I'm building an AI app and need help sizing the GPU layer

Absolutely — I can help size the GPU layer, but I’ll need a bit more detail to do it accurately.

What I need from you

Please share as many of these as you can:

1) Workload type

• LLM inference (chat, RAG, agents)
• LLM fine-tuning
• Embedding generation
• Image generation / vision
• Training
• Mixed workload

2) Model details

• Model name/version (e.g. Llama 3 8B, GPT-style custom model)
• Parameter size
• Precision/quantization:
  • FP16 / BF16
  • INT8
  • INT4 / 4-bit
• Context length / sequence length
• Batch size
• Concurrency target

3) Performance target

• Requests per second, tokens/sec, or latency target
• Peak vs average traffic
• Number of users / sessions
• SLA requirements

4) Deployment environment

• Cloud or on-prem
• Preferred vendor: AWS / GCP / Azure / other
• GPU instance types you’re considering
• Whether you need multi-GPU or a single-GPU setup

5) Memory and serving requirements

• Max prompt size
• Max output tokens
• KV cache needs
• Whether you’ll use vLLM, TensorRT-LLM, TGI, llama.cpp, Triton, etc.

6) Business constraints

• Budget per month
• Cost optimization priority vs performance
• Need for autoscaling / burst capacity

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If you want a quick first-pass estimate

You can paste something like this:

Workload: LLM inference
Model: Llama 3 8B, 4-bit
Context: 8k
Concurrency: 20 users
Target latency: <2s time-to-first-token, <10s total
Traffic: 50 req/min average, 200 req/min peak
Cloud: AWS
Serving stack: vLLM
Budget: $X/month

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What I can give you

Once you send the details, I can help estimate:

• GPU type and count
• VRAM requirements
• Throughput capacity
• Latency expectations
• Instance sizing
• Monthly cost
• Scaling strategy
• Safety margin for spikes and failover

If you want, I can also give you a simple sizing template you can fill in quickly.