Qwen 2.5 0.5B
Alibaba · dense · 0.5B parameters · 32,768 context
Parameters
0.5B
Context Window
32K tokens
Architecture
Dense
Best GPU
B200 SXM
Intelligence Brief
Qwen 2.5 0.5B is a 0.5B parameter DENSE model from Alibaba, featuring Grouped Query Attention (GQA) with 24 layers and 896 hidden dimensions. With a 32,768 token context window, it supports structured output, code, math, multilingual. For self-hosted inference, B200 SXM delivers optimal throughput at $4261/month.
Recent changes
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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
1.0 GB
FP8 Weights
0.5 GB
INT4 Weights
0.3 GB
GPU Compatibility Matrix
Qwen 2.5 0.5B 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
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, FP8)
Precision Impact
bf16
1.0 GB
weights/GPU
fp8
0.5 GB
weights/GPU
~3.5K tok/s
int4
0.3 GB
weights/GPU
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy Qwen 2.5 0.5B
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does Qwen 2.5 0.5B need for inference?
Qwen 2.5 0.5B requires approximately 1.0 GB of VRAM at BF16 precision, 0.5 GB at FP8, or 0.3 GB at INT4 quantization. Additional VRAM is needed for KV-cache (12288 bytes per token) and activations (~0.20 GB).
What is the best GPU for Qwen 2.5 0.5B?
The top recommended GPU for Qwen 2.5 0.5B 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 Qwen 2.5 0.5B inference cost?
Qwen 2.5 0.5B inference costs vary by provider and GPU setup. Use our calculator for detailed cost estimates across all providers.