Deploy LLMs in One Click

In this guide, we’ll cover how to deploy different LLMs, choose the right serving stack, and optimize for latency, cost, scaling, and reliability.

Self-hosting an LLM shouldn’t mean fighting CUDA versions, broken wheels, flash-attn builds, OOMs, and “works locally, fails on the server”.

Deploy Llama 3.1 8B in One Click (Production-Ready)

This page lets you deploy Llama 3.1 8B on our GPU servers with a single click and get a production-ready, OpenAI-compatible API endpoint (with auth, logs, metrics, and sane defaults).

What you get

OpenAI-compatible endpoint (/v1/chat/completions, streaming supported)
Dedicated deployment URL + API key
Presets for context length, quantization, batching, concurrency
Reliability defaults: health checks, auto-restart, timeouts, retries

GPU requirements (recommended)

Llama 3.1 8B is inference-friendly, but your experience depends on VRAM, context length, and precision.

Recommended minimum: 16 GB VRAM
Good baseline: 24 GB VRAM
High throughput / heavy batching: 40–80 GB VRAM

Exact VRAM needs vary by runtime (vLLM/TGI), KV cache size, context length, and concurrency. That’s why our presets are designed to be safe by default.

Expected performance (tokens/sec)

Performance depends on GPU class, quantization, batching, and concurrency. Typical single-stream ranges:

GPU tier	Expected tokens/sec (approx.)	Best for
L4 / A10 class	~35–90 tok/s	MVPs, prototypes, early SaaS
A100 40GB	~90–180 tok/s	higher traffic, lower latency
A100 80GB / H100	~140–300+ tok/s	heavy throughput + batching

Deploy in 3 steps:

Step 1 — Pick your presets for the selected model

Choose:

Precision: FP16 / INT8 / 4-bit
Context length: 8k / 16k / 32k (VRAM dependent)

Step 2 — Click Deploy

We provision the GPU server, pull a tested runtime, and start the model server.

Step 3 — Copy your endpoint + API key

You get:

Base URL
API key
Logs + metrics dashboard
Health endpoint

OpenAI-compatible endpoint snippet

Use your deployed Llama endpoint with OpenAI-style clients.

curl https://YOUR_ENDPOINT/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{ \
    "model": "llama-3.1-8b", 
    "messages": [ 
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Write a concise product description for my app."}
    ],
    "temperature": 0.7,
    "stream": true
  }'

Deploy Qwen 2.5 16B in One Click (Production-Ready)

Cold starts vary by GPU tier and model size. For consistent latency, keep at least one instance warm.

Self-hosting an LLM shouldn’t mean fighting CUDA versions, broken wheels, flash-attn builds, OOMs, and “works locally, fails on the server”.

This page lets you deploy Llama 3.1 8B on our GPU servers with a single click and get a production-ready, OpenAI-compatible API endpoint (with auth, logs, metrics, and sane defaults).

What you get

OpenAI-compatible endpoint (/v1/chat/completions, streaming supported)
Dedicated deployment URL + API key
Presets for context length, quantization, batching, concurrency
Observability: latency, TTFT (time-to-first-token), tokens/sec, GPU memory, error rate
Reliability defaults: health checks, auto-restart, timeouts, retries
Scale controls: concurrency caps + optional warm instances (“keep one hot”)

GPU requirements (recommended)

Llama 3.1 8B is inference-friendly, but your experience depends on VRAM, context length, and precision.

Recommended minimum: 16 GB VRAM
Good baseline: 24 GB VRAM
High throughput / heavy batching: 40–80 GB VRAM

Exact VRAM needs vary by runtime (vLLM/TGI), KV cache size, context length, and concurrency. That’s why our presets are designed to be safe by default.

VRAM guide (rule-of-thumb)

Mode	Min VRAM	Recommended VRAM	Notes
FP16 / BF16	~18–22 GB	24 GB+	Best quality, heavier memory
INT8	~10–14 GB	16 GB	Great balance for most apps
4-bit (AWQ/GPTQ)	~6–10 GB	12–16 GB	Lowest cost, slight quality tradeoff

Expected performance (tokens/sec)

Performance depends on GPU class, quantization, batching, and concurrency. Typical single-stream ranges:

GPU tier	Expected tokens/sec (approx.)	Best for
L4 / A10 class	~35–90 tok/s	MVPs, prototypes, early SaaS
A100 40GB	~90–180 tok/s	higher traffic, lower latency
A100 80GB / H100	~140–300+ tok/s	heavy throughput + batching

Tip: For real UX, track TTFT (time-to-first-token) and p95 latency—not just tokens/sec.

Cold start notes (what happens when it’s “sleeping”)

Cold starts usually include image pulls (first time), weight download (first time per node), kernel compilation/warmup, and server initialization.

Typical ranges:

Warm deployment: ~0.2–2s TTFT
Cold deployment: ~20–120s (depends on GPU + cache + runtime)

How we reduce cold start

prebuilt runtime images with pinned CUDA/PyTorch stacks
weight caching on the node
optional warm instances (“keep one hot”)
readiness checks that only mark the service ready when it can actually serve

Pricing example (simple mental model)

Your cost is basically:

GPU $/hour + storage + bandwidth + (optional) warm instances

Example scenarios:

Solo MVP: 1 GPU, no warm pool, low concurrency
Early startup: 1 GPU + warm instance + dashboards/logs
Production traffic: multiple replicas + autoscaling + tuned concurrency

If you share expected traffic (requests/day, tokens/request, target latency), we can recommend:

a preset (precision, context, concurrency)
expected tokens/sec and TTFT
an estimated $ / 1M tokens

Deploy in 3 steps

Step 1 — Pick your preset