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Deploy DeepSeek V4 Flash on your own H200s: requirements, boot time, cost
DeepSeek V4 Flash is a 284B-parameter mixture of experts model that activates 13B parameters per token, released by DeepSeek under the MIT license. It is the largest model in the LLM Hangar catalog. This guide covers what it takes to run it privately in your own cloud account: the hardware it needs, how long a deployment actually takes, what it costs, and how to call it once it is up.
Hardware requirements
| Configuration | GPUs | Notes |
|---|---|---|
| Recommended | 2x NVIDIA H200 (141 GB each) | The configuration we measure below |
| Alternative | 4x NVIDIA H100 (80 GB each) | Same silicon generation, ranked behind the H200 shape |
| Weights on disk | 159.6 GB in 46 shards (fp8 with fp4 expert weights, the publisher's only release) | |
| Context length | 32,768 tokens served; the model's native context is 1M via YaRN | |
The GPUs must be Hopper-class or newer. The fp8 release does not run on A100s.
What we measured
Numbers below are from live deployments on 2x H200 through RunPod in a European region (Iceland), launched with LLM Hangar into our own account. Nothing here is extrapolated from a spec sheet.
| Metric | Measured |
|---|---|
| Cold boot, fresh instance to ready endpoint (2026-08-11) | 21 minutes 26 seconds |
| Cold boot, repeat run (2026-08-18) | about 21 minutes |
| Downloaded during boot | 159.6 GB of weights |
Throughput and time-to-first-token figures are only worth publishing together with the workload they were measured under. Ours will be added from the next benchmark run, measured at 4,096 input tokens and 1,024 output tokens at concurrency 1.
What it costs
Your cloud provider bills you directly for the GPUs. LLM Hangar is a flat subscription on top, so the infrastructure cost is transparent:
| Item | Observed |
|---|---|
| 2x H200 on RunPod, per hour | $7 to $14, rate fixed at pod creation |
| Around the clock, per month | roughly $5,100 to $10,200 |
| Business hours only (about 176 h per month) via a wake/sleep schedule | roughly $1,230 to $2,460 |
Those are the rates we observed at our own boots in August 2026, not a quotation. Your exact rate is shown in the dashboard before you confirm anything.
Hard budget caps and auto-destroy are built in, so a forgotten deployment cannot run up an open-ended bill. See budget caps and auto-destroy.
Current prices, live
Deploying it
- Connect your cloud account (AWS, Nebius, or RunPod).
- In the dashboard, choose DeepSeek V4 Flash from the catalog.
- Pick the 2x H200 shape and a region. EU-only residency is available.
- Review the cost estimate, set a budget cap, and confirm.
- Watch the deployment progress live. When it is ready you get a private endpoint and an API key.
Calling the endpoint
The endpoint is OpenAI-compatible, so existing clients work by changing the base URL:
from openai import OpenAI
client = OpenAI(
base_url="https://abc12345.gw.llmhangar.com/v1",
api_key="YOUR_API_KEY",
)
resp = client.chat.completions.create(
model="deepseek-v4-flash",
messages=[{"role": "user", "content": "Hello"}],
stream=True,
)More clients (curl, JavaScript, LangChain, Vercel AI SDK, editor configs) are in using your endpoint.
Why run it privately
Prompts and responses stay on an instance in your own account. The infrastructure belongs to you, your provider bills you for it directly, and when you delete the deployment we verify nothing is left running.