GPT-3.5 Turbo
OpenAI · dense · 20B parameters · 16,384 context
Parameters
20B
Context Window
16K tokens
Architecture
Dense
Best GPU
H100 SXM
Cheapest API
$1.50/M
Quality Score
67/100
Intelligence Brief
GPT-3.5 Turbo is a 20B parameter DENSE model from OpenAI, featuring Grouped Query Attention (GQA) with 40 layers and 5,120 hidden dimensions. With a 16,384 token context window, it supports tools, structured output, code, math, multilingual. On standardized benchmarks, it achieves MMLU 70, HumanEval 48.1, GSM8K 57.1. The most cost-effective API deployment is via openai at $1.50/M output tokens. For self-hosted inference, H100 SXM delivers optimal throughput at $1794/month.
Architecture Details
Memory Requirements
BF16 Weights
40.0 GB
FP8 Weights
20.0 GB
INT4 Weights
10.0 GB
GPU Compatibility Matrix
GPT-3.5 Turbo is compatible with 74% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
BF16 · 1 GPU · tensorrt-llm
100/100
score
Throughput
722.3 tok/s
Latency (ITL)
1.4ms
Est. TTFT
0ms
Cost/Month
$1794
Cost/M Tokens
$0.94
BF16 · 1 GPU · tensorrt-llm
100/100
score
Throughput
431.2 tok/s
Latency (ITL)
2.3ms
Est. TTFT
0ms
Cost/Month
$1794
Cost/M Tokens
$1.58
BF16 · 1 GPU · tensorrt-llm
95/100
score
Throughput
862.5 tok/s
Latency (ITL)
1.2ms
Est. TTFT
0ms
Cost/Month
$940
Cost/M Tokens
$0.41
Deployment Options
API Deployment
openai
$1.50/M
output tokens
Single GPU
H100 SXM
$1794/mo
Min VRAM: 20 GB
Multi-GPU
A100 40GB SXM x2
412.1 tok/s
TP· $1613/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| openai | $0.50 | $1.50 | Cheapest |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| openaiBest Value | $0.50 | $1.50 | $10 |
Cost per 1,000 Requests
Short (500 tok)
$0.55
via openai
Medium (2K tok)
$2.20
via openai
Long (8K tok)
$7.00
via openai
Performance Estimates
Throughput by GPU
VRAM Breakdown (H100 SXM, BF16)
Quality Benchmarks
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy GPT-3.5 Turbo
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does GPT-3.5 Turbo need for inference?
GPT-3.5 Turbo requires approximately 40.0 GB of VRAM at BF16 precision, 20.0 GB at FP8, or 10.0 GB at INT4 quantization. Additional VRAM is needed for KV-cache (40960 bytes per token) and activations (~1.50 GB).
What is the best GPU for GPT-3.5 Turbo?
The top recommended GPU for GPT-3.5 Turbo is the H100 SXM using BF16 precision. It achieves approximately 722.3 tokens/sec at an estimated cost of $1794/month ($0.94/M tokens). Score: 100/100.
How much does GPT-3.5 Turbo inference cost?
GPT-3.5 Turbo API inference starts from $0.50/M input tokens and $1.50/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.