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Compare GPU and LLM inference API pricing between Gonka and Runpod. Find the best rates for AI training, inference, and ML workloads.
Provider 1
Provider 2
| GPU Model ↑ | Gonka Price | Runpod Price | Price Diff ↕ | Sources |
|---|---|---|---|---|
A100 PCIE 40GB VRAM • Runpod | Not Available | — | ||
A100 PCIE 40GB VRAM • | ||||
A100 SXM 80GB VRAM • Runpod | Not Available | — | ||
A100 SXM 80GB VRAM • | ||||
A40 48GB VRAM • Runpod | Not Available | — | ||
A40 48GB VRAM • | ||||
B200 180GB VRAM • Runpod | Not Available | — | ||
B200 180GB VRAM • | ||||
H100 NVL 94GB VRAM • Runpod | Not Available | — | ||
H100 NVL 94GB VRAM • | ||||
H100 PCIe 80GB VRAM • Runpod | Not Available | — | ||
H100 PCIe 80GB VRAM • | ||||
H100 SXM 80GB VRAM • Runpod | Not Available | — | ||
H100 SXM 80GB VRAM • | ||||
H200 141GB VRAM • Runpod | Not Available | — | ||
H200 141GB VRAM • | ||||
HGX B300 288GB VRAM • Runpod | Not Available | — | ||
HGX B300 288GB VRAM • | ||||
L4 24GB VRAM • Runpod | Not Available | — | ||
L4 24GB VRAM • | ||||
L40 40GB VRAM • Runpod | Not Available | — | ||
L40 40GB VRAM • | ||||
L40S 48GB VRAM • Runpod | Not Available | — | ||
L40S 48GB VRAM • | ||||
MI300X 192GB VRAM • Runpod | Not Available | — | ||
MI300X 192GB VRAM • | ||||
RTX 3070 8GB VRAM • Runpod | Not Available | — | ||
RTX 3070 8GB VRAM • | ||||
RTX 3080 10GB VRAM • Runpod | Not Available | — | ||
RTX 3080 10GB VRAM • | ||||
A100 PCIE 40GB VRAM • Runpod | Not Available | — | ||
A100 PCIE 40GB VRAM • | ||||
A100 SXM 80GB VRAM • Runpod | Not Available | — | ||
A100 SXM 80GB VRAM • | ||||
A40 48GB VRAM • Runpod | Not Available | — | ||
A40 48GB VRAM • | ||||
B200 180GB VRAM • Runpod | Not Available | — | ||
B200 180GB VRAM • | ||||
H100 NVL 94GB VRAM • Runpod | Not Available | — | ||
H100 NVL 94GB VRAM • | ||||
H100 PCIe 80GB VRAM • Runpod | Not Available | — | ||
H100 PCIe 80GB VRAM • | ||||
H100 SXM 80GB VRAM • Runpod | Not Available | — | ||
H100 SXM 80GB VRAM • | ||||
H200 141GB VRAM • Runpod | Not Available | — | ||
H200 141GB VRAM • | ||||
HGX B300 288GB VRAM • Runpod | Not Available | — | ||
HGX B300 288GB VRAM • | ||||
L4 24GB VRAM • Runpod | Not Available | — | ||
L4 24GB VRAM • | ||||
L40 40GB VRAM • Runpod | Not Available | — | ||
L40 40GB VRAM • | ||||
L40S 48GB VRAM • Runpod | Not Available | — | ||
L40S 48GB VRAM • | ||||
MI300X 192GB VRAM • Runpod | Not Available | — | ||
MI300X 192GB VRAM • | ||||
RTX 3070 8GB VRAM • Runpod | Not Available | — | ||
RTX 3070 8GB VRAM • | ||||
RTX 3080 10GB VRAM • Runpod | Not Available | — | ||
RTX 3080 10GB VRAM • | ||||
| Model ↑ | Gonka | Runpod | Input Diff ↕ |
|---|---|---|---|
Kimi | Not available | $0.950 in $4.00 out | — |
Kimi | Not available | $0.950 in $4.00 out | — |
Moonshot | Not available | $3.00 in $15.00 out | — |
Alibaba | Not available | $10.00 in $10.00 out | — |
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Developers send requests through an OpenAI-compatible endpoint and pay for usage in GNK
A transformer-based proof-of-work mechanism aims to direct nearly all participating hardware at AI inference rather than at separate security computation
GPU owners register as hosts, post collateral, run ML nodes, and are rewarded based on the amount and quality of compute they contribute
Documented bootstrap procedures for hosting models such as DeepSeek, Kimi, and MiniMax across the network
Developers can run their own gateway and broker setup instead of relying on a shared entry point
Ethereum bridge and IBC routes for moving GNK and USDT in and out of the network
Access to a wide range of GPU types with enterprise-grade security
Only pay for the compute time you actually use
Programmatically manage your GPU instances via REST API
Pods typically ready in 20-30 s
SSH & VS Code tunnels built-in
Automatic migration on pre-empt
On‑demand single‑node GPU instances in Secure Cloud (data‑center hosted) or lower‑cost Community Cloud, with flexible templates and storage.
Pay‑per‑second endpoints with automatic scaling for production AI/ML applications.
Fully managed multi‑node GPU clusters with high‑speed networking between nodes.
Inference is paid per request in GNK; per-model rates are published in the network documentation rather than as USD hourly rates
GPU hosts earn GNK according to the amount and quality of compute they contribute to the network
Pods hosted in vetted data centers; higher reliability tier
Pods on vetted community hosts at lower rates than Secure Cloud
Interruptible capacity at reduced prices
Pay-per-second billing for endpoint execution time, with automatic scaling and no charge when idle
Per-hour or per-second billing for multi-node GPU clusters with no commitment, scaling up to 64 GPUs
Discounted rates for 1, 3, 6, or 12+ month commitments on dedicated clusters with guaranteed availability and SLA-backed uptime; quoted by sales
Usage-based billing for pre-deployed model APIs, charged per request, per token, or per 1000 characters depending on the model
Set up a wallet to hold GNK and sign network transactions
Acquire GNK on Uniswap or bridge tokens in from Ethereum
Follow the developer quickstart to point an existing SDK at the network, or run your own gateway
Review the hardware specifications, post collateral, and follow the node setup guide to serve inference
Sign up for Runpod using your email or GitHub account
Add a credit card or cryptocurrency payment method
Select a template and GPU type to launch your first instance
Documentation site with FAQ and error reference, Discord community, GitHub repository, and a vulnerability reporting process
31 global regions worldwide
Documentation site, support request form, public status page, and a community Discord