Llama 3.1 Nemotron 51B
NVIDIA · dense · 51B parameters · 131,072 context
Parameters
51B
Context Window
128K tokens
Architecture
Dense
Best GPU
H100 SXM
Cheapest API
$0.40/M
Quality Score
78/100
Intelligence Brief
Llama 3.1 Nemotron 51B is a 51B parameter DENSE model from NVIDIA, featuring Grouped Query Attention (GQA) with 64 layers and 8,192 hidden dimensions. With a 131,072 token context window, it supports tools, structured output, code, math, multilingual, reasoning. On standardized benchmarks, it achieves MMLU 78, HumanEval 50, GSM8K 86. The most cost-effective API deployment is via nvidia-nim at $0.40/M output tokens. For self-hosted inference, H100 SXM delivers optimal throughput at $1794/month.
Architecture Details
Memory Requirements
BF16 Weights
102.0 GB
FP8 Weights
51.0 GB
INT4 Weights
25.5 GB
GPU Compatibility Matrix
Llama 3.1 Nemotron 51B is compatible with 40% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
549.5 tok/s
Latency (ITL)
1.8ms
Est. TTFT
0ms
Cost/Month
$1794
Cost/M Tokens
$1.24
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
328.1 tok/s
Latency (ITL)
3.0ms
Est. TTFT
1ms
Cost/Month
$1794
Cost/M Tokens
$2.08
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
560.0 tok/s
Latency (ITL)
1.8ms
Est. TTFT
0ms
Cost/Month
$2932
Cost/M Tokens
$1.99
Deployment Options
API Deployment
nvidia-nim
$0.40/M
output tokens
Single GPU
H100 SXM
$1794/mo
Min VRAM: 51 GB
Multi-GPU
A100 80GB SXM x2
213.4 tok/s
TP· $2259/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| nvidia-nim | $0.40 | $0.40 | Cheapest |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| nvidia-nimBest Value | $0.40 | $0.40 | $4 |
Cost per 1,000 Requests
Short (500 tok)
$0.28
via nvidia-nim
Medium (2K tok)
$1.12
via nvidia-nim
Long (8K tok)
$4.00
via nvidia-nim
Performance Estimates
Throughput by GPU
VRAM Breakdown (H100 SXM, FP8)
Precision Impact
bf16
102.0 GB
weights/GPU
fp8
51.0 GB
weights/GPU
~549.5 tok/s
int4
25.5 GB
weights/GPU
Quality Benchmarks
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy Llama 3.1 Nemotron 51B
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does Llama 3.1 Nemotron 51B need for inference?
Llama 3.1 Nemotron 51B requires approximately 102.0 GB of VRAM at BF16 precision, 51.0 GB at FP8, or 25.5 GB at INT4 quantization. Additional VRAM is needed for KV-cache (262144 bytes per token) and activations (~2.00 GB).
What is the best GPU for Llama 3.1 Nemotron 51B?
The top recommended GPU for Llama 3.1 Nemotron 51B is the H100 SXM using FP8 precision. It achieves approximately 549.5 tokens/sec at an estimated cost of $1794/month ($1.24/M tokens). Score: 100/100.
How much does Llama 3.1 Nemotron 51B inference cost?
Llama 3.1 Nemotron 51B API inference starts from $0.40/M input tokens and $0.40/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.