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Hardware Fleet78+ Models Online

NovaPod Hardware Fleet & GPU Catalog

From single-GPU development workstations (RTX 4090 / 5090) to 8-way Hopper (H100 / H200) and Blackwell (B200 / B300 / GB300) superclusters. Deploy via 1-click Docker pods, root KVM VPS, or multi-node NVLink fabrics with 1-second crypto billing.

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Selection Framework

Match the Hardware to Your Computational Boundary

1. LLM Serving & Real-Time Inference

LLM token generation is predominantly memory-bandwidth bound. A 70B model in FP16 requires ~140 GB VRAM before KV cache; one H200 (141 GB, 4.8 TB/s) or MI300X (192 GB, 5.3 TB/s) hosts it cleanly on a single device. For sub-30B models, L40S (48 GB) or RTX PRO 6000 (96 GB) provides maximum tokens per dollar.

2. Pre-Training & Distributed Fine-Tuning

Dense and mixture-of-experts training runs require high-bandwidth gradient synchronization. NovaPod provides 8× GPU nodes interconnected via 900 GB/s NVLink 4 (Hopper) and 1.8 TB/s NVLink 5 (Blackwell), paired with 3.2 Tb/s Quantum-2 InfiniBand RDMA for seamless multi-node scaling with PyTorch FSDP and Megatron-LM.

3. Media Synthesis, VFX & Prototyping

For ComfyUI Flux.1 workflows, 3D Gaussian Splatting, and Blender rendering, consumer Ada (RTX 4090) and Blackwell (RTX 5090) provide immense raw FP32 compute at spot rates starting from $0.40/hr. Deploy with 25 Gb/s persistent storage so checkpoints remain safe when jobs finish.

Available Accelerator Classes & Topologies

NVIDIA Flagship Datacenter & Superclusters

Hopper & Blackwell architecture with NVLink 900 GB/s - 1.8 TB/s and InfiniBand RDMA.

NVIDIA H200 SXM5141 GB HBM3e
Bandwidth: 4.8 TB/s
Interconnect: NVLink 4 (900 GB/s)

Frontier LLM inference (70B-405B), long-context decode, high-batch serving

Hardware Deep Dive →
NVIDIA H100 SXM580 GB HBM3
Bandwidth: 3.35 TB/s
Interconnect: NVLink 4 (900 GB/s)

FP8 model fine-tuning, continuous pre-training, high-throughput vLLM serving

Hardware Deep Dive →
NVIDIA B200 SXM6192 GB HBM3e
Bandwidth: 8.0 TB/s
Interconnect: NVLink 5 (1.8 TB/s)

Dense & MoE training, native FP4 inference, ultra-low latency generation

Hardware Deep Dive →
NVIDIA B300 SXM6288 GB HBM3e
Bandwidth: 8.0 TB/s
Interconnect: NVLink 5 (1.8 TB/s)

Extreme memory-bound workloads, massive KV cache pooling, trillion-param scale

Hardware Deep Dive →
NVIDIA GB300 NVL72288 GB/GPU (20 TB Rack)
Bandwidth: 8.0 TB/s
Interconnect: NVLink-C2C (900 GB/s)

Rack-scale AI factories, unified Grace CPU memory, trillion-parameter training

Hardware Deep Dive →
NVIDIA A100 SXM480 GB HBM2e
Bandwidth: 2.0 TB/s
Interconnect: NVLink 3 (600 GB/s)

Cost-efficient 80GB fine-tuning, FP16/BF16 deep learning, multi-GPU FSDP

Hardware Deep Dive →

Workstation & High-Throughput Inference

High VRAM PCIe cards optimized for batch inference, media generation, and rendering.

NVIDIA RTX PRO 600096 GB GDDR7 ECC
Bandwidth: 1.79 TB/s
Interconnect: PCIe 5.0 x16

Single-card 70B inference, massive ECC memory, 3D visualization & rendering

Hardware Deep Dive →
NVIDIA L40S48 GB GDDR6 ECC
Bandwidth: 864 GB/s
Interconnect: PCIe 4.0 x16

Sub-30B LLM serving, batch Flux.1 image synthesis, Omniverse & Blender rendering

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NVIDIA RTX A600048 GB GDDR6
Bandwidth: 768 GB/s
Interconnect: PCIe 4.0 x16

Stable Diffusion batch generation, mid-tier fine-tuning, CAD & VFX pipelines

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Consumer Flagship & Development Cards

Top-tier GeForce cards for prototype development, small fine-tunes, and ComfyUI workflows.

NVIDIA GeForce RTX 509032 GB GDDR7
Bandwidth: 1.79 TB/s
Interconnect: PCIe 5.0 x16

Blackwell dev workstation, rapid iteration, local LLM evaluation, video diffusion

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NVIDIA GeForce RTX 409024 GB GDDR6X
Bandwidth: 1.01 TB/s
Interconnect: PCIe 4.0 x16

LoRA fine-tuning, ComfyUI Flux studios, entry-level research at under $0.50/hr

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NVIDIA GeForce RTX 309024 GB GDDR6X
Bandwidth: 936 GB/s
Interconnect: PCIe 4.0 x16

Budget-conscious 24GB AI training, testing Docker pipelines from $0.10/hr

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AMD Instinct & Open Ecosystem

AMD CDNA 3 architecture powered by ROCm open-source stack.

AMD Instinct MI300X192 GB HBM3
Bandwidth: 5.3 TB/s
Interconnect: Infinity Fabric (896 GB/s)

Raw 192GB memory per GPU, ROCm 6.2 deep learning, PyTorch vLLM serving

Hardware Deep Dive →

Dedicated GPU Deep-Dive Guides

In-depth architecture analysis, benchmark performance, and execution recommendations for individual accelerators.

Vera Rubin

Vera Rubin is the platform after Blackwell Ultra: a Rubin GPU paired with a Vera CPU. Published NVL72 figures put 288 GB of HBM4 on each GPU at up to 22 TB/s, with NVFP4 inference around 50 PFLOPS and NVLink 6 at 3.6 TB/s. NVIDIA still marks those numbers preliminary. Broad cloud availability is expected after the first 2026 cohort.

GB300

GB300 is the NVL72 rack built from B300 GPUs and Grace CPUs. A full rack is 72 B300 GPUs and 36 Grace CPUs on one NVLink 5 domain, with about 20 TB of HBM3e. Smaller slices still sit in that architecture. It is the rack you ask for when a single 8-GPU node is not enough memory.

B300 SXM6

B300 is the Blackwell Ultra GPU: 288 GB of HBM3e at 8 TB/s, fifth-generation Tensor Cores, and NVLink 5 at 1.8 TB/s. The extra 96 GB over B200 is what matters when a 200B-class model or a very long context does not fit in 192 GB. A typical node quote pairs the GPU with about 32 vCPUs and 184 GB of host RAM.

B200 SXM6

B200 is the Blackwell datacenter GPU most teams mean when they say Blackwell: 192 GB HBM3e, 8 TB/s, NVLink 5, and a Transformer Engine with FP4. It is the step up from H200 when you need more than 141 GB or you want FP4. A common node shape is about 30 vCPUs and 184 GB of host memory beside the 192 GB of VRAM.

H100 SXM5

H100 SXM5 is the Hopper datacenter GPU: 80 GB HBM3, 3.35 TB/s, fourth-generation Tensor Cores, and FP8 through the Transformer Engine. A typical SXM node quote is 26 vCPUs, 116 GB of host RAM, and 80 GB of VRAM. It is still the default card for 30B–70B training and production FP8 serving.

H200 SXM5

H200 keeps H100's Hopper compute and replaces the memory system: 141 GB of HBM3e at 4.8 TB/s. The menu shape you see in the catalog is a larger host, about 44 vCPUs and 182 GB of RAM, next to 141 GB of VRAM. Pick it when H100 jobs fail on memory, not on FLOPS.

RTX PRO 6000

RTX PRO 6000 Blackwell is a 96 GB GDDR7 ECC card with about 1.79 TB/s of bandwidth, 24,064 CUDA cores, and FP4 Tensor Cores. It is a PCIe workstation part: no NVLink. The small host in the menu, 8 vCPUs and 24 GB RAM beside 96 GB of VRAM, matches a single-GPU inference box more than an HGX node.

A100 SXM4

A100 80GB SXM4 is the Ampere workhorse: 80 GB HBM2e, about 2.0 TB/s, NVLink at 600 GB/s, and MIG if you want to split the card. The catalog host is 14 vCPUs and 100 GB of RAM. It remains the right spend for 7B–30B training and quantized 70B inference when you do not need FP8.

MI300X

MI300X is AMD's 192 GB HBM3 accelerator at about 5.3 TB/s. One GPU can hold a 70B model in BF16 that would need two H100s or FP8 on an H200. The catalog host is 8 vCPUs and 241 GB of RAM. The software stack is ROCm, not CUDA.

Frequently Asked Questions

How do I choose the right GPU for my AI project?
Start from VRAM requirements: For 7B–14B models, a 24GB or 32GB card (RTX 4090/5090) is ideal and cost-effective. For 70B models, a single 80GB H100 (FP8 quantized) or 141GB H200 (BF16 native) gives optimal throughput. For multi-node distributed training, always pick NVLink-connected datacenter parts (H100, H200, B200) to avoid slow PCIe interconnect bottlenecks.
How is pricing calculated on NovaPod?
NovaPod operates on transparent, competitive spot asks with true per-second metering accuracy. There are no monthly minimums or licensing lock-ins. When you terminate an instance, billing ceases instantly.
What is the difference between Docker GPU Pods and Root KVM VPS?
Docker Pods boot in under 15 seconds with pre-cached frameworks (PyTorch, vLLM, ComfyUI) and have zero idle fees when destroyed. Root KVM VPS gives you a dedicated OS with sudo root, independent public IPv4, and persistent NVMe drives that stay preserved when paused (charging only $0.00014/GB/hr for disk storage).
Can I pay with cryptocurrency without completing KYC?
Yes. NovaPod is engineered with native Web3 financial rails. You receive dedicated on-chain deposit addresses for USDT (TRC-20, ERC-20, BEP-20), USDC (Solana, Base, Ethereum), native SOL, ETH, or BNB. Once confirmed on-chain, credits reflect automatically with zero mandatory KYC.
Does NovaPod support shared persistent storage?
Yes. You can mount high-speed shared network storage (up to 25 Gb/s) across multiple instances simultaneously, ensuring model weights and checkpoints outlive individual compute machines.

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