multilingual-e5-large-instruct
intfloat · dense · 0.6B parameters · 514 context
Parameters
0.6B
Context Window
1K tokens
Architecture
Dense
Best GPU
B200 SXM
Intelligence Brief
multilingual-e5-large-instruct is a 0.6B parameter DENSE model from intfloat, featuring Multi-Head Attention (MHA) with 24 layers and 1,024 hidden dimensions. With a 514 token context window, it supports general text generation. For self-hosted inference, B200 SXM delivers optimal throughput at $4261/month.
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Architecture Details
Memory Requirements
BF16 Weights
1.2 GB
FP8 Weights
0.6 GB
INT4 Weights
0.3 GB
GPU Compatibility Matrix
multilingual-e5-large-instruct is compatible with 100% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
BF16 · 1 GPU · tensorrt-llm
78/100
score
Throughput
3.5K tok/s
Latency (ITL)
0.3ms
Est. TTFT
0ms
Cost/Month
$4261
Cost/M Tokens
$0.46
BF16 · 1 GPU · tensorrt-llm
78/100
score
Throughput
3.5K tok/s
Latency (ITL)
0.3ms
Est. TTFT
0ms
Cost/Month
$4271
Cost/M Tokens
$0.46
BF16 · 1 GPU · tensorrt-llm
78/100
score
Throughput
3.5K tok/s
Latency (ITL)
0.3ms
Est. TTFT
0ms
Cost/Month
$6169
Cost/M Tokens
$0.67
Deployment Options
API Deployment
No API pricing available
Single GPU
B200 SXM
$4261/mo
Min VRAM: 1 GB
Multi-GPU
B200 SXM
3.5K tok/s
Best available config
API Pricing Comparison
No API pricing data available for this model.
Performance Estimates
Throughput by GPU
VRAM Breakdown (B200 SXM, BF16)
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy multilingual-e5-large-instruct
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does multilingual-e5-large-instruct need for inference?
multilingual-e5-large-instruct requires approximately 1.2 GB of VRAM at BF16 precision, 0.6 GB at FP8, or 0.3 GB at INT4 quantization. Additional VRAM is needed for KV-cache (98304 bytes per token) and activations (~0.00 GB).
What is the best GPU for multilingual-e5-large-instruct?
The top recommended GPU for multilingual-e5-large-instruct is the B200 SXM using BF16 precision. It achieves approximately 3.5K tokens/sec at an estimated cost of $4261/month ($0.46/M tokens). Score: 78/100.
How much does multilingual-e5-large-instruct inference cost?
multilingual-e5-large-instruct inference costs vary by provider and GPU setup. Use our calculator for detailed cost estimates across all providers.