Qwen3-8B-Base
Q4 GGUF download: mradermacher/Co-rewarding-I-Qwen3-8B-Base-MATH-i1-GGUF - 5.03 GB on disk, read 2026-10-10. VRAM figures above are estimates; this is the actual file size.
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
7.3
AA Index
Coding
9.0
AA Index
Agentic
0.8
AA Index
Intelligence Index - Qwen3-8B-Base vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Coding Index comparison
Agentic Index comparison
Benchmark data from Artificial Analysis · updated 2026-10-10.
Hosted API cost & speed
What it costs to rent this model instead of running it locally
| Metric | Value |
|---|---|
| Input (per 1M tokens) | $0.18 |
| Output (per 1M tokens) | $2.10 |
| Median speed (hosted API) | 41 tok/s |
Median across hosted providers, measured by Artificial Analysis - updated 2026-10-10. Local llama.cpp speed depends on your hardware — see the hardware pages for estimates.
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve Qwen/Qwen3-8B-BaseNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
Frequently asked
Quick answers to common questions
How much VRAM does Qwen3-8B-Base need?
Qwen3-8B-Base with 8.2B parameters needs approximately 5 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is Qwen3-8B-Base better than other Qwen models?
Qwen3-8B-Base has 8.2B parameters with 32,768 context - a strong choice for general use.
What license is Qwen3-8B-Base under?
Qwen3-8B-Base is released under the apache-2.0 license, making it suitable for most commercial and personal projects.
What hardware runs Qwen3-8B-Base well?
With 8.2B parameters, Qwen3-8B-Base requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for Qwen3-8B-Base?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~6 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
How long can Qwen3-8B-Base's context window handle?
Qwen3-8B-Base supports a 32,768-token context window - enough for most medium-length documents and conversations. Real-world usable context may vary by implementation.
What models compete with Qwen3-8B-Base?
Qwen3-8B-Base competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec