InternLM3 8B
Shanghai AI Lab · dense · 8B parameters · 32,768 context
InternLM3 8B is a 8B parameter DENSE model from Shanghai AI Lab, featuring a 32,768 token context window. With 32 transformer layers and a hidden dimension of 4,096, it delivers efficient Grouped Query Attention (GQA) for optimized inference throughput. Based on InferenceBench analysis, the optimal deployment configuration is the A30 at BF16 precision, achieving approximately 314.9 tokens/second at $0.40/million tokens.
Architecture Details
Memory Requirements
BF16 Weights
16.0 GB
FP8 Weights
8.0 GB
INT4 Weights
4.0 GB
Fits on (single-node)
GPU Recommendations
BF16 · 1 GPU · vllm
100/100
score
Throughput
314.9 tok/s
Cost/Month
$332
Cost/M Tokens
$0.40
BF16 · 1 GPU · vllm
100/100
score
Throughput
340.2 tok/s
Cost/Month
$370
Cost/M Tokens
$0.41
BF16 · 1 GPU · vllm
100/100
score
Throughput
315.9 tok/s
Cost/Month
$180
Cost/M Tokens
$0.22
API Pricing Comparison
No API pricing data available for this model.
Capabilities
Features
Supported Frameworks
Supported Precisions
Similar Models
Frequently Asked Questions
How much VRAM does InternLM3 8B need for inference?
InternLM3 8B requires approximately 16.0 GB of VRAM at BF16 precision, 8.0 GB at FP8, or 4.0 GB at INT4 quantization. Additional VRAM is needed for KV-cache (65536 bytes per token) and activations (~0.50 GB).
What is the best GPU for InternLM3 8B?
The top recommended GPU for InternLM3 8B is the A30 using BF16 precision. It achieves approximately 314.9 tokens/sec at an estimated cost of $332/month ($0.40/M tokens). Score: 100/100.
How much does InternLM3 8B inference cost?
InternLM3 8B inference costs vary by provider and GPU setup. Use our calculator for detailed cost estimates across all providers.