Qwen3-235B-A22B FP8

FP8-quantized version of Qwen3-235B-A22B, a 235B MoE with 22B active, with a reduced memory footprint at near-identical quality.

Params: 235B, 22B active
Context: 33K tokens
License: Apache 2.0

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Hosted API pricing

How we price

Order

Providers are listed cheapest input rate first, then by output rate, a tie going to the creator's own listing. A provider that lists the model without a published rate sits at the end.

Prices

Base-tier, on-demand rates in USD per 1M tokens, converted at ECB reference rates where a provider publishes in another currency. Cached-input and batch rates show only where a provider publishes them. "Cheapest" marks the one provider with the lowest input and output pair; a tie wears no badge.

Cost

Input rate times the input tokens plus output rate times the output tokens, at the monthly volume set above the table. Cached-input, batch, long-context and reasoning-token billing are not modelled.

Transparency and funding

Affiliates: Affiliate links are marked. We may earn a commission if you click them, but commissions never affect the order.

Every provider serving Qwen3-235B-A22B FP8, cheapest input first. Set your monthly volume to see what each would bill.

Input M Output M

Every provider serving Qwen3-235B-A22B FP8, with its price per 1M input and output tokens and what the volume above would cost
Provider	Input /1M	Output /1M	Cost at 10M in + 2M out	Link
Alibaba Cloud Creator	Open weights, no hosted price			Visit website

Heads up: Base-tier, on-demand rates per 1M tokens; cached, batch and long-context tiers excluded. A provider may serve a shorter context or a quantized build than the creator's release. Verify before provisioning. More on how we price.

Estimated cost to self-host

Qwen3-235B-A22B FP8 needs about 138 GB of GPU memory at 4-bit with its full 32K context. The cheapest rental that fits is 8× RTX 3090 at about $1,440 a month.

Single node ≤ 8

What it costs to self-host Qwen3-235B-A22B FP8 at each precision: the memory it needs, the cheapest rental that holds it, that rental's month and the API volume it pays off at
Precision	Memory	Cheapest fit (Full 32K context)	Cost /mo
4-bit INT4 / FP4	138 GB	8× RTX 3090	$1,440
8-bit FP8 / INT8	243 GB	8× RTX A6000	$3,398
16-bit FP16 / BF16	478 GB	8× A100	$10,714

Estimates based on median on-demand rates for Nvidia GPUs. Memory is weights plus KV cache for one request, using FP8 KV cache where supported and FP16/BF16 otherwise. No guarantee of runtime support, usable performance, or that a matching quantized build exists. Pricing methodology.

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Capabilities

What Qwen3-235B-A22B FP8 accepts and can do, as published by Alibaba Cloud.

Text Accepts and generates natural-language text.
Reasoning Thinks before it answers, always on or as a switchable mode.
Function calling Connect to external tools, APIs, and systems.
Structured output Return responses in structured formats like JSON.

Common questions

What is Qwen3-235B-A22B FP8 good for?

Self-hosted reasoning and chat with switchable thinking and non-thinking modes. Apache 2.0 weights, Alibaba's reduced-memory FP8 build of the 235B mixture of experts, on a multi-GPU node.

When is Qwen3-235B-A22B FP8 not a good fit?

Image, video or audio input: text only, and its native context limits long-document work. Memory follows the full 235B parameters, not the 22B active, so it still takes a multi-GPU node.

What is the cheapest way to run Qwen3-235B-A22B FP8?

No provider we track hosts it; self-hosting on the cheapest rental that fits (8x RTX 3090, $2.00 per hour) costs about $1,440 a month.

Can I self-host Qwen3-235B-A22B FP8?

Yes. The weights are Apache 2.0 licensed. At 4-bit it needs about 138 GB of GPU memory, which starts at roughly $1,440 a month on the cheapest rental that fits.