Qwen 2.5 Math 7B
Alibaba · dense · 7.6B parameters · 4,096 context
Parameters
7.6B
Context Window
4K tokens
Architecture
Dense
Best GPU
A10G
Cheapest API
$0.20/M
Intelligence Brief
Qwen 2.5 Math 7B is a 7.6B parameter DENSE model from Alibaba, featuring Grouped Query Attention (GQA) with 28 layers and 3,584 hidden dimensions. With a 4,096 token context window, it supports structured output, code, math, reasoning. The most cost-effective API deployment is via together at $0.20/M output tokens. For self-hosted inference, A10G delivers optimal throughput at $285/month.
Architecture Details
Memory Requirements
BF16 Weights
15.2 GB
FP8 Weights
7.6 GB
INT4 Weights
3.8 GB
GPU Compatibility Matrix
Qwen 2.5 Math 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
213.1 tok/s
Latency (ITL)
4.7ms
Est. TTFT
1ms
Cost/Month
$285
Cost/M Tokens
$0.51
BF16 · 1 GPU · vllm
100/100
score
Throughput
331.4 tok/s
Latency (ITL)
3.0ms
Est. TTFT
1ms
Cost/Month
$332
Cost/M Tokens
$0.38
BF16 · 1 GPU · vllm
100/100
score
Throughput
358.1 tok/s
Latency (ITL)
2.8ms
Est. TTFT
0ms
Cost/Month
$370
Cost/M Tokens
$0.39
Deployment Options
API Deployment
together
$0.20/M
output tokens
Single GPU
A10G
$285/mo
Min VRAM: 8 GB
Multi-GPU
A4000 x2
248.0 tok/s
TP· $323/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| together | $0.20 | $0.20 | Cheapest |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| togetherBest Value | $0.20 | $0.20 | $2 |
Cost per 1,000 Requests
Short (500 tok)
$0.14
via together
Medium (2K tok)
$0.56
via together
Long (8K tok)
$2.00
via together
Performance Estimates
Throughput by GPU
VRAM Breakdown (A10G, BF16)
Precision Impact
bf16
15.2 GB
weights/GPU
~213.1 tok/s
fp8
7.6 GB
weights/GPU
int4
3.8 GB
weights/GPU
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy Qwen 2.5 Math 7B
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does Qwen 2.5 Math 7B need for inference?
Qwen 2.5 Math 7B requires approximately 15.2 GB of VRAM at BF16 precision, 7.6 GB at FP8, or 3.8 GB at INT4 quantization. Additional VRAM is needed for KV-cache (57344 bytes per token) and activations (~0.80 GB).
What is the best GPU for Qwen 2.5 Math 7B?
The top recommended GPU for Qwen 2.5 Math 7B is the A10G using BF16 precision. It achieves approximately 213.1 tokens/sec at an estimated cost of $285/month ($0.51/M tokens). Score: 100/100.
How much does Qwen 2.5 Math 7B inference cost?
Qwen 2.5 Math 7B API inference starts from $0.20/M input tokens and $0.20/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.