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Qwen3 30B A3B Thinking 2507: Self-Hosting & Deployment Guide

Qwen3 30B A3B Thinking 2507 brings Alibaba conversational intelligence to LLM.API so teams can ship assistants and agents without juggling multiple vendor SDKs.

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What is Qwen3 30B A3B Thinking 2507?

Qwen3 30B A3B Thinking 2507 is an Alibaba chat model exposed on LLM.API under id `qwen3-30b-a3b-thinking-2507`. qwen3-30b-a3b-thinking-2507 provided by nebius. It is wired for API access through LLM.API with OpenAI-compatible patterns. Teams use it when they need reliable text generation with text inputs and text outputs over an OpenAI-compatible API.

How to run Qwen3 30B A3B Thinking 2507 yourself

Open weights (apache-2.0), 30.5B parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.

Qwen3 30B A3B Thinking 2507 VRAM requirements

Precision / quantizationWeightsFigureSource
BF16 (full precision)61.1 GBEstimate30.5B params × 2 bytes
FP831.2 GBFile sizeQwen/Qwen3-30B-A3B-Thinking-2507-FP8
AWQ 4-bit18.1 GBFile sizecyankiwi/Qwen3-30B-A3B-Thinking-2507-AWQ-4bit
MLX 4-bit17.2 GBFile sizelmstudio-community/Qwen3-30B-A3B-Thinking-2507-MLX-4bit
GGUF Q8_032.5 GBFile sizeunsloth/Qwen3-30B-A3B-Thinking-2507-GGUF
GGUF Q4_K_M18.6 GBFile sizeunsloth/Qwen3-30B-A3B-Thinking-2507-GGUF
GGUF Q2_K11.3 GBFile sizeunsloth/Qwen3-30B-A3B-Thinking-2507-GGUF

File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.

GPU requirements: deploy Qwen3 30B A3B Thinking 2507 on your hardware

HardwareBF16 (full precision)FP8AWQ 4-bitMLX 4-bitGGUF Q8_0GGUF Q4_K_MGGUF Q2_K
NVIDIA H200141 GB1×1×1×1×1×1×1×
NVIDIA H10080 GB1×1×1×1×1×1×1×
NVIDIA A100 80GB80 GB1×1×1×1×1×1×1×
NVIDIA L40S48 GB2×1×1×1×1×1×1×

GPUs needed per variant.

HardwareGGUF Q8_0GGUF Q4_K_MGGUF Q2_K
RTX 509032 GB2×1×1×
RTX 409024 GB2×1×1×
RTX 309024 GB2×1×1×

GGUF via llama.cpp; more cards or CPU offload for larger files.

HardwareGGUF Q8_0GGUF Q4_K_MGGUF Q2_KMLX 4-bit
Mac, 512 GBM3 UltraFitsFitsFitsFits
Mac, 192 GBM2 UltraFitsFitsFitsFits
Mac, 128 GBM4 Max / M3 MaxFitsFitsFitsFits
Mac, 64 GBM4 Pro / MaxFitsFitsFitsFits

Assumes ~75% of unified memory is usable by the GPU.

HardwareBF16 (full precision)FP8AWQ 4-bitMLX 4-bitGGUF Q8_0GGUF Q4_K_MGGUF Q2_K
AWS p5.48xlarge8× H100 · 640 GBFitsFitsFitsFitsFitsFitsFits
AWS p4de.24xlarge8× A100 80GB · 640 GBFitsFitsFitsFitsFitsFitsFits
AWS g6e.12xlarge4× L40S · 192 GBFitsFitsFitsFitsFitsFitsFits
Google Cloud a3-highgpu-8g8× H100 · 640 GBFitsFitsFitsFitsFitsFitsFits
Azure ND H100 v58× H100 · 640 GBFitsFitsFitsFitsFitsFitsFits

Whole-instance GPU memory; public instance specs.

Serve it yourself

pip install -U vllm
vllm serve Qwen/Qwen3-30B-A3B-Thinking-2507
pip install 'sglang[all]'
python -m sglang.launch_server --model-path Qwen/Qwen3-30B-A3B-Thinking-2507
llama-server -hf unsloth/Qwen3-30B-A3B-Thinking-2507-GGUF:Q4_K_M \
  --ctx-size 32768 --n-gpu-layers 99
pip install -U mlx-lm
mlx_lm.generate --model lmstudio-community/Qwen3-30B-A3B-Thinking-2507-MLX-4bit --prompt "Hello"

Commands use the official repositories above; vLLM and SGLang expose an OpenAI-compatible endpoint.

Not on LLM.API yet

We do not route this model through our API at the moment. Browse the models you can call today — most workloads have a close match already live.

Browse available models

Providers

LLM.API routes Qwen3 30B A3B Thinking 2507 to the providers below, with discounted effective rates versus list price.

List price by provider

InputOutput
nebius$0.1 in
$0.3 out

Provider list prices; the LLM.API discount applies on top.

ProviderPricingContextCapabilities
nebius30% offin $0.1; out $0.3 per 1M tokens262K tokenstools, streaming, reasoning

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 27 Sept 2026.

Try this model

Test Qwen3 30B A3B Thinking 2507 right here — free to start.

Qwen3 30B A3B Thinking 2507
Hi! Want to test the model?

Suggestions for your first prompt

Not available on LLM API yet

We do not route this model through our API at the moment, so there is no endpoint or code snippet for it yet. Browse the models you can call today — most workloads have a close match already live.

Browse available models

Qwen3 30B A3B Thinking 2507 in production

What the model is good at.

  • Multilingual drafting

    Drafts and translates professional content across major business languages.

  • Tool-ready dialogue

    Works well in agent loops that call functions, browsers, or retrieval APIs.

  • Conversational UX

    Maintains coherent multi-turn assistant behavior for product chat surfaces. Relevant for `qwen3-30b-a3b-thinking-2507` workloads on LLM.API.

  • Long-context synthesis

    Summarizes and cross-references information across large prompts when context allows. Grounded in the model's chat role rather than generic chat claims.

  • Analytical writing

    Produces clear analyses, comparisons, and decision memos from messy source material. Relevant for `qwen3-30b-a3b-thinking-2507` workloads on LLM.API.

What you get when you call Qwen3 30B A3B Thinking 2507 through LLM.API.

  • Unified AI Routing

    Production: Reach Qwen3 30B A3B Thinking 2507 and sibling models through one OpenAI-compatible endpoint.

    One endpoint, every model.

  • Cost Control

    Compare provider price points and keep spend visible as you scale Qwen3 30B A3B Thinking 2507.

    More performance, less spend.

  • Reliability Layer

    Retry and route across configured providers when a single upstream blips.

    Never go dark on users.

  • Observability

    Trace prompts, tokens, and errors for Qwen3 30B A3B Thinking 2507 alongside the rest of your stack.

    See every token, everywhere.

  • Drop-in SDKs

    Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.

    Think tasks, not models.

  • Model Breadth

    Swap Qwen3 30B A3B Thinking 2507 for chat, media, or embedding alternatives without rewriting auth.

    Scale from 1 to millions.

When to Use — When NOT to Use

Use it if...

  • You need provider failover options exposed for this model id (Qwen3 30B A3B Thinking 2507)
  • You want OpenAI-compatible chat completions through a single LLM.API key (Qwen3 30B A3B Thinking 2507)
  • You need a general-purpose text model for assistants, agents, or content workflows (Qwen3 30B A3B Thinking 2507)
  • Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (Qwen3 30B A3B Thinking 2507)

Avoid if...

  • Your use case depends on unpublished proprietary benchmarks not listed here
  • You need guaranteed real-time hard latency SLAs without benchmarking the provider
  • You require on-prem only deployment with no cloud inference
  • You need pure embedding, OCR, or media generation instead of chat

LLM API rating for Qwen3 30B A3B Thinking 2507

★ 3.9/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value✓ Verified benchmark · LMArena
4.2Production adoptionStrong open-weight adoption
3.2Benchmark qualityLMArena text rank #163, Elo 1384
4.2Developer ecosystemContext window, tool and JSON support, price against the category median
Reasoning & chat 3.9Coding 4.0Tool & function calling 4.4Long context 4.1
How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

  1. Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
  2. Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
  3. Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

COMMUNITY

What developers say about Qwen models

LMArena#163 · Elo 1384Arena coding#146 · Elo 1416Speed—Sources: LMArena, Artificial Analysis

Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to Qwen3 30B A3B Thinking 2507.

  • Qwen is widely described as the strongest open-weight coding family, with reviewers testing whether local Qwen can replace hosted frontier assistants for agentic coding.
  • Benchmarks published by developer blogs show production-usable code and solid architectural reasoning, especially in the Coder variants.
  • The honest verdict in most write-ups: excellent value and privacy, still behind the top proprietary models on the hardest long-horizon tasks.

SOURCES

Frequently Asked Questions

  • What is Qwen3 30B A3B Thinking 2507?

    qwen3-30b-a3b-thinking-2507 provided by nebius. It is wired for API access through LLM.API with OpenAI-compatible patterns. On LLM.API it is addressed as `qwen3-30b-a3b-thinking-2507`.

  • When should I choose Qwen3 30B A3B Thinking 2507?

    You need a general-purpose text model for assistants, agents, or content workflows — especially when you specifically need Qwen3 30B A3B Thinking 2507.

  • Is Qwen3 30B A3B Thinking 2507 a chat model?

    Yes—Qwen3 30B A3B Thinking 2507 is exposed as a chat/completions-style endpoint on LLM.API.

  • What modalities does Qwen3 30B A3B Thinking 2507 support?

    Qwen3 30B A3B Thinking 2507 accepts text and produces text according to its architecture metadata on LLM.API.

  • What are limitations of Qwen3 30B A3B Thinking 2507?

    Like other API models, Qwen3 30B A3B Thinking 2507 can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

  • How do I call Qwen3 30B A3B Thinking 2507 via API?

    Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "qwen3-30b-a3b-thinking-2507" and your LLM.API key. See the code snippet on this page.

  • Can I use tools or structured outputs with Qwen3 30B A3B Thinking 2507?

    Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.

  • How is Qwen3 30B A3B Thinking 2507 priced on LLM.API?

    Listed pricing metadata shows: In $0.1 / 1M tokens · Out $0.3 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

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