Qwen 3 32B
Alibaba · dense · 32.8B parameters · 131,072 context
Parameters
32.8B
Context Window
128K tokens
Architecture
Dense
Best GPU
H20
Cheapest API
$0.00/M
Quality Score
74/100
Intelligence Brief
Qwen 3 32B is a 32.8B parameter DENSE model from Alibaba, featuring Grouped Query Attention (GQA) with 64 layers and 5,120 hidden dimensions. With a 131,072 token context window, it supports tools, structured output, code, math, multilingual, reasoning. On standardized benchmarks, it achieves MMLU 82, HumanEval 72, GSM8K 85. The most cost-effective API deployment is via featherless at $0.00/M output tokens. For self-hosted inference, H20 delivers optimal throughput at $940/month.
Provider pricing
7 providers · canonical: together| Provider | Input $/M | Output $/M ▲ | Notes |
|---|---|---|---|
| featherless | free | free | cheapest input · cheapest output |
| ovhcloud | $0.090 | $0.250 | — |
| openrouter | $0.080 | $0.280 | — |
| novita | $0.100 | $0.450 | — |
| groq | $0.290 | $0.590 | — |
| togethercanonical | $0.800 | $0.800 | — |
| fireworks | $0.900 | $0.900 | — |
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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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
65.6 GB
FP8 Weights
32.8 GB
INT4 Weights
16.4 GB
GPU Compatibility Matrix
Qwen 3 32B is compatible with 57% of GPU configurations across 41 GPUs at 3 precision levels.
GPU Recommendations
FP8 · 1 GPU · tensorrt-llm
100/100
score
Throughput
1.0K tok/s
Latency (ITL)
1.0ms
Est. TTFT
0ms
Cost/Month
$940
Cost/M Tokens
$0.35
FP8 · 1 GPU · tensorrt-llm
95/100
score
Throughput
1.1K tok/s
Latency (ITL)
1.0ms
Est. TTFT
0ms
Cost/Month
$2553
Cost/M Tokens
$0.93
FP8 · 1 GPU · tensorrt-llm
95/100
score
Throughput
854.4 tok/s
Latency (ITL)
1.2ms
Est. TTFT
0ms
Cost/Month
$1794
Cost/M Tokens
$0.80
Deployment Options
API Deployment
featherless
$0.00/M
output tokens
Single GPU
H20
$940/mo
Min VRAM: 33 GB
Multi-GPU
RTX A6000 x2
111.3 tok/s
TP· $930/mo
API Pricing Comparison
| Provider | Input $/M | Output $/M | Badges |
|---|---|---|---|
| featherless | $0.00 | $0.00 | Cheapest |
| ovhcloud | $0.09 | $0.25 | |
| openrouter | $0.08 | $0.28 | |
| novita | $0.10 | $0.45 | |
| groq | $0.29 | $0.59 | |
| together | $0.80 | $0.80 | |
| fireworks | $0.90 | $0.90 |
Cost Analysis
| Provider | Input $/M | Output $/M | ~Monthly Cost |
|---|---|---|---|
| featherlessBest Value | $0.00 | $0.00 | $0 |
| ovhcloud | $0.09 | $0.25 | $2 |
| openrouter | $0.08 | $0.28 | $2 |
| novita | $0.10 | $0.45 | $3 |
| groq | $0.29 | $0.59 | $4 |
| together | $0.80 | $0.80 | $8 |
| fireworks | $0.90 | $0.90 | $9 |
Cost per 1,000 Requests
Short (500 tok)
$0.00
via featherless
Medium (2K tok)
$0.00
via featherless
Long (8K tok)
$0.00
via featherless
Performance Estimates
Throughput by GPU
VRAM Breakdown (H20, FP8)
Precision Impact
bf16
65.6 GB
weights/GPU
fp8
32.8 GB
weights/GPU
~1.0K tok/s
int4
16.4 GB
weights/GPU
Quality Benchmarks
Capabilities
Features
Supported Frameworks
Supported Precisions
Where to Deploy Qwen 3 32B
Self-Hosted Infrastructure
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Frequently Asked Questions
How much VRAM does Qwen 3 32B need for inference?
Qwen 3 32B requires approximately 65.6 GB of VRAM at BF16 precision, 32.8 GB at FP8, or 16.4 GB at INT4 quantization. Additional VRAM is needed for KV-cache (262144 bytes per token) and activations (~2.00 GB).
What is the best GPU for Qwen 3 32B?
The top recommended GPU for Qwen 3 32B is the H20 using FP8 precision. It achieves approximately 1.0K tokens/sec at an estimated cost of $940/month ($0.35/M tokens). Score: 100/100.
How much does Qwen 3 32B inference cost?
Qwen 3 32B API inference starts from $0.00/M input tokens and $0.00/M output tokens. Self-hosted inference costs depend on your GPU configuration — use our ROI calculator for a detailed breakdown.