Nemotron 15B
NVIDIA · dense · 15B parameters · 4,096 context
Parameters
15B
Context Window
4K tokens
Architecture
Dense
Best GPU
H100 SXM
Cheapest API
$0.30/M
Quality Score
72/100
Intelligence Brief
Nemotron 15B is a 15B parameter DENSE model from NVIDIA, featuring Grouped Query Attention (GQA) with 32 layers and 6,144 hidden dimensions. With a 4,096 token context window, it supports tools, structured output, code, math, multilingual. On standardized benchmarks, it achieves MMLU 71, HumanEval 40, GSM8K 75. The most cost-effective API deployment is via nvidia at $0.30/M output tokens. For self-hosted inference, H100 SXM delivers optimal throughput at $1794/month.
Architecture Details
Memory Requirements
BF16 Weights
30.0 GB
FP8 Weights
15.0 GB
INT4 Weights
7.5 GB
GPU Compatibility Matrix
Nemotron 15B is compatible with 82% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
1.1K tok/s
Latency (ITL)
1.0ms
Est. TTFT
0ms
Cost/Month
$1794
Cost/M Tokens
$0.65
FP8 · 1 GPU · tensorrt-llm
95/100
score
Throughput
1.1K tok/s
Latency (ITL)
1.0ms
Est. TTFT
0ms
Cost/Month
$1794
Cost/M Tokens
$0.65
BF16 · 1 GPU · vllm
95/100
score
Throughput
138.2 tok/s
Latency (ITL)
7.2ms
Est. TTFT
1ms
Cost/Month
$465
Cost/M Tokens
$1.28
Deployment Options
API Deployment
nvidia
$0.30/M
output tokens
Single GPU
H100 SXM
$1794/mo
Min VRAM: 15 GB
Multi-GPU
RTX 3090 x2
280.4 tok/s
TP· $361/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| nvidia | $0.30 | $0.30 | Cheapest |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| nvidiaBest Value | $0.30 | $0.30 | $3 |
Cost per 1,000 Requests
Short (500 tok)
$0.21
via nvidia
Medium (2K tok)
$0.84
via nvidia
Long (8K tok)
$3.00
via nvidia
Performance Estimates
Throughput by GPU
VRAM Breakdown (H100 SXM, FP8)
Precision Impact
bf16
30.0 GB
weights/GPU
fp8
15.0 GB
weights/GPU
~1.1K tok/s
int4
7.5 GB
weights/GPU
Quality Benchmarks
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy Nemotron 15B
Self-Hosted Infrastructure
Similar Models
Minitron 8B
8B params · dense
Quality: 62
from $0.10/M
Qwen 2.5 14B
14.8B params · dense
Quality: 76
from $0.30/M
DeepSeek R1 Distill 14B
14.8B params · dense
Quality: 88
from $0.30/M
Phi-4
14.7B params · dense
Quality: 73
from $0.14/M
Qwen 2.5 Coder 14B
14.7B params · dense
Quality: 50
from $0.30/M
Frequently Asked Questions
How much VRAM does Nemotron 15B need for inference?
Nemotron 15B requires approximately 30.0 GB of VRAM at BF16 precision, 15.0 GB at FP8, or 7.5 GB at INT4 quantization. Additional VRAM is needed for KV-cache (131072 bytes per token) and activations (~1.50 GB).
What is the best GPU for Nemotron 15B?
The top recommended GPU for Nemotron 15B is the H100 SXM using FP8 precision. It achieves approximately 1.1K tokens/sec at an estimated cost of $1794/month ($0.65/M tokens). Score: 100/100.
How much does Nemotron 15B inference cost?
Nemotron 15B API inference starts from $0.30/M input tokens and $0.30/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.