300, MI350/MI400) and new precision formats like FP3. Keep an eye on software advances like speculative decoding and quantization, which could reduce reliance on expensive hardware.

Key Takeaways

• Performance vs Cost: B200 offers unmatched speed but at high cost and power. H200 balances memory and throughput. H100 delivers strong ROI for many tasks. MI300X is a good option for certain ecosystems.
  
  
• Precision is Powerful: FP4/NVFP4 unlocks huge efficiency gains; expect to see FP3 or even 2-bit precision soon.
  
  
• Software Wins: Tools like Clarifai’s Reasoning Engine show that optimization can double performance and halve costs, sometimes more valuable than the latest hardware.
  
  
• Hybrid and Modular: Plan for hybrid environments that combine on-premises and cloud resources. Use Clarifai’s Local Runner for testing and Compute Orchestration for production to scale seamlessly.
  
  
• Environmental Responsibility: As AI scales, energy efficiency will be a critical factor. Choose GPUs and software that minimize your carbon footprint.
  
  

Decision Framework

To help you choose the right GPU, follow this step-by-step decision path:

1. Identify Model Size: ≤70 B → H100; 70–120 B → H200; ≥120 B → B200 or multi-GPU.
   
   
2. Define Latency Needs: Real-time (0.3 s TTFT) → B200; near-real-time (≤1 s TTFT) → H200; moderate latency → H100 or MI300X.
   
   
3. Set Budget & Power Limits: If cost and power are critical, look at H100 or consumer GPUs with quantization.
   
   
4. Consider Future Upgrades: Evaluate if your infrastructure can easily adopt B300/GB300 or MI350/MI400.
   
   
5. Use Smart Software: Adopt Clarifai’s Reasoning Engine and modern frameworks to maximize existing hardware performance.
   
   

Expert Insights

• Industry Forecasts: Analysts suggest that within two years, FP3 and even FP2 precision could become mainstream, further reducing memory and power consumption.
  
  
• AI Ecosystem Evolution: Open-source models like GPT‑OSS promote innovation and lower barriers to entry. As more organizations adopt them, expect the hardware and software stack to become even more optimized for MoE and low-precision operations.
  
  
• Continuous Learning: Stay engaged with developer communities and research journals to adapt quickly as new techniques emerge.
  
  

Frequently Asked Questions

1. Can GPT‑OSS‑120B run on a single consumer GPU?
   No. It requires at least 80 GB VRAM, while consumer GPUs max out around 24 GB. Use multi-GPU setups or data-centre cards instead.
2. Is the H100 obsolete with the arrival of B200?
   Not at all. The H100 still offers a strong balance of cost, performance, and availability. Many tasks, especially those involving ≤70 B models, run perfectly well on H100.
3. What’s the difference between FP4 and MXFP4?
   FP4 is NVIDIA’s general 4-bit floating-point format. MXFP4 is a variant optimized for mixture-of-experts (MoE) architectures like GPT‑OSS. Both reduce memory and speed up inference, but MXFP4 fine-tunes the dynamic range for MoE.
4. How does speculative decoding improve performance?
   It allows a draft model to generate several possible tokens and a target model to verify them in one pass. This reduces sequential operations and boosts throughput.
5. Should I choose AMD’s MI300X over NVIDIA GPUs?
   MI300X is a viable option, especially if you already use AMD for other workloads. However, software support and overall latency are still slightly behind NVIDIA’s ecosystem. Consider your existing stack and performance requirements before deciding.

Conclusion

Selecting the best GPU for GPT‑OSS is about balancing performance, cost, power consumption, and future‑proofing. As of 2025, NVIDIA’s B200 sits at the top for raw performance, H200 delivers a strong balance of memory and efficiency, and H100 remains a cost-effective staple. AMD’s MI300X provides competitive scaling and may become more attractive as its ecosystem matures.

With innovations like FP4/NVFP4 precision, speculative decoding, and Clarifai’s Reasoning Engine, AI practitioners have unprecedented tools to optimize performance without escalating costs. By carefully weighing your model size, latency needs, and budget—and by leveraging smart software solutions—you can deliver fast, cost-efficient reasoning applications while positioning yourself for the next wave of AI hardware advancements.