AI COMPUTE STRATEGY: SCALING & SOURCING GUIDE (Q1 2026)

by SELLNET Research. January, 2025

1. Executive Summary

As of Q1 2026, the compute landscape has bifurcated: Nvidia’s H100 has commoditized into a workhorse for mid-scale tasks, while the B200 (Blackwell) and AMD MI325X define the new frontier for large-scale training. This guide outlines how to allocate resources to maximize model performance while navigating these shifts.

2. Strategic Tiers: Scaling Your Compute

Tier A: Domain Expert (Entry)

Goal: Fine-tuning specialized models (1-3B parameters) on 100B-500B tokens.
Hardware: 8× Nvidia H100 SXM (Commoditized).
Strategy: H100 availability has stabilized, making it the most accessible entry point for rapid iteration.

Tier B: Mid-Scale (Growth)

Goal: Training 7-13B parameter models on 500B-2T tokens.
Hardware: 32-64× Nvidia H200 or AMD MI325X.
Strategy: Requires high memory bandwidth. H200 (141GB) or MI325X (256GB) allows larger batch sizes, significantly reducing training iterations vs H100.

Tier C: Frontier (Enterprise)

Goal: Large-scale foundation models (70B+) on 2T-15T tokens.
Hardware: 128-512× Nvidia B200 (Blackwell).
Strategy: B200 offers ~3-4x the training performance of H100. At this scale, networking (InfiniBand/Spectrum-X) and power density (1000W/GPU) are the primary bottlenecks.

3. Hardware Selection Matrix (Q1 2026 Market)

4. Technical Reference: Resource Formulas

Use these formulas to estimate precise requirements before booking capacity.

A. VRAM Estimation (Full Fine-Tuning / Pre-training)

$$Total\ VRAM \approx P \times 16\text{ bytes} + \text{Activations}$$

P = Parameters (billions)
16 Bytes Breakdown (Mixed Precision):

2 bytes: Model weights (BF16)
2 bytes: Gradients (BF16)
8 bytes: AdamW optimizer states (Momentum + Variance in FP32)
4 bytes: Master weights (FP32) / Gradient Accumulation buffer

Example (70B Model):

$$70B \times 16\text{ bytes} = 1,120GB + \sim200GB\text{ (Activations)} \approx \mathbf{1,320GB}$$

Result: Requires 16-24× H100s (80GB each) to fit the model in memory.

B. Compute Budget (GPU-Hour Estimation)

$$GPU\text{-}Hours \approx \frac{6 \times P \times T}{TFLOPS_{eff}}$$

6 = Factor for Forward pass (2) + Backward pass (4)
P = Parameters (Billions), T = Tokens (Trillions)
TFLOPS_eff = Effective FLOPs per GPU (Assume ~30-40% of peak spec).
Example (7B Model on 1T Tokens):

$$6 \times 7B \times 1T = 42 \text{ ZettaFLOPs}$$
Result: ~42,000 GPU-Hours (at ~280 TFLOPS effective/GPU).

5. Critical Warnings & Pitfalls

⚠ The L40S Trap: Avoid L40S for training. While cheaper, it uses GDDR6 memory with low bandwidth (864 GB/s) and connects via PCIe, not NVLink. This creates a massive bottleneck for multi-GPU training, making it suitable only for inference or single-GPU fine-tuning.
⚠ Hidden Infrastructure Costs: Raw GPU pricing is deceptive. Data egress fees, storage costs (High-IOPS NVMe for checkpointing), and idle time during debugging can significantly inflate the final bill. Ensure your SELLNET.ai negotiation includes "all-in" pricing transparency.
⚠ AMD Software Overhead: While the MI325X offers incredible hardware value, the ROCm software stack still lags behind CUDA in "out-of-the-box" compatibility for niche libraries. Budget for engineering overhead for initial environment setup and kernel optimization.

6. Quick Decision Framework

7. $5M Budget Allocation (Example)

8. Procurement Strategy

Recommendation: Do not use AWS, Azure, or GCP. Their "hyperscale premium" is not cost-effective for pure training workloads.
The Better Path: Source specialized GPUs as a Service (GPUaaS) providers through SELLNET.ai. These providers (often called "Alt-Clouds") offer bare-metal performance at significantly lower rates than the "Big 3," but the market is fragmented and hard to navigate alone.

The Process:
Gather Needs: Architects define exact requirements (Cloud, Hardware, Network).
No-Cost RFP: SELLNET.ai interviews niche GPU vendors and aggregates global inventory.
Top 5 Choices: You receive a matrix comparing these specialized providers with internal scores.
Negotiation: They drive aggressive discounts using volume leverage against the fragmented market. Often driving 5% to 20% below market pricing.
Contract: You sign directly with the "Best Fit" provider, ensuring no middleman markup.
Key Benefit: Access to arbitrage pricing (e.g., finding underutilized clusters) without the risk of vendor lock-in or the high markups of major cloud platforms.