GB300 vs H200: What Actually Changes in Your Code?

H200 represented the final evolution of the Hopper generation. It retained the GH100 architecture used in H100 while expanding memory capacity and bandwidth to 141 GB HBM3e at 4.8 TB/s.

That additional memory headroom mattered because it enabled practical deployment of larger models:

• 70B-class models on a single GPU
  
• 405B-class models on a single 8-GPU node
  
• Larger KV cache residency
  
• Reduced quantization pressure

GB300 belongs to a different infrastructure category entirely.

It introduces:

• Blackwell Ultra architecture
  
• 288 GB HBM3e per GPU
  
• 8 TB/s memory bandwidth
  
• Native NVFP4 tensor execution
  
• Expanded attention acceleration
  
• NVL72 rack-scale deployment architecture
  
• Large coherent NVLink domains

From a software perspective, H200 was primarily a compatibility-preserving upgrade. Existing TensorRT-LLM and Hopper inference stacks transferred with minimal architectural change.

GB300 changes the performance model itself.

Applications still run in PyTorch. Existing Python code still executes. But extracting GB300-class performance requires changes in:

• Precision strategy
  
• Serving topology
  
• Quantization pipelines
  
• Parallelism strategy
  
• KV cache orchestration
  
• Inference scheduling

The key distinction is simple:

H200 improves existing deployment patterns.
GB300 rewards teams that adopt new deployment patterns.

The Two Software Stacks Being Compared

H200: Mature Hopper Stack

The Hopper software ecosystem is operationally mature.

H200 deployments typically rely on:

• CUDA 12.x
  
• TensorRT-LLM
  
• cuDNN
  
• vLLM
  
• SGLang
  
• FlashAttention-3
  
• Transformer Engine

Most production issues have already been surfaced and optimized across H100/H200 deployments.

For teams deploying inference today, the Hopper stack is stable, predictable, and broadly supported.

GB300: Blackwell Ultra Stack

GB300 requires the Blackwell software path.

This includes:

• CUDA 12.8+
  
• New cuDNN fast paths
  
• NVFP4 precision support
  
• MXFP4 and MXFP6 support
  
• Updated attention kernels
  
• Dynamo disaggregated serving
  
• Expanded NVLink orchestration
  
• New TensorRT-LLM optimization paths

The application-level code may look identical, but the achievable performance ceiling changes substantially depending on whether teams adopt these new capabilities.

This is the central difference between H200 and GB300.

What Actually Changes in Practice

For teams already running inference on H200-class infrastructure, migration to GB300 does not require rewriting application logic.

Models still load normally.

Framework APIs remain largely unchanged.

The major changes happen underneath the serving layer.

Tensor Parallelism Expands

GB300’s NVL72 architecture enables much wider tensor and expert parallelism domains than Hopper-era systems.

Frameworks such as:

• vLLM
  
• TensorRT-LLM
  
• SGLang

can increasingly exploit rack-scale NVLink fabrics transparently.

The application code does not change, but placement strategy and communication topology do.

Throughput Gains Depend on Software Adoption

The largest GB300 benchmark numbers are not generated from default deployments.

They depend on:

• NVFP4 quantization
  
• TensorRT-LLM optimization
  
• Disaggregated serving
  
• Optimized scheduler paths
  
• Updated attention kernels
  
• KV cache routing strategies

Without these optimizations, GB300 behaves more like an incremental silicon upgrade over H200.

The practical takeaway is important:

The hardware advantage exists, but software determines how much of it becomes visible.

TensorRT-LLM vs vLLM vs SGLang

TensorRT-LLM

TensorRT-LLM currently delivers the highest achievable throughput on Blackwell infrastructure.

However, the operational tradeoff is complexity.

Teams adopting TensorRT-LLM at scale typically need to manage:

• Quantization workflows
  
• Precision tuning
  
• Multi-stage serving
  
• KV cache routing
  
• Performance-specific deployment engineering

For hyperscale inference, that investment can be justified.

For smaller teams, the operational overhead may outweigh the gain.

vLLM

vLLM remains the most developer-accessible inference framework.

It offers:

• Faster iteration cycles
  
• Simpler deployment
  
• Broad ecosystem adoption
  
• Strong community support

However, the gap between default vLLM deployment and optimized Blackwell deployment remains significant.

Teams using vLLM without Blackwell-specific optimization paths should not expect full GB300-class performance scaling automatically.

SGLang

SGLang increasingly occupies the middle ground.

It delivers:

• Strong MLPerf-class performance
  
• Simpler operational workflows than TensorRT-LLM
  
• Better developer ergonomics
  
• Increasing Blackwell optimization support

For many new GB300 deployments, SGLang is becoming a practical balance between operational simplicity and high-end performance.

NVFP4 Is the Core Architectural Shift

The most important GB300 feature is not raw FLOPS. It is NVFP4.

Without NVFP4, GB300’s advantage over H200 narrows substantially.

At equal precision levels, GB300 may deliver roughly:

• 1.5x–2.5x performance improvement over H200

The larger headline gains depend on FP4 deployment.

What NVFP4 Actually Changes

NVFP4 combines:

• Lower precision execution
  
• Smaller memory footprint
  
• Higher tensor throughput
  
• Better batching efficiency

Compared with FP8:

• Weight storage shrinks significantly
  
• Tensor core throughput increases
  
• Larger models fit more efficiently
  
• Larger batch sizes become practical

Importantly, this is not equivalent to older low-precision INT4 quantization approaches.

The scaling and precision management techniques used in NVFP4 preserve accuracy far more effectively than earlier 4-bit methods.

The Practical Constraints

NVFP4 adoption still depends on workload behavior.

Performance gains may shrink when:

• Models are small
  
• Workloads are compute-bound
  
• Certain routing layers are precision-sensitive
  
• Framework kernels lack full support
  
• Mixed-precision exceptions are required

H200 does not support FP4 execution paths.

This means most large GB300 marketing comparisons implicitly compare:

• GB300 running NVFP4
  
• H200 running FP8

That distinction matters when interpreting benchmark claims.

Attention Performance Is a Larger Deal Than Many Teams Realize

Blackwell Ultra significantly improves attention-path execution.

The key hardware change is expanded EX2 Special Function Unit throughput.

This matters because long-context transformer inference becomes increasingly dominated by:

• Softmax
  
• Exponential operations
  
• Attention-layer execution

At shorter context lengths, tensor core throughput dominates.

At very long context lengths, attention operations become the bottleneck.

This is where GB300 gains become much more pronounced relative to H200.

For workloads involving:

• 128K+ context
  
• Reasoning models
  
• High-concurrency long-context inference

GB300’s attention-path improvements can matter more than FP4 acceleration itself.

Importantly, these improvements are mostly automatic once the stack is compiled against updated Blackwell toolchains.

Disaggregated Serving Changes Inference Architecture

One of the largest operational shifts in Blackwell-era inference is disaggregated serving.

Traditional inference colocates:

• Prefill
  
• Decode

on the same GPU resources.

These workloads have fundamentally different characteristics:

Phase	Characteristics
Prefill	Compute-heavy, batch-friendly
Decode	Memory-bandwidth-heavy, latency-sensitive

Disaggregated serving separates them into optimized GPU pools.

GB300 NVL72 is specifically designed for this model.

Its large coherent NVLink fabric allows:

• Fast KV cache transfer
  
• Separate scaling domains
  
• Better GPU utilization
  
• Higher throughput
  
• Lower latency variability

This architecture is difficult to justify operationally on H200 because interconnect overhead becomes much more expensive outside NVL72-class fabrics.

On GB300, disaggregated serving becomes economically viable.

Training: Where GB300 Actually Matters

For smaller fine-tuning workloads, H200 remains highly effective.

Examples include:

• LoRA fine-tuning
  
• Sub-70B adaptation
  
• RLHF iteration
  
• Mid-scale continued pretraining

In many of these workloads, GB300 delivers only incremental gains relative to deployment complexity and cost.

Frontier-Scale Training

GB300 becomes meaningfully different for:

• Frontier pretraining
  
• Very large MoE models
  
• Long-context training
  
• NVFP4-enabled training pipelines

The larger HBM pool reduces:

• Aggressive parallelism requirements
  
• Activation memory pressure
  
• Optimizer-state fragmentation
  
• Synchronization overhead

At large scale, those operational simplifications matter significantly.

What This Means for Stack Decisions

The most important decisions are no longer only hardware decisions.

They are framework and deployment-pattern decisions.

Teams Optimizing for Peak Performance

These teams should evaluate:

• TensorRT-LLM
  
• NVFP4
  
• Dynamo
  
• Disaggregated serving
  
• NVL72 deployment topology

This path delivers the highest achievable Blackwell performance but increases operational complexity.

Teams Optimizing for Developer Velocity

These teams may prefer:

• vLLM
  
• SGLang
  
• FP8 deployment
  
• Simpler serving topologies

In these scenarios, H200 may remain the more economical and operationally efficient platform.

Cost and Deployment Reality

H200 remains more widely available and operationally mature.

For:

• Development
  
• Prototyping
  
• Mid-scale inference
  
• Standard production serving

H200 often remains the more practical deployment target.

GB300 economics improve significantly only when teams fully adopt:

• NVFP4
  
• Rack-scale serving
  
• Disaggregated inference
  
• Long-context optimization

Without those changes, the cost premium becomes harder to justify.

Conclusion

H200 is the platform most teams can deploy efficiently today.

GB300 is the platform optimized for the next generation of inference architecture.

The key distinction is not whether code runs. Existing code runs on both.

The real difference is whether the inference stack evolves to exploit:

• NVFP4
  
• Blackwell attention acceleration
  
• NVL72-scale communication
  
• Disaggregated serving
  
• Rack-scale inference topology

At equal precision levels, GB300 delivers a meaningful but moderate silicon advantage over H200.

The larger performance multipliers appear only when the software stack evolves alongside the hardware.

In practice, that means:

• H200 remains highly effective for standard FP8 inference
  
• GB300 becomes transformative when paired with next-generation serving architecture

The hardware matters. The software path matters more.

FAQ