$AMD MI450 vs $NVDA Rubin Comprehensive 🧵 The @AMD Instinct MI450 (part of the MI400 series) and @nvidia 's Rubin architecture (expected flagship like the R200 or VR200) represent the next wave of AI accelerators, both slated for production and deployments in 2026. These chips target hyperscale AI training and inference, with AMD emphasizing memory capacity and rack-scale integration to challenge NVIDIA's ecosystem dominance. Both use HBM4 memory and advanced packaging, but MI450 leverages a superior process node for density. Performance metrics are peak theoretical (FP4 for AI inference/training); real-world varies by workload. Architecture AMD: CDNA 5 (UDNA-based) NVDA: Rubin (successor to Blackwell) Process Node AMD: TSMC 2nm NVDA: TSMC 3nm Memory AMD: 432 GB HBM4 NVDA: 288 GB HBM4 Memory Bandwidth AMD: 9.6 TB/s NVDA: 20 TB/s (enhanced post-MI450 reveal) Max Compute FP4 vs FP8 AMD: ~40 PFLOPS(FP4) & ~20 PFLOPS(FP8) NVDA: ~50 PFLOPS(FP4) & ~25 PFLOPS(FP8) Power Consumption AMD: 1000-1400W NVDA: 1,800-2,300W Rack-Scale Solution AMD: Helios (72 GPUs; 31 TB HBM, 1.4 PB/s total) NVDA: NVL144 (144 GPUs; liquid-cooled, Vera CPU Release Date: Both in H2 2026 Estimated Price: AMD MI450: $30k-$40k(large scale discount) NVDA Rubin: $45-$60k(Minimal discount seen so far) Total Cost of Ownership (TCO) for AI accelerators like the MI450 and Rubin encompasses not just the upfront hardware price but also ongoing expenses such as energy bills, cooling infrastructure, maintenance, and scalability over 3-5 years in data centers. AMD's MI450 delivers a significantly lower TCO estimated at 20-40% less than Rubin's in inference-heavy workloads primarily due to a combination of pricing aggression, superior energy efficiency, and optimized rack-scale designs that minimize infrastructure upgrades. ~Lower Acquisition Costs: AMD GPUs are typically priced 25-35% below NVIDIA equivalents, with MI450 units projected at $30K-40K versus Rubin's $45K-60K. This stems from AMD's fabless model leveraging TSMC without NVIDIA's custom ecosystem premiums. Partnerships like the 6GW OpenAI deal and Oracle's 50K-unit order further drive volume discounts, reducing per-unit costs for hyperscalers. ~Energy and Cooling Savings: Power is the biggest TCO driver, accounting for 40-60% of lifetime costs in AI clusters. MI450's estimated 1,200W TGP (versus Rubin's 2,300W) translates to ~48% lower draw per GPU, cutting annual electricity bills by up to $500K per 1,000-unit rack at $0.10/kWh. Cooling follows suit: AMD's chiplet-based Helios racks (72 GPUs) require less dense liquid-cooling setups than NVIDIA's NVL144 (144 GPUs), avoiding costly retrofits for existing data centers. TSMC benchmarks show 2nm nodes yielding 20-30% better perf/Watt, amplifying these savings in memory-bound tasks like LLM inference. ~Higher Density and Scalability: MI450's Infinity Fabric enables denser racks (up to 128 GPUs in IF128 configs) with 1.4 PB/s aggregate bandwidth, delivering 6.4 EFLOPS FP4 nearly double Rubin's 3.6 EFLOPS in equivalent space. This means fewer racks for the same throughput, slashing deployment costs by 15-25%. AMD's UALink compatibility also future-proofs against vendor lock-in, reducing long-term refresh expenses. How MI450 Consumes Nearly Half the Power? The MI450's ~1,200W Thermal Design Power (TGP) is indeed about half of Rubin's 2,300W, a deliberate design choice rooted in AMD's advanced process node, chiplet architecture, and workload optimization. This isn't just smaller transistors it's a holistic efficiency play that avoids NVIDIA's power escalations to chase raw FLOPS. ~Superior Process Node: MI450's core compute dies use TSMC's 2nm (N2P) node, versus Rubin's full-chip 3nm (N3P). The 2nm shrink delivers ~1.15x transistor density and 20-30% better power efficiency per TSMC data, allowing AMD to hit 40 PFLOPS FP4 at lower voltages/clocks. NVIDIA's redesigns bumping TGP by 500W to counter MI450 pushed Rubin to 2,300W for marginal gains in bandwidth (20 TB/s vs. MI450's 19.6 TB/s) ~Chiplet Modularity: AMD's multi-die approach (separate accelerator core, interposer, and media dies) isolates power-hungry elements, enabling fine-grained scaling. Only the core needs 2nm; others use cost-effective 3nm, reducing overall leakage and dynamic power by 15-25% compared to NVIDIA's monolithic (or early chiplet) Rubin. This modularity also cuts thermal hotspots, allowing sustained boosts without thermal throttling. ~Architecture and Workload Focus: CDNA 5 prioritizes dense matrix ops for AI ( FP4/FP8) with fewer overhead cycles than Rubin's tensor-heavy design, which inflates power for peak training bursts. In rack-scale, Helios's Infinity Fabric (IF64/128) offloads interconnect power to fabric links, versus NVLink 6's GPU-centric draw. AMD's ROCm optimizations yield 4x efficiency gains over MI300X in inference, where power scales linearly with model size MI450 handles 432 GB HBM4 without NVIDIA's sharding penalties that spike energy use. Conclusion: As the 2026 AI landscape crystallizes, AMD's Instinct MI450 emerges not as a mere challenger but as a transformative force, poised to erode NVIDIA's 95% market stranglehold through unmatched TCO efficiency and power frugality. By harnessing TSMC's 2nm edge and chiplet ingenuity, MI450 delivers Rubin-caliber performance 40 PFLOPS FP4, 432 GB HBM4 at half the power (1,200W vs. 2,300W) and 20-40% lower lifetime costs, making it the go-to for inference-dominated hyperscalers like $META (42% allocation), @OpenAI (6GW commitment) and Oracle (50K units). NVIDIA's Rubin retains software supremacy and training prowess via CUDA's moat, but AMD's Helios racks boasting 1.4 PB/s bandwidth and 6.4 EFLOPS density signal a "Milan moment" per AMD management, potentially flipping 20-30% share by 2026 amid HBM4 crunches

NINE F***ING MONTHS! NOT. ONE. SINGLE. CORRUPT. DEMOCRAT. ARRESTED.OVER.RUSSIAGATE. Zero charges for the Russiagate HOAX architects. Zero cuffs for the lawfare ASSASSINS who weaponized courts against patriots. The DOJ is a DEMOCRAT PROTECTION RACKET—a giant middle finger to justice. I’m not “losing hope.” I’m DONE BELIEVING THE SWAMP WILL EVER DRAIN ITSELF. If they won’t prosecute the traitors, WE THE PEOPLE WILL REMEMBER THEIR NAMES.

Someone doxxed Stephen Miller’s home address, then showed up to his house 24 hours after Charlie Kirk was assassinated to try to intimidate his wife.. These threats forced them to move out to a military base and now activist judges and prosecutors are protecting the criminal…

Trump is throwing Gatsby parties while our military are standing in line at food banks to feed their families. This is not the MAGA I voted for.

I want everyone who orchestrated J6 I want everyone who played a role in J6 I want everyone in the media who helped in the cover-up And I also want everyone on the J6 Committee who assisted in burying the evidence that would've exonerated the J6ers put in Prison for Treason