Llama 3.1 Nemotron 70B Reward
NVIDIA · dense · 70.6B parameters · 131,072 context
Parameters
70.6B
Context Window
128K tokens
Architecture
Dense
Best GPU
H200 SXM
Cheapest API
$0.50/M
Quality Score
80/100
Intelligence Brief
Llama 3.1 Nemotron 70B Reward is a 70.6B parameter DENSE model from NVIDIA, featuring Grouped Query Attention (GQA) with 80 layers and 8,192 hidden dimensions. With a 131,072 token context window, it supports code, math, multilingual, reasoning. On standardized benchmarks, it achieves MMLU 80, HumanEval 52, GSM8K 88. The most cost-effective API deployment is via nvidia-nim at $0.50/M output tokens. For self-hosted inference, H200 SXM delivers optimal throughput at $2553/month.
Architecture Details
Memory Requirements
BF16 Weights
141.2 GB
FP8 Weights
70.6 GB
INT4 Weights
35.3 GB
GPU Compatibility Matrix
Llama 3.1 Nemotron 70B Reward is compatible with 37% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
560.0 tok/s
Latency (ITL)
1.8ms
Est. TTFT
0ms
Cost/Month
$2553
Cost/M Tokens
$1.73
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
474.0 tok/s
Latency (ITL)
2.1ms
Est. TTFT
0ms
Cost/Month
$940
Cost/M Tokens
$0.75
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
474.0 tok/s
Latency (ITL)
2.1ms
Est. TTFT
0ms
Cost/Month
$2838
Cost/M Tokens
$2.28
Deployment Options
API Deployment
nvidia-nim
$0.50/M
output tokens
Single GPU
H200 SXM
$2553/mo
Min VRAM: 71 GB
Multi-GPU
H100 SXM x2
560.0 tok/s
TP· $3587/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| nvidia-nim | $0.50 | $0.50 | Cheapest |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| nvidia-nimBest Value | $0.50 | $0.50 | $5 |
Cost per 1,000 Requests
Short (500 tok)
$0.35
via nvidia-nim
Medium (2K tok)
$1.40
via nvidia-nim
Long (8K tok)
$5.00
via nvidia-nim
Performance Estimates
Throughput by GPU
VRAM Breakdown (H200 SXM, FP8)
Precision Impact
bf16
141.2 GB
weights/GPU
fp8
70.6 GB
weights/GPU
~560.0 tok/s
int4
35.3 GB
weights/GPU
Quality Benchmarks
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy Llama 3.1 Nemotron 70B Reward
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does Llama 3.1 Nemotron 70B Reward need for inference?
Llama 3.1 Nemotron 70B Reward requires approximately 141.2 GB of VRAM at BF16 precision, 70.6 GB at FP8, or 35.3 GB at INT4 quantization. Additional VRAM is needed for KV-cache (327680 bytes per token) and activations (~2.50 GB).
What is the best GPU for Llama 3.1 Nemotron 70B Reward?
The top recommended GPU for Llama 3.1 Nemotron 70B Reward is the H200 SXM using FP8 precision. It achieves approximately 560.0 tokens/sec at an estimated cost of $2553/month ($1.73/M tokens). Score: 100/100.
How much does Llama 3.1 Nemotron 70B Reward inference cost?
Llama 3.1 Nemotron 70B Reward API inference starts from $0.50/M input tokens and $0.50/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.