BGE Large EN v1.5
BAAI · dense · 0.335B parameters · 512 context
Parameters
0.335B
Context Window
1K tokens
Architecture
Dense
Best GPU
B200 SXM
Cheapest API
$0.01/M
Intelligence Brief
BGE Large EN v1.5 is a 0.335B parameter DENSE model from BAAI, featuring Multi-Head Attention (MHA) with 24 layers and 1,024 hidden dimensions. With a 512 token context window, it supports general text generation. The most cost-effective API deployment is via together at $0.01/M output tokens. For self-hosted inference, B200 SXM delivers optimal throughput at $4261/month.
Provider pricing
1 provider · canonical: together| Provider | Input $/M | Output $/M ▲ | Notes |
|---|---|---|---|
| togethercanonical | $0.0080 | $0.0080 | cheapest input · cheapest output |
Prices update via the nightly pricing cron + admin approvals at /admin/ingest-queue. The leaderboard's Input/Output cells show the canonical rate above; this table shows the full spread.
Recent changes
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Related models
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NV Embed v2NV Embed · 7.85B$0.012/M out
Picks: same family first, then same vendor within ±2× params, then top tag-overlap matches. Price shown is the cheapest Output $/M across providers — the row's page shows the canonical anchor.
Architecture Details
Memory Requirements
BF16 Weights
0.7 GB
FP8 Weights
0.3 GB
INT4 Weights
0.2 GB
GPU Compatibility Matrix
BGE Large EN v1.5 is compatible with 100% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
FP8 · 1 GPU · tensorrt-llm
83/100
score
Throughput
3.5K tok/s
Latency (ITL)
0.3ms
Est. TTFT
0ms
Cost/Month
$4261
Cost/M Tokens
$0.46
FP8 · 1 GPU · tensorrt-llm
83/100
score
Throughput
3.5K tok/s
Latency (ITL)
0.3ms
Est. TTFT
0ms
Cost/Month
$4271
Cost/M Tokens
$0.46
FP8 · 1 GPU · tensorrt-llm
83/100
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
together
$0.01/M
output tokens
Single GPU
B200 SXM
$4261/mo
Min VRAM: 0 GB
Multi-GPU
B200 SXM
3.5K tok/s
Best available config
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| together | $0.01 | $0.01 | Cheapest |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| togetherBest Value | $0.01 | $0.01 | $0 |
Cost per 1,000 Requests
Short (500 tok)
$0.01
via together
Medium (2K tok)
$0.02
via together
Long (8K tok)
$0.08
via together
Performance Estimates
Throughput by GPU
VRAM Breakdown (B200 SXM, FP8)
Precision Impact
bf16
0.7 GB
weights/GPU
fp8
0.3 GB
weights/GPU
~3.5K tok/s
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy BGE Large EN v1.5
Self-Hosted Infrastructure
Similar Models
BGE Base EN v1.5
0.11B params · dense
Quality: 50
BGE M3
0.568B params · dense
Quality: 50
from $0.01/M
NV EmbedQA E5 v5
0.33B params · dense
Quality: 50
from $0.01/M
NV Retriever v1
0.33B params · dense
Quality: 50
from $0.01/M
SmolLM 360M
0.36B params · dense
Quality: 50
Frequently Asked Questions
How much VRAM does BGE Large EN v1.5 need for inference?
BGE Large EN v1.5 requires approximately 0.7 GB of VRAM at BF16 precision, 0.3 GB at FP8, or 0.2 GB at INT4 quantization. Additional VRAM is needed for KV-cache (98304 bytes per token) and activations (~0.10 GB).
What is the best GPU for BGE Large EN v1.5?
The top recommended GPU for BGE Large EN v1.5 is the B200 SXM using FP8 precision. It achieves approximately 3.5K tokens/sec at an estimated cost of $4261/month ($0.46/M tokens). Score: 83/100.
How much does BGE Large EN v1.5 inference cost?
BGE Large EN v1.5 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.