NVIDIA H100 vs H200 vs B200 vs B300 — GPU Comparison for AI Infrastructure

NVIDIA's data center GPU lineup has advanced through two generations in rapid succession: the Hopper generation (H100, H200) and the Blackwell generation (B200, B300). Each GPU occupies a distinct position in the AI infrastructure stack — H100 remains the most widely deployed AI training GPU globally, H200 extends Hopper with dramatically more memory for inference and memory-bound training, B200 is the current-generation Blackwell platform delivering roughly 2–3× the compute of H100, and B300 (Blackwell Ultra) pushes further with increased memory and throughput. Haink supplies server platforms for all four GPU generations in Hong Kong, Dubai, and Mainland China.

Quick Summary

H100 SXM5 — 80 GB HBM2e, 3,958 TFLOPS FP8, 3.35 TB/s memory bandwidth, 700W TDP; the proven workhorse of enterprise AI training with the broadest software ecosystem
H200 SXM5 — 141 GB HBM3e, 3,958 TFLOPS FP8 (same compute as H100), 4.8 TB/s memory bandwidth, 700W TDP; same Hopper chip as H100 with 76% more memory and 43% higher bandwidth — the upgrade for memory-bound workloads
B200 SXM5 — 192 GB HBM3e, 9,000 TFLOPS FP8 / 18,000 TFLOPS FP4, 8 TB/s memory bandwidth, ~1,000W TDP; Blackwell architecture with 2.3× the FP8 compute of H100, new FP4 precision, and NVLink 5.0
B300 SXM (Blackwell Ultra) — 288 GB HBM3e, higher compute than B200, 8+ TB/s memory bandwidth; DLC-mandatory next-generation platform for frontier model training and the highest-throughput inference deployments

Architecture: Hopper vs Blackwell

H100 and H200 are both based on the NVIDIA GH100 Hopper die — they use the same GPU chip, same Streaming Multiprocessor (SM) count, and the same compute throughput. The difference between H100 and H200 is entirely in the memory subsystem: H200 replaces H100's 80 GB HBM2e with 141 GB HBM3e, gaining 76% more capacity and 43% more bandwidth while the chip itself is unchanged. This means upgrading from H100 to H200 does not improve compute-bound workloads at all — only workloads that are limited by GPU memory capacity or bandwidth benefit.

B200 is based on NVIDIA's Blackwell architecture (GB100 die), a full ground-up redesign with 208 billion transistors — 2.5× the transistor count of GH100. Blackwell introduces several architectural advances that H100/H200 do not have:

Second-generation Transformer Engine with FP4 precision support — enabling 2× more inference throughput versus FP8 for compatible models with minimal quality loss
NVLink 5.0 — 1,800 GB/s bidirectional GPU-to-GPU bandwidth (2× NVLink 4.0 on H100/H200)
Fifth-generation NVTensor Cores delivering 9,000 TFLOPS FP8 versus H100's 3,958 TFLOPS — 2.3× improvement
RAS (Reliability, Availability, Serviceability) Engine — dedicated on-die hardware for error detection and self-healing, designed for very large cluster deployments where GPU failure detection latency impacts training job reliability
Confidential Computing support — hardware-level memory encryption for AI workloads with strict data privacy requirements

Detailed Specifications

NVIDIA H100 SXM5

Architecture: Hopper (GH100)
GPU Memory: 80 GB HBM2e
Memory Bandwidth: 3.35 TB/s
FP8 Tensor Core: 3,958 TFLOPS (with sparsity)
FP16 / BF16: 1,979 TFLOPS (3,958 with sparsity)
FP32: 67 TFLOPS
NVLink: NVLink 4.0, 900 GB/s bidirectional
Interconnect: SXM5 socket (NVLink) or PCIe Gen5
TDP: 700W (SXM5) / 350W (PCIe)
Memory ECC: Yes (HBM2e)

NVIDIA H200 SXM5

Architecture: Hopper (GH100) — same die as H100
GPU Memory: 141 GB HBM3e
Memory Bandwidth: 4.8 TB/s
FP8 Tensor Core: 3,958 TFLOPS (identical to H100)
FP16 / BF16: 1,979 TFLOPS (3,958 with sparsity)
NVLink: NVLink 4.0, 900 GB/s bidirectional
Interconnect: SXM5 socket
TDP: 700W
Key difference vs H100: +76% memory capacity, +43% memory bandwidth, same compute

NVIDIA B200 SXM5

Architecture: Blackwell (GB100)
GPU Memory: 192 GB HBM3e
Memory Bandwidth: 8 TB/s
FP4 Tensor Core: 18,000 TFLOPS (with sparsity)
FP8 Tensor Core: 9,000 TFLOPS (with sparsity)
FP16 / BF16: 4,500 TFLOPS (with sparsity)
NVLink: NVLink 5.0, 1,800 GB/s bidirectional
Interconnect: SXM5 socket
TDP: ~1,000W (requires direct liquid cooling at full utilization)
Key new capabilities: FP4 precision, RAS Engine, NVLink 5.0, Confidential Compute

NVIDIA B300 SXM (Blackwell Ultra)

Architecture: Blackwell Ultra
GPU Memory: 288 GB HBM3e
Memory Bandwidth: 8+ TB/s
FP8 Tensor Core: higher than B200 (exact figures subject to platform configuration)
NVLink: NVLink 5.0
Cooling: Direct Liquid Cooling mandatory
Key difference vs B200: 50% more memory (288 GB vs 192 GB), higher compute throughput, optimized for frontier model training at 1T+ parameter scale and high-concurrency inference

H100 PCIe vs H100 SXM5 — Form Factor Matters

H100 is available in two form factors with significantly different performance profiles:

H100 SXM5 — installed in NVLink-equipped SXM server baseboard (DGX H100, Supermicro SYS-821GE, Dell XE9680); 80 GB HBM2e, 3.35 TB/s bandwidth, 700W TDP, full 900 GB/s NVLink 4.0 between GPUs; required for training workloads where GPU-to-GPU communication bandwidth is a bottleneck
H100 PCIe — standard PCIe Gen5 x16 card for conventional server slots; 80 GB HBM2e, 2 TB/s bandwidth (lower than SXM5), 350W TDP; no NVLink between GPUs (peer-to-peer via PCIe only); lower cost server platforms, appropriate for inference-dominant workloads or training jobs that are not bandwidth-bound between GPUs

For large-scale LLM training, H100 SXM5 with NVLink is required. For inference serving, H100 PCIe is often sufficient and substantially cheaper per GPU due to lower server platform costs.

H200 PCIe

H200 is available in a PCIe form factor (H200 PCIe NVL) with 141 GB HBM3e at a lower bandwidth than SXM5. H200 PCIe targets inference workloads — specifically serving 70B+ parameter models that previously required two H100 PCIe cards (80+80 GB) but now fit on a single H200 card (141 GB), halving the server cost and power for single-model inference deployments.

GB200 NVL72 — Rack-Scale Blackwell

The GB200 NVL72 is NVIDIA's rack-scale Blackwell architecture combining 36 Grace CPU modules and 72 B200 GPU dies in a single NVLink domain across a full rack. All 72 GPUs share a single NVLink 5.0 fabric with 130 TB/s total bisection bandwidth — effectively making the entire rack behave as a single very large GPU for model parallelism. GB200 NVL72 is the target platform for training frontier models above 1 trillion parameters and for the highest-throughput inference of large deployed models. It requires full rack liquid cooling infrastructure and is the most complex AI infrastructure deployment available. Haink supplies Supermicro and other GB200 NVL72-capable platforms.

When to Choose H100

Established model training where H100-optimized software stacks (NCCL, cuDNN, TensorRT) are already validated and working
Inference of models up to ~34B parameters where 80 GB VRAM is sufficient for the batch size and sequence length required
Organizations building on a tight budget where H100 SXM5 or PCIe platforms are available at lower cost than B200 due to H100's now-mature supply chain
Workloads where compute throughput is the bottleneck (not memory) and the FP8 TFLOPS difference between H100 and B200 does not materially change the training timeline
Environments where 700W per GPU is the maximum supported by existing rack power and cooling infrastructure

When to Choose H200

Inference of large models (70B–405B parameters) where the 80 GB H100 memory limit forces multi-GPU tensor parallelism that H200's 141 GB eliminates — consolidating two H100-based inference instances onto one H200
Training jobs that are HBM bandwidth-bound — where the model's memory access pattern is the limiting factor rather than raw compute, and H200's 4.8 TB/s vs H100's 3.35 TB/s bandwidth delivers a measurable speedup
Long-context inference (128K+ token context windows) where activations and KV cache size exceed H100 memory limits
Organizations already committed to Hopper-generation software and wanting an in-place upgrade path — H200 drops into the same SXM5 socket and server infrastructure as H100

When to Choose B200

New AI training cluster builds where maximizing training throughput per rack is the priority — B200 delivers ~2.3× more FP8 TFLOPS than H100 per GPU, meaning a B200 cluster completes training jobs in less than half the time of an equivalent H100 cluster
Frontier model training at 70B+ parameters where FP4 precision (18,000 TFLOPS) enables further acceleration versus FP8
High-throughput inference where NVLink 5.0's 1,800 GB/s GPU-to-GPU bandwidth (2× H100) reduces inter-GPU communication latency for tensor-parallel inference of large deployed models
Deployments planning for multi-year hardware lifecycles — B200 will have a longer useful training life than H100 before the next generation supersedes it
Organizations with data center liquid cooling infrastructure or willingness to invest in DLC — B200 at full utilization requires direct liquid cooling

When to Choose B300 (Blackwell Ultra)

Frontier model pre-training at 1T+ parameter scale where 288 GB per GPU memory capacity enables tensor parallelism degrees that B200's 192 GB cannot support
Inference of the largest deployed models (Llama 405B, GPT-4 class) at the highest batch sizes and throughput requirements
Organizations building new AI infrastructure in 2025–2026 who want the most forward-looking hardware and are prepared for DLC-mandatory deployment requirements
Hyperscaler and cloud provider AI clusters where maximum FLOPS per rack and maximum memory capacity per GPU are the defining selection criteria

H100 vs H200 vs B200 — Training vs Inference

For LLM Pre-Training

B200 is the best choice for new clusters — 2.3× more FP8 compute per GPU and 2× NVLink bandwidth reduce training time significantly. For existing H100 clusters, H200 is not a meaningful upgrade for compute-bound training since FP8 TFLOPS are identical. H100 SXM5 remains cost-effective for training 7B–70B models where the longer training time is acceptable.

For LLM Inference Serving

H200 is the most impactful upgrade from H100 for inference — 141 GB memory serves larger models on fewer GPUs, reducing infrastructure cost per served token. B200 improves inference throughput further with higher compute and FP4 support. For inference of models that fit in 80 GB (7B–34B at FP16, or 70B at INT4), H100 PCIe remains cost-effective.

For Fine-Tuning

H100 SXM5 or H200 SXM5 are appropriate for most fine-tuning workloads at 7B–70B scale using LoRA or QLoRA. Full fine-tuning of 70B+ models benefits from H200's additional memory. B200 is overkill for fine-tuning unless running multiple concurrent fine-tuning jobs on the same GPU.

Server Platforms Available from Haink

H100 SXM5 / H200 SXM5 — Supermicro SYS-821GE-TNHR (8 GPU), Dell PowerEdge XE9680 (8 GPU), HPE Cray XD670 (8 GPU)
H100 PCIe / H200 PCIe — Supermicro SYS-420GP-TNR (up to 10 GPU), SYS-220HE-TNR (4 GPU), SYS-111E-FWTR (2 GPU)
B200 SXM5 — Supermicro ARS-821GL-NHR (8 GPU), next-generation platforms
B300 SXM — Supermicro ARS-821GL-NHR (B300 configuration), GB300 NVL72 rack-scale
DGX H100 / DGX H200 / DGX B200 — NVIDIA DGX systems available as complete factory-integrated platforms

Where Haink Supplies H100, H200, B200, and B300 Servers

Hong Kong — GPU server platforms with H100/H200/B200/B300 delivered duty-free. AI GPU server supplier Hong Kong →
Dubai — GPU AI infrastructure delivered through Dubai free trade zone logistics for MENA data center deployments. AI GPU server supplier Dubai →
Mainland China — GPU server availability subject to current NVIDIA export control regulations; Haink advises on compliant configurations for Mainland China delivery. AI GPU server supplier Mainland China →

Related Resources

Frequently Asked Questions

What is the difference between H100 and H200?

H100 and H200 use the identical NVIDIA GH100 Hopper die with the same FP8 compute throughput (3,958 TFLOPS). The only difference is memory: H200 has 141 GB HBM3e versus H100's 80 GB HBM2e, providing 76% more GPU memory and 43% more memory bandwidth. H200 improves performance only for workloads limited by GPU memory capacity or bandwidth — primarily inference of large models (70B+) and training runs that are memory-bandwidth-bound. For compute-bound training, H100 and H200 perform identically.

Is B200 much faster than H100?

For FP8 compute, B200 delivers 9,000 TFLOPS versus H100's 3,958 TFLOPS — 2.3× faster in raw tensor compute. For FP4 inference (a new precision tier B200 supports that H100 does not), B200 delivers 18,000 TFLOPS. B200 also has 2.4× more memory (192 GB vs 80 GB), 2.4× more memory bandwidth (8 TB/s vs 3.35 TB/s), and 2× the NVLink bandwidth (1,800 GB/s vs 900 GB/s). In practice, LLM training benchmarks show B200 training the same model in approximately 2–3× less time than H100, depending on how compute-bound vs memory-bound the specific training job is.

Do I need liquid cooling for B200?

B200 at full AI training utilization requires direct liquid cooling (DLC) with cold plates on the GPU — air cooling is insufficient for sustained B200 TDP. H100 and H200 can run in air-cooled server configurations, though DLC improves thermal headroom and reduces data center cooling load for both. Organizations planning B200 deployments must provision DLC infrastructure (rack-level rear-door heat exchangers or direct cold plate liquid loops) before or alongside GPU server procurement. Haink advises on DLC infrastructure requirements for B200 and B300 deployments.

What is NVIDIA B300 and how does it differ from B200?

NVIDIA B300 (Blackwell Ultra) is the next-generation evolution of Blackwell, increasing GPU memory from 192 GB (B200) to 288 GB HBM3e — a 50% increase — while also delivering higher compute throughput than B200. B300 targets frontier model training at 1T+ parameter scale where B200's 192 GB per GPU limits tensor parallelism efficiency, and the highest-throughput inference deployments of the largest deployed models. Like B200, B300 is DLC-mandatory. B300 is available in the same SXM server infrastructure (ARS-821GL platform) as B200.

Should I buy H100 or wait for B200/B300?

For organizations that need GPU compute now, H100 SXM5 remains the most proven, best-supported AI training GPU with the broadest software ecosystem. H100's supply chain is mature and lead times are shorter than B200/B300. If training timeline is the binding constraint and the workload justifies it, B200 delivers 2.3× more throughput. If your primary use case is inference of 70B+ models, H200 offers the best cost-per-token improvement over H100. Haink can advise on current availability and lead times for H100, H200, B200, and B300 server platforms.

What is GB200 NVL72?

GB200 NVL72 is NVIDIA's rack-scale architecture combining 36 Grace ARM CPUs and 72 B200 GPU dies in a single NVLink 5.0 fabric spanning a full liquid-cooled rack, with 130 TB/s total NVLink bisection bandwidth. The entire 72-GPU rack behaves as a single unified compute domain for model parallelism — eliminating the inter-node InfiniBand bottleneck for workloads that fit within the NVLink domain. NVL72 is designed for training frontier models above 1T parameters and serving the largest deployed models at hyperscale. It requires full rack DLC infrastructure and is the most complex AI infrastructure deployment currently available.

Which GPU is best for running local LLMs on a small team server?

For small teams running local LLMs, NVIDIA L40S (48 GB GDDR6) or RTX 6000 Ada (48 GB GDDR6) are more practical and cost-efficient than H100/B200. H100, H200, and B200 are data center GPUs designed for large-scale training and high-throughput inference in rack servers — they require SXM baseboard or high-end PCIe server infrastructure, full DLC for B200, and carry significant cost premiums. For a team running Llama 3.3 70B or DeepSeek-R1 locally, a workstation with one or two RTX 6000 Ada GPUs or an NVIDIA DGX Spark is the appropriate solution. See the AI Workstation page for full guidance.