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How to Host Open Source LLMs: GPU Cloud Cost Comparison

Deploybase · February 20, 2025 · LLM Guides

Contents

Why Host Open Source LLMs

Self-hosted open source LLMs give developers cost control, data privacy, and full customization. No vendor lock-in. No recurring API bills that grow with scale.

Cost. OpenAI's GPT-4 API runs $0.03 per 1K input tokens and $0.06 per 1K output tokens. A 100-token request: $0.009. At 1 million monthly requests, that's $9,000+. Self-hosting open source on modest GPUs cuts this 50-80% with comparable quality.

Privacy. If handling sensitive data, proprietary info, or regulated content, cloud APIs are off the table. Compliance requires data staying in-house. Self-hosted means the data never leaves the servers.

Customization. Fine-tune on domain-specific data. Train on proprietary knowledge bases. Add custom vocabularies. APIs don't let developers do this. Open source does - all the way.

Llama 2 / Llama 3 family (as of March 2026)

Meta's Llama dominates. Llama 3 hits commercial quality for general work. Sizes: 8B, 70B, 405B (Llama 3.1). The 8B runs on cheap GPUs. 70B is the production standard.

Mistral 7B / Mixtral

Mistral trades performance for efficiency. Mistral 7B matches Llama 2 70B while using 90% less compute. Mixtral 8x7B uses mixture-of-experts - activates only relevant model parts, dropping latency. Great for cost-sensitive builds.

Phi-3 / Phi-4

Microsoft's Phi series cuts fat through training design. Phi-3 (3.8B) shocks with performance on specialized tasks. Beats overkill commercial models at summarization, code, and classification.

Qwen / Baichuan

Chinese models built for multilingual. Qwen competes with Llama. Baichuan specializes in Chinese. Matter for non-English markets.

Open Llama / Alpaca derivatives

Community fine-tunes of Llama. Marginal gains for niche cases. Less stable. Use for testing, not production.

GPU Requirements by Model Size

3B parameters (e.g., Phi-3):

  • Minimum VRAM: 8GB
  • Recommended: 12-16GB
  • Cost-efficient GPU: L4 at $0.44/hour (RunPod)
  • Throughput: 20-40 tokens/second
  • Best for: Experimentation, edge deployment, cost-conscious teams

7B parameters (e.g., Mistral 7B, Llama 3 8B):

  • Minimum VRAM: 16GB
  • Recommended: 24GB
  • Cost-efficient GPU: L40S at $0.79/hour (RunPod)
  • Throughput: 40-80 tokens/second
  • Best for: Production endpoints, balanced cost/performance

13B parameters:

  • Minimum VRAM: 28GB
  • Recommended: 40GB
  • Cost-efficient GPU: A100 PCIe at $1.19/hour (RunPod)
  • Throughput: 30-60 tokens/second
  • Best for: Higher quality outputs, moderate scale

70B parameters (e.g., Llama 3 70B):

  • Minimum VRAM: 80GB
  • Recommended: 80GB+ with distributed inference
  • Cost-efficient GPU: H100 PCIe at $1.99/hour (RunPod)
  • Throughput: 40-100 tokens/second depending on optimization
  • Best for: Commercial-grade deployments, complex reasoning

400B+ parameters (e.g., Llama 3.1 405B):

  • Minimum VRAM: 400GB+ across multiple GPUs
  • Recommended: 8xH100 or 8xH200 clusters
  • Cost: CoreWeave 8xH200 at $50.44/hour
  • Throughput: 100-200 tokens/second on dedicated infrastructure
  • Best for: Frontier research, production deployments only

Cloud Provider Cost Comparison

Small model deployment (Mistral 7B on L40S):

  • RunPod: $0.79/hour = $580/month (24/7)
  • Lambda: $0.92/hour = $672/month
  • CoreWeave: No direct pricing, multi-GPU pricing only
  • Clear winner: RunPod

Medium deployment (Llama 70B on H100 PCIe):

  • RunPod: $1.99/hour = $1,452/month
  • Lambda: $2.86/hour = $2,088/month
  • CoreWeave: $49.24/hour for 8xH100 = $36,000/month (but serves massive scale)
  • Clear winner: RunPod for single-GPU, CoreWeave for multi-GPU

Large-scale deployment (8xH200 cluster):

  • RunPod: No 8-GPU bundles, must rent separately (~$28.72/hour) = $20,965/month
  • Lambda: No multi-GPU bundles
  • CoreWeave: 8xH200 at $50.44/hour = $36,821/month
  • Clear winner: RunPod if available; otherwise CoreWeave

Batch processing (overnight model serving):

  • RunPod H100 ($1.99/hour) for 8 hours/night = $11.92/night = $358/month
  • Lambda for 8 hours/night = $17.04/month = $512/month
  • CoreWeave 8xH100 overnight: $394/month
  • Recommendation: RunPod for small-scale, CoreWeave for large-scale

Infrastructure Setup Guide

Step 1: Choose a model

Mistral 7B handles most tasks cheaply. Llama 70B only if developers need complex reasoning or domain-specific power.

Step 2: Select GPU tier

Match VRAM to model size plus headroom for context and batching. 13B needs 28GB minimum; 40GB is comfortable. Undersized setups bottleneck hard.

Step 3: Pick a provider

RunPod wins for single-GPU under 100 tokens/sec. For bigger clusters, check CoreWeave's bundles against renting individual GPUs on RunPod.

Step 4: Deploy with vLLM or TGI

vLLM and Text Generation Inference handle loading, quantization, batching. Both run easily on RunPod via Docker.

docker run -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm/vllm-openai:latest \
  --model mistralai/Mistral-7B-Instruct-v0.2

Step 5: Configure auto-scaling

Production needs load balancers across multiple GPUs. Ray Serve or Kubernetes orchestrate. RunPod serverless is simpler for unpredictable traffic.

Step 6: Monitor costs

Track spending hourly. Alert on overruns. Benchmark model quality regularly. A $1K/month endpoint with worse results is just waste.

FAQ

How much does it cost to run Llama 70B continuously?

Running Llama 70B on RunPod H100 PCIe costs $1.99/hour. Monthly continuous operation reaches $1,452 (accounting for brief maintenance windows). This serves thousands of requests daily at $0.001-0.003 per request depending on context length, making it cost-comparable to API providers.

Can I run open source models cheaper than OpenAI or Anthropic APIs?

At 1M+ monthly requests, self-hosting saves 50-70%. Under 10K monthly, APIs still win when you count engineering overhead. Compare total cost of ownership including DevOps time.

What about model quantization to reduce GPU requirements?

Quantization shrinks model size 50-75% with 2-5% quality loss. An 8-bit quantized 70B fits a 40GB A100, dropping cost from $1.99 to $1.19/hour. Benchmark quantized models on production workloads first.

Is it worth fine-tuning open source models?

Fine-tuning pays off when domain-specific output beats compute cost. Medical Q&A and legal doc analysis benefit. General chat rarely justifies it. LoRA fine-tuning cuts costs 10-100x vs full training.

How do I handle sudden traffic spikes?

Auto-scale GPU instances on demand. RunPod's API handles this. Kubernetes and Ray work for complex setups. Budget idle capacity if spike size is hard to predict.

Sources