Here is the Technical Intelligence Report for 2026-01-14.

Executive Summary

  • Strategic Partnership: AMD and TCS announced a major collaboration to scale AI solutions in enterprise environments (BFSI, Manufacturing, Life Sciences) using the full AMD stack (EPYC, Instinct, Ryzen, Xilinx).
  • Performance Optimization: New technical benchmarks from AMD demonstrate that GPU partitioning (CPX mode) on MI300X can nearly double throughput for molecular dynamics (GROMACS) and drug discovery (REINVENT4) workloads.
  • Leaked/Retracted Research: A blog post regarding “Athena-PRM,” a new Multimodal Process Reward Model from AMD, was committed and subsequently deleted from the ROCm repository. The data indicates significant reasoning improvements (up to +10.2 points on WeMath) using Qwen2.5-VL on AMD hardware.
  • Competitor Update: NVIDIA released DLSS 4.5. While it introduces a 2nd-gen transformer model for better image quality, it requires 5x the compute power, reportedly causing performance degradation on older RTX 20/30 series cards.
  • Software Status: The AMD GPU DRA (Dynamic Resource Allocation) Driver for Kubernetes has been officially classified as Beta.

🤖 ROCm Updates & Software

[2026-01-14] [New Post] Applying GPU Compute Partitioning for GPU workloads

Source: ROCm Tech Blog

Key takeaway relevant to AMD:

  • Demonstrates massive throughput gains on MI300X by utilizing hardware partitioning (CPX mode), specifically for scientific and life-science workloads (GROMACS, REINVENT4).
  • Validates MI300X’s 8-XCD chiplet architecture as a competitive advantage for handling multiple smaller concurrent jobs versus a single monolithic workload.

Summary:

  • A new technical guide details how to use AMD GPU compute partitioning to increase utilization.
  • Compares SPX (Single Partition X-celerator) vs. CPX (Core Partitioned X-celerator) modes.
  • Provides specific amd-smi configuration commands and Docker implementation examples.

Details:

  • Partitioning Mechanics: The MI300X has 8 XCDs. In CPX mode, each XCD presents as an independent logical GPU (8 partitions per card, up to 64 partitions per 8-way node).
  • Command: Enabled via amd-smi set --gpu all --compute-partition CPX.
  • Benchmark - GROMACS (Molecular Dynamics):
    • Tested “multidir” runs (independent replicas).
    • Result: CPX mode delivered 1.75x to 2.47x speedups over SPX.
    • Throughput: 8x MI300X in CPX mode achieved 8026 ns/day vs. 4507 ns/day in SPX mode.
  • Benchmark - REINVENT4 (Drug Discovery/AI):
    • Tested concurrent Hyperparameter Optimization (HPO) jobs.
    • Result: 8 concurrent jobs on a partitioned GPU achieved a 1.42x speedup (191 minutes total time) compared to sequential execution (272 minutes).
    • Crossover Point: Concurrent execution becomes faster than sequential execution when running more than 5 jobs.

[2026-01-14] Update: Clarify AMD GPU DRA Driver beta status

Source: ROCm Tech Blog

Key takeaway relevant to AMD:

  • Sets expectations for enterprise Kubernetes operators: The DRA driver is not yet General Availability (GA).
  • Highlights the shift from legacy Device Plugins to Dynamic Resource Allocation (DRA) for more granular control over AMD hardware in clusters.

Summary:

  • Documentation update to the “Reimagining GPU Allocation in Kubernetes” blog.
  • Explicitly marks the AMD GPU DRA Driver as beta.

Details:

  • Status: “Features, APIs, and behaviors may change in future releases.”
  • Functionality:
    • The driver allows GPUs to be treated as attribute-aware resources.
    • Enables requesting specific models (e.g., “two MI300X GPUs on same PCIe root”) or partition profiles (slices) via ResourceClaims.
    • Replaces the traditional “count-based” scheduling of Kubernetes Device Plugins.

🔬 Research & Papers

[2026-01-14] Athena-PRM: Enhancing Multimodal Reasoning (Retracted/Deleted Post)

Source: ROCm Tech Blog

Key takeaway relevant to AMD:

  • INTEL ALERT: This article was deleted from the repo immediately after commit, suggesting a leak of upcoming research or an embargo break.
  • AMD is developing “Athena-PRM,” a Process Reward Model designed to improve reasoning in Large Multimodal Models (LMMs) on Instinct GPUs.
  • This positions AMD as competing in the “reasoning/Chain-of-Thought” space (similar to OpenAI o1/DeepSeek R1 techniques) by improving test-time scaling.

Summary:

  • The paper introduces Athena-PRM, trained on 5k high-quality process-labeled data.
  • Uses a “consistency between weak and strong completers” method to filter data, reducing annotation costs.
  • The model evaluates the correctness of intermediate reasoning steps.

Details:

  • Methodology:
    • Completer Consistency: Estimates step labels by comparing outputs from a weak model vs. a strong model; only retains steps where both agree to remove bias.
    • Strategies: Initializes the PRM (Process Reward Model) from an ORM (Outcome Reward Model) and uses negative data up-sampling.
  • Benchmarks (Best-of-N, N=8):
    • Policy Model: Qwen2.5-VL-7B.
    • WeMath: 46.4 (Athena-PRM) vs 36.2 (Base Model). (+10.2 points).
    • MathVista: 75.2 (Athena-PRM) vs 68.1 (Base Model).
    • VisualProcessBench: Claims State-of-the-art (SoTA) results, outperforming previous SoTA by 3.9 F1-score.
  • Hardware: Optimized for and trained on AMD Instinct GPUs.
  • Linked Paper (Unverified): ArXiv 2506.09532 (Note: 2506 implies June 2025, likely a typo in the source or future-dated placeholder).

🤼‍♂️ Market & Competitors

[2026-01-14] TCS and AMD Announce Strategic Collaboration

Source: AMD Press Releases

Key takeaway relevant to AMD:

  • Major B2B channel expansion: TCS (Tata Consultancy Services) is a massive global systems integrator. This partnership creates a direct funnel for AMD hardware into large enterprise legacy modernization projects.
  • Broad ecosystem play: Covers the entire product stack (Ryzen AI PCs, EPYC servers, Instinct Accelerators, and Xilinx embedded).

Summary:

  • TCS and AMD will co-develop industry-specific AI/GenAI solutions.
  • TCS to upskill/certify associates on AMD software/hardware.
  • Focus on moving clients from “AI pilots to production.”

Details:

  • Target Sectors & Use Cases:
    • Life Sciences: Drug discovery.
    • Manufacturing: Cognitive quality engineering, smart manufacturing.
    • BFSI (Banking/Finance): Intelligent risk management.
  • Hardware Integration:
    • Client: Ryzen CPU-powered workplace transformation.
    • Datacenter: EPYC CPUs and Instinct GPUs for hybrid cloud/HPC.
    • Edge: Adaptive SoCs and FPGAs (Xilinx portfolio) for industrial digitalization.

[2026-01-14] Nvidia DLSS 4.5 Super Resolution leaves beta

Source: Tom’s Hardware

Key takeaway relevant to AMD:

  • NVIDIA is pushing image quality over raw performance efficiency in this update, increasing the compute cost significantly (5x). This may give AMD FSR a “lightweight/performance” argument for lower-end hardware.
  • Features utilizing FP8 acceleration (RTX 40/50 series) leave older NVIDIA generations (RTX 20/30) behind, potentially opening a window for AMD upgrades in the mid-range market.

Summary:

  • DLSS 4.5 Super Resolution is now available to all Nvidia app users (auto-update).
  • Supports over 400 titles.
  • Delayed Feature: 6X Multi Frame Generation is delayed (originally teased for Jan 13).

Details:

  • Architecture: Uses a 2nd generation transformer model.
  • Compute Cost: Utilizes 5x the compute power compared to the 1st gen transformer model.
  • Hardware Dependency:
    • RTX 40/50 series use Tensor Cores with accelerated FP8 processing to handle the load.
    • Older GPUs (RTX 20/30) are reported to suffer performance losses of up to 20%+.
  • Visual Improvements: Reduces “shimmering” on static surfaces and ghostly trails/after-images.
  • Issues: User reports indicate potential degradation of text legibility and “focusing” artifacts.