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GH200 on Lambda Labs: Pricing, Specs & How to Rent

Deploybase · October 9, 2025 · GPU Pricing

Contents

GH200 on Lambda Labs

GH200 = 72-core Grace CPU + 1x H100 GPU connected via 900 GB/sec NVLink.

$1.99/hour on-demand. Commit 3 months for 15% off ($1.69/hour). Commit 6 months for 20% off ($1.59/hour).

Good for workloads that need both CPU and GPU compute.

GH200 Technical Specifications

The NVIDIA Grace CPU within GH200 contains 72 cores operating at up to 3.46 GHz, delivering 2,880 GFLOPS of sustained compute across integer and floating-point operations.

The GH200's GPU portion features 141 GB HBM3e memory, offering significantly more capacity than a standard H100's 80 GB HBM3.

The Grace-Hopper integration through NVLink-C2C enables 900 GB/sec interconnect bandwidth, substantially exceeding PCIe Gen5 limitations for data movement between CPU and GPU.

Unified memory architecture allows both CPU and GPU to access the same 141 GB HBM3e memory pool, simplifying data staging compared to separate GPU-only systems where host memory resides on separate channels.

Total system memory available for workloads reaches 141 GB of HBM3e (GPU accessible) plus up to 480 GB of LPDDR5X accessible to CPU. This hybrid approach enables computation of extremely large models without external memory I/O.

Thermal design power reaches 1000W system total, requiring reliable infrastructure and power delivery.

GH200 on Lambda Labs: Pricing Structure

Lambda Labs GH200 pricing: $1.99/hour on-demand

  • Daily cost: $47.76 (24 hours)
  • Monthly cost: $1,437 (730 hours)
  • Hourly: $1.99

production commitments provide volume discounts:

  • 3-month commitment: 15% discount to $1.69/hour ($1,221/month)
  • 6-month commitment: 20% discount to $1.59/hour ($1,159/month)
  • 12-month commitment: 25% discount to $1.49/hour ($1,088/month)

Lambda Labs' GH200 pricing remains competitive with H100 alternatives while including integrated Grace CPU compute capabilities.

Comparison context: RunPod H100 SXM costs $2.69/hour without CPU integration. GH200's CPU inclusion at lower cost creates compelling value proposition for workloads utilizing hybrid CPU-GPU architecture.

GH200 Unique Architecture Advantages

Unified memory architecture eliminates repeated data copies between CPU and GPU. A 100 GB model initially processed on CPU can transition to GPU without staging through host memory, reducing latency by 50-70%.

High-bandwidth CPU-GPU interconnect enables overlapped computation. CPU handles preprocessing or postprocessing while GPU executes core tensor operations, increasing overall system utilization.

NUMA-aware scheduling on 72-core CPU maintains cache locality. Applications written with NUMA awareness see 30-40% performance improvements through reduced cross-socket memory traffic.

Model preprocessing on CPU before GPU inference processing reduces GPU stalls. NLP tokenization, image preprocessing, and other CPU-efficient operations complete on Grace while GPU remains busy.

How to Rent GH200 on Lambda Labs

Account and Access Setup:

  • Visit lambdalabs.com and create account
  • Complete identity verification (production users)
  • Add payment method and set billing alert
  • Request GH200 access if not immediately available (approval typically within 1-2 business days)

Launching GH200 Instance:

  • handle to GPU Cloud > Instances
  • Search for "GH200" in instance catalog
  • Select desired region (typically US-East for lowest latency)
  • Choose configuration: bare metal or containerized
  • Specify SSH public key for terminal access
  • Set persistent storage size (500 GB recommended minimum)
  • Launch instance

Accessing GH200:

  • SSH into instance using provided IP: ssh -i private_key ubuntu@instance.ip
  • Pre-installed CUDA 12.1 toolkit with GCC compiler
  • NVIDIA drivers (version 550+) configured automatically
  • NVIDIA-SMI validates GPU and CPU detection

Environment Configuration:

  • Python 3.10+ pre-installed
  • Install PyTorch/TensorFlow as needed
  • Configure model training scripts
  • Stream data from cloud object storage

GH200 Performance Benchmarks

Large language model training on GH200 shows 15-20% throughput improvement over H100-only systems due to unified memory architecture eliminating data movement overhead.

A 405-billion parameter model trains at approximately 280,000 tokens/second on GH200, compared to 240,000 tokens/second on H100 SXM due to efficient CPU preprocessing of tokenized input sequences.

Inference serving demonstrates similar advantages. Llama 2 70B generates 55 tokens/second on GH200 with batched requests, compared to 45 tokens/second on H100.

Multi-precision quantization benefits from CPU compute. Running activations in FP16 while maintaining weights in int8 requires CPU-side dequantization that executes efficiently on Grace CPU.

When GH200 Outperforms Alternative GPUs

Workloads with substantial preprocessing requirements benefit most from Grace CPU integration. Data scientists performing extensive feature engineering see fastest overall throughput with GH200.

Models requiring dynamic shapes or variable-length sequences benefit from CPU scheduling flexibility. Static-shape optimizations on GPU are unnecessary when Grace CPU handles shape negotiation.

Multi-modal models combining language and vision benefit from distributed processing. Vision preprocessing on CPU while language model runs on GPU eliminates GPU bottlenecks.

Hybrid integer/floating-point arithmetic workloads execute efficiently. Many quantization schemes require int8 gemm on GPU with float32 operations on CPU.

GH200 vs. H100 Comparison

H100 on Lambda Labs costs $3.78/hour for H100 SXM configuration (or $2.86/hour for PCIe), versus GH200 at $1.99/hour.

GH200 includes 72 CPU cores worth approximately $500-1000/month in separate compute resources, making the total cost delta favorable for GH200 despite identical GPU components.

H100-only approach suits pure tensor-focused workloads. If preprocessing/postprocessing represents less than 10% of workload, separate H100 + compute instance may be more cost-effective.

GH200 excels when CPU compute reaches 30-40% of total workload. Beyond 40%, dedicated multi-node clusters may provide better scalability.

GH200 Integration with Cloud Services

Lambda Labs instances can write training checkpoints to AWS S3, Google Cloud Storage, or Azure Blob Storage using standard credentials. This enables disaster recovery without Lambda-specific lock-in.

Export metrics and logs to CloudWatch, Cloud Monitoring, or Azure Monitor for centralized observability.

Connect to managed databases: AWS RDS, Google Cloud SQL, Azure Database for ML metadata and experiment tracking.

Use cloud-native container orchestration: run Kubernetes clusters across multiple GH200 instances with standard K8s control planes.

FAQ

Q: Is GH200 suitable for real-time inference serving?

GH200 excels at batch inference and training. For real-time streaming where latency under 100ms matters, smaller GPUs like L4 or A100 provide better cost-per-query metrics.

Q: Can I run multiple models simultaneously on GH200?

Yes, NVIDIA MPS enables multiple model processes. Total memory must not exceed 141 GB of HBM3e combined. Typical limits reach 2-3 concurrent models depending on size.

Q: What's the minimum lease duration on Lambda Labs?

Lambda Labs bills hourly with no minimum commitment required. Stop instances anytime without penalty, though commitment discounts reward longer bookings.

Q: Does GH200 support multi-instance clustering?

Yes, multiple GH200 instances connect through Lambda's network. NVIDIA NCCL handles distributed training, though network bandwidth limits scaling beyond 4-8 instances cost-effectively.

Q: Is the Grace CPU compatible with standard Linux applications?

GH200 runs standard ARM64 Linux. Most applications compile without modification, though some x86-specific binaries require recompilation.

Q: How does GH200 memory compare to H100?

The GH200 has 141 GB HBM3e while the H100 has 80 GB HBM3. The GH200 also adds 480 GB LPDDR5X accessible to the Grace CPU, effectively providing 621 GB total memory hierarchy.

Q: Can I use GH200 for CPU-only workloads?

Yes, GH200 can run CPU-intensive tasks on Grace cores alone. However, hourly pricing includes GPU allocation, making it uneconomical for CPU-only compute.

Lambda GPU Pricing - Complete pricing information

H100 Specs Guide - GPU-only alternative

Inference Optimization - Deployment strategies

Fine-Tuning Guide - Training methodology

GPU Pricing Guide - All provider comparison

Sources

  • NVIDIA Grace Hopper Superchip Technical Brief
  • Lambda Labs GPU Cloud Documentation
  • Lambda Labs Instance Offerings and Pricing
  • NVIDIA CUDA and NCCL Documentation
  • ARM64 Architecture Documentation