Model & Hardware Setup
Model Parameters
70B
Quantization Precision
FP8 (8-bit)
Target GPU Architecture
GPU Allocation Count
2 GPUs
Context Length (Tokens)
8,192
Concurrent User Streams
16
Avg Output Length (Tokens)
512
Hardware Requirement & Execution Telemetry
Self-Hosted Simulation
Total VRAM Required
82.58 GB
Weights + KV Cache + CUDA Overhead
KV Cache Footprint
12.58 GB
16 streams @ 8k context
Min GPUs Required
2
NVIDIA H100 SXM 80GB
Time-Per-Output Token
14.20 ms
~70.4 tokens/sec/user
Cost per 1M Tokens
$0.42
Self-Hosted TCO estimate
VRAM Memory Allocation Breakdown (GB)
Latency Bottleneck (TTFT vs TPOT)
Self-Hosted TCO vs. Commercial Closed API Pricing
| Deployment Route | Hardware / API | Cost / 1M Input Tokens | Cost / 1M Output Tokens | Estimated Blended Cost (1M) |
|---|---|---|---|---|
| Self-Hosted | 2x NVIDIA H100 SXM | $0.14 | $0.70 | $0.42 |
| Closed API | GPT-4o (Commercial API) | $2.50 | $10.00 | $3.75 |
| Closed API | Claude 3.5 Sonnet | $3.00 | $15.00 | $4.50 |
Generated Execution Flags (vLLM / TensorRT-LLM)
# Auto-generated vLLM production startup command
python3 -m vllm.entrypoints.openai.api_server \
--model meta-llama/Meta-Llama-3.1-70B-Instruct \
--tensor-parallel-size 2 \
--quantization fp8 \
--max-model-len 8192 \
--gpu-memory-utilization 0.90 \
--max-num-seqs 16