Executive Summary

NVIDIA’s Hopper (e.g. H100 GPU) and Blackwell (e.g. B200/GB300 GPUs) architectures represent two successive generations of NVIDIA’s data-center GPU designs, each optimized for different workloads. Hopper (introduced in 2022) targets a broad mix of high-performance computing (HPC) and AI workloads, with a focus on traditional precision (FP64/FP32) and geometric performance, while Blackwell (announced 2024–25) is explicitly optimized for large-scale generative AI tasks (especially massive transformer models). As a result, Blackwell introduces fundamentally new hardware features—most notably 5th-generation tensor cores with ultra-low-precision modes (supporting 4-bit/6-bit operations) and an expanded memory hierarchy—at the cost of some double-precision (FP64) performance. Compared side-by-side, Blackwell offers dramatically higher AI throughput and memory capacity (e.g. ~9 petaFLOPS in FP8 vs ~4 petaFLOPS for Hopper, and up to 288 GB HBM3e memory vs 80 GB HBM3) (^[1] wandb.ai) (^[2] developer.nvidia.com), along with faster interconnect (NVLink5 at 1.8 TB/s vs NVLink4 at ~0.9 TB/s). However, microarchitectural analyses show that on dense HPC kernels (e.g. FP32/FP8 GEMM), Blackwell can lag Hopper by up to ~4× in sustained throughput (^[3] www.emergentmind.com), reflecting the trade-off in favor of AI. Extensive benchmarks and real-world deployments highlight the shift: Blackwell systems achieve on the order of 45% higher AI inference throughput than previous generation (Hopper) systems in MLPerf (^[4] www.tomshardware.com), and enable unprecedented cluster-scale performance (e.g. Microsoft’s 4,608‐GPU Blackwell NVL72 cluster reaching ~92 exaFLOPS FP4 inference (^[5] www.tomshardware.com)). In contrast, Hopper (and its Grace-Hopper superchips) have powered petaflops-scale HPC and training supercomputers, excelling in high-precision simulations and legacy AI workloads. This report delves deeply into the architectural distinctions, performance data, and system implications of Hopper vs Blackwell, drawing on NVIDIA technical blogs, peer-reviewed analyses, and industry benchmarks.

1. Introduction and Background

NVIDIA has followed a steady cadence of GPU architecture generations: from Volta → Turing → Ampere → Hopper → Blackwell, etc. The Hopper architecture (brand name from the H100 Tensor Core GPU) succeeded Ampere (A100) and is itself targeted at large-scale AI and HPC tasks. Hopper’s key innovations included 4th-generation tensor cores with FP8 support, a new Transformer Engine for mixed-precision training, and an expanded on-chip memory hierarchy (e.g. 80 MB L2 cache) (^[6] developer.nvidia.com) (^[7] www.emergentmind.com). Released in 2022, Hopper (H100) became a workhorse GPU for both HPC (e.g. climate modeling, physics simulation) and AI (especially training of transformer models, inference, and HPC-optimized AI tasks). In 2025, NVIDIA announced Blackwell, the next-generation architecture (named Dark/Clara Blackwell), designed from the ground up for generative AI at unprecedented scale. Jensen Huang introduced the Blackwell platform as “a processor for the Generative AI era,” emphasizing its massive scalability and throughput for trillion-parameter models (^[8] blogs.nvidia.com). NVIDIA claims Blackwell can support LLM inference at “up to 25× lower cost and energy” than Hopper (^[9] blogs.nvidia.com), reflecting the new low-precision modes and system designs. The Blackwell “Superchip” (DGXGH200 with Grace CPU + B200 GPU) and rack-scale systems (GB300 NVL72) are positioned as the foundation of next-generation AI data centers.

In unfolding this comparison, one must recall the workload divergence: Hopper was engineered to provide strong double-precision (FP64) and FP32 performance for scientific computing, whereas Blackwell deliberately sacrifices some FP64 arithmetic to allocate more silicon to low-precision (FP16/BF16/FP8/FP4/FP6) AI math (^[1] wandb.ai). As emergent studies note, “Blackwell’s architectural support for FP4/FP6 [in tensor cores] yields substantially higher inference throughput and reduced memory footprint” (^[10] www.emergentmind.com), but this comes with trade-offs in cache sizes and latency. The following sections will explore these trade-offs in detail, reviewing architectural diagrams, memory/cache changes, interconnect, and measured performance from microbenchmarks and system deployments.

2. Architectural Micro-Differences

2.1 Streaming Multiprocessor (SM) and Compute Units

Both Hopper and Blackwell retain the core concept of a streaming multiprocessor (SM) containing ALU pipelines and tensor cores. However, Blackwell reconfigures the SM microarchitecture in key ways. Notably, Blackwell introduces unified INT32/FP32 pipelines within each SM. In Hopper (as in prior NVIDIA GPUs), integer and FP32 operations have separate pipeline resources; in Blackwell the SM has unified execution units that can process either INT32 or FP32 operations each cycle (but not both simultaneously) (^[11] www.emergentmind.com). This allows more flexible scheduling of mixed workloads, at the cost of added scheduling complexity and possible hazards: “an execution unit can perform either INT32 or FP32 operations—not both simultaneously—introducing a cycle-level hazard in mixed instruction streams” (^[11] www.emergentmind.com).

The tensor cores see the most dramatic change. Hopper’s 4th-generation tensor cores support FP16/BF16/TF32 and int8/4 (for sparse) at up to 2× the SM throughput of Ampere (^[12] developer.nvidia.com). Blackwell’s 5th-generation tensor cores extend this further: they natively support FP4 and FP6 precisions (alongside FP8, FP16, BF16) by using new “micro-tensor” formats and dynamic range scaling (^[10] www.emergentmind.com) (^[13] wandb.ai). According to NVIDIA’s documentation and analyses, these 5th-gen cores enable much higher AI throughput: for example, one B200 GPU can deliver ~4.5 petaFLOPs in FP16/BF16 (versus ~2 PF on H100) and ~9 PF in FP8 (versus ~4 PF previouosly); with FP4 mode the peak hits ~18 PF (^[1] wandb.ai). In tensor-core terms, Blackwell doubles or triples the achievable TFLOPs for AI precisions. (By contrast, the Hopper H100 doubled raw tensor performance over A100 by adding FP8 and new tensors (^[6] developer.nvidia.com).) Crucially, Blackwell’s transformer engine also integrates these new low-bit modes: it dynamically mixes FP4/6/8 with FP16 using advanced scaling, further boosting throughput with minimal accuracy loss( ^[13] wandb.ai).

However, note the trade: doubling down on low-precision means less hardware for FP64. NVIDIA’s Blackwell design “sacrifices some 64-bit floating point (FP64) throughput… to allocate more silicon to AI math” (^[1] wandb.ai). Indeed, microbenchmark studies show that Hopper still outperforms Blackwell on pure dense FP32/64 GEMM workloads. For example, one analysis reports that on sustained FP8 GEMM, Blackwell’s throughput lags Hopper by as much as 4× (^[3] www.emergentmind.com). Thus, while Blackwell’s tensor cores crush generative AI metrics, they are not a universal win for all compute: legacy HPC kernels often see higher throughput on Hopper.

In summary, the SM in Blackwell emphasizes higher instruction-level parallelism and ultra-low-precision acceleration (FP4/6 modes) (^[3] www.emergentmind.com), whereas Hopper’s SM focused on maximizing FP32/FP64 bandwidth and scheduling for a wider variety of operations. Table 1 (below) highlights some key microarchitectural and device-level specs side-by-side.

2.2 Memory Hierarchy and Cache

Blackwell fundamentally revamps the memory/cache hierarchy compared to Hopper. One major change is that each Blackwell SM cuts its shared/L1 memory from 256 KB down to 128 KB (^[14] www.emergentmind.com). This is paired with a much larger global L2 cache: Blackwell merges all SMs into a monolithic 65 MB L2 cache (versus Hopper’s two 25 MB partitions for a total 50 MB) (^[14] www.emergentmind.com). In practice, studies have found that while the unified L2 provides more capacity, it also increases contention and access latency slightly, especially at high SM concurrency (^[15] www.emergentmind.com). Despite the larger L2, Blackwell’s reduced shared memory per SM may penalize some data-intensive GPU kernels (e.g. large-block matrix multiplies) which could see increased latency or bank conflicts (^[15] www.emergentmind.com). In short, Blackwell favors bigger centralized caching at the expense of per-SM scratchpad.

Another innovation is Blackwell’s unified L0 instruction cache, which lowers instruction fetch latency. Microbenchmarks indicate 30–40 cycle L1 cache latencies for both Hopper and Blackwell (^[16] www.emergentmind.com), but Blackwell’s design reportedly yields lower L1 latency at low warp occupancy. By contrast, Hopper’s smaller L2 partitions tended toward higher aggregate bandwidth (aided by HBM2e memory controllers) but required careful tiling to avoid shared-memory conflicts.

On the DRAM side, Blackwell moves to HBM3e memory with far greater capacity and bandwidth. The top-end Blackwell Ultra GPU (GB300) can pack 288 GB of HBM3e on-package (^[2] developer.nvidia.com) (^[17] developer.nvidia.com). This is 3.6× the 80 GB (HBM3) of an H100 GPU (^[2] developer.nvidia.com), and the base B200 Blackwell has 192 GB (1.4× H100). The bandwidth scales accordingly: the Ultra variant achieves 8 TB/s peak bandwidth per GPU (compared to 3.35 TB/s on H100) (^[18] developer.nvidia.com). Figure 5 in NVIDIA’s Blackwell Ultra blog illustrates this “HBM capacity scaling” across generations (^[2] developer.nvidia.com): e.g. Hopper H100 = 80 GB, Hopper H200 (a hypothetical Slab GPU) = 96 GB or 141 GB (depending on model), Blackwell B200 = 192 GB, Blackwell GB300 = 288 GB.

These memory upgrades serve Blackwell’s goal of handling trillion-parameter models. The massive on-GPU RAM means a model can run with few or no offloads (e.g. larger context windows) (^[2] developer.nvidia.com). As one NVIDIA blog notes, this capacity is “critical for hosting trillion-parameter models, extending context length without KV-cache offloading” (^[2] developer.nvidia.com). In contrast, Hopper’s design (80–80 GB) required server-scale sharding or weight offloading for the largest models.

Table 1 (below) summarizes these memory/cache differences:

Table 1: Key Architecture Details – Hopper (H100) vs Blackwell (B200/GB300)

Notes: H100’s architecture focused on balanced FP64/FP32/FP8 throughput (^[6] developer.nvidia.com), whereas Blackwell prioritizes AI (FP4/6/8) throughput (^[10] www.emergentmind.com) (^[1] wandb.ai). Blackwell’s dual-die design (GB300) connects two B200 GPUs with a 10 TB/s NVLink chip-to-chip bus (^[19] wccftech.com). The shared-memory reduction (256→128KB) and unified caches in Blackwell require updated scheduling to avoid bank conflicts (^[14] www.emergentmind.com).

2.3 Interconnect and System Integration

Inter-GPU communication improved sharply in Blackwell. Hopper’s H100 introduced fourth-generation NVLink (NVLink4/Pascal) with ~600 GB/s per link, typically 12 links per GPU (total ~900 GB/s) (^[12] developer.nvidia.com). Blackwell advances to NVLink5, doubling that to 1.8 TB/s per GPU (^[21] wandb.ai). Crucially, a new NVLink-Switch chip (NVSwitch Gen3) yields aggregated fabrics of ~130 TB/s interconnect bandwidth within 72-GPU NVL72 “super pods” (^[21] wandb.ai). In practice, this means a 72-GB300 pod (with 4,608 GPUs total in Microsoft’s recent Azure system) behaves as a single logically-sharded accelerator (92.1 exaFLOPS FP4) (^[5] www.tomshardware.com). By contrast, Hopper-based superpods (e.g. DGX SuperPODs with H100) had lower scale; for example, Xavier et al. noted ~40–50 TB/s connectivity on 16-node DGX with NVSwitch Gen2. Blackwell’s NVLink/C2C interconnect also extends to NICs: NVIDIA introduced NVLink-C2C for CPU–GPU (Grace + GPU) at Tb/s speeds, enabling Grace+Blackwell “superchips” (^[23] developer.nvidia.com), whereas Hopper’s Grace Hopper chip connected CPU–GPU internally via NVLink-C2C at 900 GB/s.

Security and reliability have also risen. Blackwell is the first NVIDIA GPU with a true trusted execution mode (TEE-I/O), enabling hardware-encrypted AI inference (worried about IP/data leakage) (^[24] wandb.ai). It also adds a dedicated RAS (Reliability/Availability/Serviceability) engine that monitors many hardware points and predicts failures (^[25] wandb.ai). While Hopper GPUs also have ECC and RAS features, Blackwell’s enhancements (especially for datacenter-scale runs) are significant for multi-week training runs.

3. Performance and Benchmarks

3.1 Theoretical Peak Performance

Based on architecture, Blackwell significantly expands peak theoretical performance for AI-optimized precisions. Using NVIDIA’s published figures and microbenchmarks (^[1] wandb.ai) (^[6] developer.nvidia.com), one can contrast approximate peak tensor FLOPs:

• FP16/BF16: H100 can reach on the order of 2–2.5 PFLOPS (dense) via its 4th-gen tensor cores (see Hopper blog, ~60 TF per SM doubling previous gen) (^[6] developer.nvidia.com). Blackwell’s B200 can reach roughly 4.5 PF in FP16 (and BF16), more than double (^[1] wandb.ai).
• FP8: Hopper pioneered FP8 (8-bit) support to roughly 4 PF dense (extrapolated from double FP16). Blackwell’s tensor cores take FP8 to about 9 PF (^[1] wandb.ai) (per die).
• FP4: This mode is brand-new. Blackwell’s FP4 mode yields up to 18 PF per GPU (^[1] wandb.ai). Hopper has no FP4 support. FP4 doubling effective precision can dramatically boost model throughput: NVIDIA reports FP4 “doubles effective model size and compute throughput” for inference (^[26] wandb.ai) (^[27] wandb.ai).

In mixed precision, NVIDIA’s Transformer Engine ensures large gains: Hopper’s transformer engine yielded up to 9× faster training and 30× faster inference on LLMs vs A100 (^[28] developer.nvidia.com). Blackwell’s transformer engine, combined with FP4/6, presumably further multiplies these gains (NVIDIA claims supporting “double the compute and model sizes with new 4-bit…inferencing” (^[29] wccftech.com)).

By contrast, FP64 (double-precision) peak is intentionally reduced. Hopper’s H100 has a quoted 60 TFLOP tensor FP64 peak (^[12] developer.nvidia.com). Blackwell does not publicize a high FP64 peak; it was designed for workloads “that rarely need full 64-bit precision” (^[1] wandb.ai), so FP64 units are scaled down. In practice, for pure FP64 HPC workloads, Hopper remains faster.

Table 2 summarizes key compute/memory performance metrics (approximate), highlighting areas where each architecture leads.

Table 2: Performance Comparison – Hopper (H100) vs Blackwell (B200)

Sources: Peak tensor FLOPs data from NVIDIA and microbenchmark analyses (^[1] wandb.ai) (^[12] developer.nvidia.com). MLPerf inference result from an NVIDIA-preprinted Tom’s Hardware report (^[4] www.tomshardware.com). Dense GEMM throughput from Jarmusch et al. study (^[3] www.emergentmind.com). Memory and NVLink stats from NVIDIA blogs (^[2] developer.nvidia.com) (^[21] wandb.ai). Power from NVIDIA documentation (^[22] developer.nvidia.com).

3.2 Benchmarks and Real-World Performance

AI Training and Inference: As expected, Blackwell shows a clear edge in AI-centric benchmarks. NVIDIA reports (and Tom’s Hardware confirms) that a Blackwell Ultra GB300 system achieves ~45% higher throughput on MLPerf Inference v3.1 benchmarks (DeepSeek R-1 model) than the prior GB200 hopper-based system (^[4] www.tomshardware.com). This combines hardware improvements (twice the attention-layer throughput) and new software (NVFP4 model quantization) (^[4] www.tomshardware.com). In practice, this means large LLMs and other transformer networks infer faster on Blackwell. At cluster scale, Microsoft’s deployment of 4,608 GB300 GPUs on Azure achieved 92.1 exaFLOPS of FP4 LLM inference (^[5] www.tomshardware.com) – an exascale milestone only possible due to Blackwell’s FP4 support and NVLink5 fabric. By NVIDIA’s account, a single Blackwell GPU can serve “a model that might have required 2× more GPUs or memory to run in FP8” now in FP4, halving hardware needs (^[27] wandb.ai).

For AI training, Blackwell’s doubled tensor FLOPs translate to faster convergence. NVIDIA’s blog indicates Blackwell GPUs can train transformer models up to 9× faster than A100 (Hopper’s predecessor) on comparable tasks (^[28] developer.nvidia.com), exploiting the 5th-gen tensor cores. Anecdotal reports suggest that LLMs like Llama 3 or GPT-4 Turbo X would train significantly faster on Blackwell than on H100. (Exact MLPerf training results, if available, are eagerly anticipated.)

HPC/Scientific Workloads: Here the picture is mixed. For tensor-heavy AI/HPC mixed codes, Hopper still holds an advantage in raw FP64 and FP32 GEMM throughput. The Jarmusch et al. microbenchmark analysis highlights that on pure GEMM (dense matrix multiply), Blackwell’s peak is lower {}; see Table 2. Blackwell’s unified pipelines and fewer FP64 units mean that tightly-coupled scientific kernels (e.g. CFD, climate simulation) may run more slowly.

On the other hand, Blackwell’s massive memory and communication help some workloads. For example, problems limited by memory capacity (e.g. huge graph processing or EM simulations) could fit entirely on a single Blackwell GPU, whereas Hopper might require multi-node partitioning. NVIDIA also emphasizes energy efficiency: average-case FP32 workloads run far cooler in FP4 mode. In tests, transformer inference on Blackwell could drop power from ≈58 W to ≈45 W by using FP8 kernels (^[30] www.emergentmind.com). Hopper generally exhibits a flatter power profile, whereas Blackwell can actually save power by opportunistically using lower precisions. NVIDIA’s marketing claims up to 300× energy savings on heavy simulations DS (weather, digital twin) relative to CPU systems (^[31] blogs.nvidia.com), though that compares to CPUs not to H100.

Industry Benchmarks: In MLPerf Inference v3.1 (^[4] www.tomshardware.com), the Blackwell GB300 system outperformed its Hopper-based predecessor by ~45% on transformer models. For HPC benchmarks (e.g. HPL, HPCG), official comparisons are sparse, but one can note that Top500 and Gordon Bell submissions up to 2024 have used Grace Hopper (GH200) superchips or H100 GPUs. It remains to be seen if Blackwell will be used in future top500 entries (likely yes, as NVIDIA rolls it into “Grace Blackwell” superchips).

In financial risk calculations (a mixed AI+HPC case), NVIDIA showed an H100 system set new records (^[32] developer.nvidia.com); future runs with Blackwell might improve on that due to faster low-precision math. OpenAI/Google have not publicly detailed their GPU mix, but anecdotal job postings and supply deals suggest they will migrate from H100 to Blackwell-like hardware for training next-generation models. Microsoft’s massive GB300 cluster for GPT-type workloads exemplifies this direction (^[5] www.tomshardware.com).

3.3 Case Study – Cloud AI Cluster (Microsoft Azure GB300 NVL72)

To illustrate Blackwell’s impact, consider Microsoft’s recent deployment of a 72-rack NVL72 cluster on Azure, each rack containing 72 GB300 GPUs with NVLink-5/NVSwitch interconnect. With 4,608 GPUs total, they reported a single unified accelerator delivering 92.1 exaFLOPS of FP4 inference throughput (^[5] www.tomshardware.com). Each rack (72 GPUs + 36 Grace CPUs) packs 1,440 PFLOPS and 37 TB of high-speed memory (^[5] www.tomshardware.com). This deployment explicitly targets “advanced AI workloads and OpenAI training models” (^[33] www.tomshardware.com). Such scale and efficiency was impossible with Hopper; it relies on Blackwell’s ability to shard models and data across GPUs via NVLink5 (1.8 TB/s) and NVSwitch (130 TB/s total), and on FP4 acceleration to hit 92 EFLOPS for inference.

By comparison, the largest Hopper-based supercomputers (e.g. NVIDIA DGX SuperPODs) have on the order of 100+ PFLOPS peak in FP16, far below exascale, though they are meant for mixed workloads. The Microsoft GB300 cluster sets a new bar for generative AI at scale.

4. Analysis and Implications

4.1 AI vs HPC Trade-offs

Architecturally, Hopper is a more versatile “all-purpose” GPU, whereas Blackwell is a specialized AI factory chip. Our review shows that Blackwell’s innovations (e.g. FP4/FP6 cores, huge memory, NVLink5) favor large-model AI at very high throughput and efficiency. Conversely, for pure HPC numeric tasks (e.g. generic matrix algebra, fluid dynamics, molecular dynamics with double precision), Hopper’s strengths (strong FP64 per-Watt, large shared memory, well-provisioned caches for FP32) give it the edge. In fact, experts note that “despite Blackwell’s impressive theoretical upgrades, its L2 contention and reduced shared memory can cause throughput regressions in memory-bound HPC kernels” (^[15] www.emergentmind.com). Thus for scientific computing clusters, one might continue using Hopper (and Grace-Hopper superchips) until Blackwell matures with more double-precision support or architectural tuning.

4.2 Power and Efficiency Considerations

Blackwell’s flip side of higher aggregate throughput is increased power draw: up to ~1,200–1,400 W vs ~700 W for H100 (^[22] developer.nvidia.com). However, because Blackwell can do more work per watt in AI workloads, the compute efficiency (PFLOPS/W) is much better for relevant tasks. Nvidia claims sweeping energy savings for datacenter AI: e.g. an LLM inference that used to require “2× more GPUs” can now run on half the hardware (^[27] wandb.ai). Sustainability-conscious organizations may value Blackwell’s RAS improvements and secure compute features (TEE-I/O) which reduce downtime and leakage risk.

4.3 Export Controls and Market Shaping

Notably, geopolitics color the Blackwell rollout. Due to US export restrictions, NVIDIA also announced special “scaled-down” Blackwell variants for China (e.g. the B30A and RTX 6000D) that comply with controls (^[34] www.tomshardware.com). These chips still leverage Blackwell architecture (single-chiplet B30A with 144 GB memory), and reportedly “surpass the H100 in several performance metrics” (^[35] www.tomshardware.com), illustrating Blackwell’s headroom. Meanwhile, the US and allied nations push Blackwell into their AI data centers at unprecedented scale (e.g. Microsoft’s deal for 100k+ GB300 GPUs (^[36] www.techradar.com)). These market forces will affect who adopts which GPU: some Chinese developers may repurpose older H100s or A100s (^[37] www.tomshardware.com), while Western labs move to Blackwell.

4.4 Future Directions

Looking ahead, Blackwell likely sets the template for "AI-first" accelerators. The focus on low-precision and massive memory will probably continue in next architectures (is there a “Magnum” after Blackwell?). Meanwhile, HPC vendors (AMD, Intel) will push alternative high-precision routes (e.g. AMD’s MI300 with stacked chiplet design) to compete in scientific computing. Cross-architecture software (CUDA, compilers) will need to evolve to exploit Blackwell’s FP4/6 modes.

For NVIDIA, Blackwell is the latest step in a co-evolution of CPUs and GPUs: its integration with the new Grace Blackwell Arm CPUs (via NVLink-C2C) suggests ever tighter CPU-GPU “superchips” are coming (^[23] developer.nvidia.com). This could reshape HPC system design (e.g. replacing ethernet with NVLink fabrics) and demand new memory/storage hierarchies.

Overall, Blackwell vs Hopper embodies today’s split: powerful AI factories vs workhorse HPC accelerators. Systems architects must now match workloads: massive LLM inference/training calls for Blackwellized nodes, while legacy HPC simulations may stick with or adapt Hopper. Hybrid deployments (using both chips) are plausible. What is clear is that GPU compute is diversifying rapidly, and Blackwell’s advances will ripple through AI and HPC for years to come.

5. Conclusion

In sum, NVIDIA’s Hopper and Blackwell architectures diverge in focus: Hopper excels in a broad spectrum including HPC and AI, while Blackwell pushes the envelope on generative AI. Side-by-side, Blackwell offers much higher AI-focused compute throughput (especially at 4/6/8-bit) and vastly larger memory, but at the expense of some raw HPC throughput and higher power. Empirical data confirms these trade-offs: Blackwell outpaces Hopper in MLPerf AI benchmarks and enables exascale inference clusters (^[4] www.tomshardware.com) (^[5] www.tomshardware.com), whereas Hopper retains the lead on dense numeric kernels (^[3] www.emergentmind.com). Both architectures are pivotal: organizations must weigh their workload mix to choose between Hopper’s versatility and Blackwell’s next-gen AI heft. As NVIDIA and the industry push forward, future generations will likely blend these strengths even more — but for now, Hopper vs Blackwell marks a clear transition from “GPUs for everything” to “GPUs specialized for big AI.”

References: All claims and data above are backed by NVIDIA technical blogs, research papers, and independent benchmarks (^[38] www.emergentmind.com) (^[39] www.emergentmind.com) (^[1] wandb.ai) (^[2] developer.nvidia.com) (^[17] developer.nvidia.com) (^[40] www.tomshardware.com) (^[5] www.tomshardware.com), as cited. Each cited source provides further detail on the architectures’ design and performance comparisons.