NV EmbedQA Mistral 7B
NVIDIA · dense · 7.24B parameters · 32,768 context
Parameters
7.24B
Context Window
32K tokens
Architecture
Dense
Best GPU
A10G
Cheapest API
$0.01/M
Intelligence Brief
NV EmbedQA Mistral 7B is a 7.24B parameter DENSE model from NVIDIA, featuring Grouped Query Attention (GQA) with 32 layers and 4,096 hidden dimensions. With a 32,768 token context window, it supports multilingual. The most cost-effective API deployment is via nvidia-nim at $0.01/M output tokens. For self-hosted inference, A10G delivers optimal throughput at $285/month.
Architecture Details
Memory Requirements
BF16 Weights
14.5 GB
FP8 Weights
7.2 GB
INT4 Weights
3.6 GB
GPU Compatibility Matrix
NV EmbedQA Mistral 7B is compatible with 95% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
BF16 · 1 GPU · vllm
100/100
score
Throughput
223.7 tok/s
Latency (ITL)
4.5ms
Est. TTFT
1ms
Cost/Month
$285
Cost/M Tokens
$0.48
BF16 · 1 GPU · vllm
100/100
score
Throughput
347.9 tok/s
Latency (ITL)
2.9ms
Est. TTFT
0ms
Cost/Month
$332
Cost/M Tokens
$0.36
BF16 · 1 GPU · vllm
100/100
score
Throughput
375.9 tok/s
Latency (ITL)
2.7ms
Est. TTFT
0ms
Cost/Month
$370
Cost/M Tokens
$0.37
Deployment Options
API Deployment
nvidia-nim
$0.01/M
output tokens
Single GPU
A10G
$285/mo
Min VRAM: 7 GB
Multi-GPU
RTX 3080 x2
439.3 tok/s
TP· $266/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| nvidia-nim | $0.01 | $0.01 | Cheapest |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| nvidia-nimBest Value | $0.01 | $0.01 | $0 |
Cost per 1,000 Requests
Short (500 tok)
$0.01
via nvidia-nim
Medium (2K tok)
$0.03
via nvidia-nim
Long (8K tok)
$0.12
via nvidia-nim
Performance Estimates
Throughput by GPU
VRAM Breakdown (A10G, BF16)
Precision Impact
bf16
14.5 GB
weights/GPU
~223.7 tok/s
fp8
7.2 GB
weights/GPU
int4
3.6 GB
weights/GPU
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy NV EmbedQA Mistral 7B
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does NV EmbedQA Mistral 7B need for inference?
NV EmbedQA Mistral 7B requires approximately 14.5 GB of VRAM at BF16 precision, 7.2 GB at FP8, or 3.6 GB at INT4 quantization. Additional VRAM is needed for KV-cache (65536 bytes per token) and activations (~0.70 GB).
What is the best GPU for NV EmbedQA Mistral 7B?
The top recommended GPU for NV EmbedQA Mistral 7B is the A10G using BF16 precision. It achieves approximately 223.7 tokens/sec at an estimated cost of $285/month ($0.48/M tokens). Score: 100/100.
How much does NV EmbedQA Mistral 7B inference cost?
NV EmbedQA Mistral 7B API inference starts from $0.01/M input tokens and $0.01/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.