Models / Qwen3.6 35B A3B

Qwen3.6 35B A3B

MoE enthusiast
HuggingFace → HuggingBay → Alibaba → Find for my hardware

35B total, 3B active per token (MoE: 256 experts, 8 routed + 1 shared) with hybrid Gated DeltaNet + Gated Attention layers. 256K native context, extensible to ~1M.

The agentic flagship - ranked #1 local model for agent workflows in 2026 (perfect tool-calling, zero catastrophic failures on an 84-scenario / 16-category / 8-trial benchmark).

  • Q4_K_M (~20GB) for GGUF runtimes.
  • Q8_0 (~37GB) for full precision.

AI-generated content marks

This model embeds text watermarks in generated text and adds C2PA provenance metadata to supported files such as .png, .jpg, and .svg. Marks can be lost through editing, screenshots, or format conversion, so their absence does not prove a file is human-made.

Provider transparency docs →

coding reasoning writing chat
Parameters
35.0B
Context
262k
License
apache 2.0
Developer
Alibaba
Origin
🇨🇳 China
Released
Apr 2026

Scores

Coding
90
Reasoning
91
Tool calling
95
General
87

Score per dollar

1740 pts per $/M input

general_score (87) divided by cheapest input price ($0.05/M). Higher is better value. See live pricing.

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Guides covering Qwen3.6 35B A3B

DGX Spark benchmarks: which local model wins in 2026 Aug 19, 2026

Save your hardware and every model page answers the real question: will it run on your machine, and how fast?

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Run it locally

Per-quant memory needs and a static "can you run it?" reference - no rig entry required

Q4_K_M
20.0GB 20.0GB min 22.0GB rec
Balanced - the usual local sweet spot
Q8_0
37.0GB 37.0GB min 40.0GB rec
Near-lossless

The reference hardware

Schematic of the NVIDIA Jetson Orin NX 16GB reference rig - 16GB unified memory, 102 GB/s aggregate bandwidth
Schematic of the Single GTX 1080 Ti (11GB) reference rig - 11GB VRAM, 484 GB/s aggregate bandwidth
Schematic of the 4x H100 80GB (320GB) reference rig - 320GB VRAM, 13400 GB/s aggregate bandwidth
Schematic of the NVIDIA DGX Station 748GB reference rig - 748GB unified memory, 8000 GB/s aggregate bandwidth
Schematic of the 8x RTX 3090 rack (192GB) reference rig - 192GB VRAM, 7489 GB/s aggregate bandwidth
Schematic of the 4x RTX 5090 (128GB) reference rig - 128GB VRAM, 7168 GB/s aggregate bandwidth
Schematic of the AMD Instinct MI300X (192GB) reference rig - 192GB VRAM, 5324 GB/s aggregate bandwidth
Schematic of the 4x RTX 4090 (96GB) reference rig - 96GB VRAM, 4032 GB/s aggregate bandwidth
Schematic of the 2x RTX 5090 (64GB) reference rig - 64GB VRAM, 3584 GB/s aggregate bandwidth
Schematic of the 2x RTX 3090 (48GB) reference rig - 48GB VRAM, 1872 GB/s aggregate bandwidth
Schematic of the Single RTX 5090 (32GB) reference rig - 32GB VRAM, 1792 GB/s aggregate bandwidth
Schematic of the RTX PRO 6000 Blackwell (96GB) reference rig - 96GB VRAM, 1792 GB/s aggregate bandwidth
Schematic of the Mac Studio M4 Ultra 192GB reference rig - 192GB unified memory, 1092 GB/s aggregate bandwidth
Schematic of the Mac Studio M4 Ultra 512GB reference rig - 512GB unified memory, 1092 GB/s aggregate bandwidth
Schematic of the Single RTX 4090 (24GB) reference rig - 24GB VRAM, 1008 GB/s aggregate bandwidth
Schematic of the MacBook Pro M5 Max 128GB reference rig - 128GB unified memory, 614 GB/s aggregate bandwidth
Schematic of the Dual EPYC 9004 + 768GB DDR5-4800 reference rig - 768GB unified memory, 460 GB/s aggregate bandwidth
Schematic of the DGX Spark 128GB unified reference rig - 128GB unified memory, 273 GB/s aggregate bandwidth
Schematic of the Ryzen AI Max+ 395 128GB reference rig - 128GB unified memory, 256 GB/s aggregate bandwidth
Schematic of the Jetson AGX Orin 64GB reference rig - 64GB unified memory, 204 GB/s aggregate bandwidth
Schematic of the Epyc + 512GB DDR4-3200 + 2x RTX 3090 reference rig - 560GB unified memory, 204 GB/s aggregate bandwidth
Schematic of the Epyc + 512GB DDR4-2400 + 2x RTX 3090 reference rig - 560GB unified memory, 153 GB/s aggregate bandwidth

22 reference configs, drawn in-house. Scroll for more.

Can you run it? - reference rigs

Rig Q4_K_M Q8_0
NVIDIA Jetson Orin NX 16GB no -> cloud no -> cloud
Single GTX 1080 Ti (11GB) no -> cloud no -> cloud
4x H100 80GB (320GB) fast 1858.2t/s fast 1358.9t/s
NVIDIA DGX Station 748GB fast 1109.4t/s fast 811.3t/s
8x RTX 3090 rack (192GB) fast 1038.6t/s fast 759.5t/s
4x RTX 5090 (128GB) fast 994.0t/s fast 726.9t/s
AMD Instinct MI300X (192GB) fast 738.4t/s fast 540.0t/s
4x RTX 4090 (96GB) fast 559.1t/s fast 408.9t/s
2x RTX 5090 (64GB) fast 497.0t/s fast 363.5t/s
2x RTX 3090 (48GB) fast 259.6t/s fast 189.9t/s
Single RTX 5090 (32GB) fast 248.5t/s offload
RTX PRO 6000 Blackwell (96GB) fast 248.5t/s fast 181.7t/s
Mac Studio M4 Ultra 192GB fast 165.2t/s fast 120.8t/s
Mac Studio M4 Ultra 512GB fast 165.2t/s fast 120.8t/s
Single RTX 4090 (24GB) fast 139.8t/s offload
MacBook Pro M5 Max 128GB fast 92.9t/s fast 67.9t/s
Dual EPYC 9004 + 768GB DDR5-4800 fast 63.9t/s fast 46.7t/s
DGX Spark 128GB unified fast 37.9t/s fast 27.7t/s
Ryzen AI Max+ 395 128GB fast 35.5t/s fast 26.0t/s
Jetson AGX Orin 64GB fast 28.4t/s fast 20.8t/s
Epyc + 512GB DDR4-3200 + 2x RTX 3090 fast 28.4t/s fast 20.8t/s
Epyc + 512GB DDR4-2400 + 2x RTX 3090 fast 21.3t/s ok 15.6t/s

Fit tiers use the same will-it-run logic as the rig finder. For comfortable fits, the badge reflects decode speed: fast >=20 t/s, ok 8-20 t/s, slow <8 t/s. t/s is a bandwidth estimate, not a measured benchmark.

Download options

Q4_K_M official -5% vs fp16
20.0GB dl 20.0GB min 22.0GB rec
REC RAM vs largest quant
Q8_0 official -1% vs fp16
37.0GB dl 37.0GB min 40.0GB rec
REC RAM vs largest quant

Or run it in the cloud

Live per-provider pricing, throughput and uptime - refreshed about 5 hours ago via OpenRouter. Click a column to sort.

Provider Type Input $/M Output $/M Cache $/M Tok/s Latency Uptime Value
Venice
API 0.10 1.00 0.050 - - 100.00% best uptime
AkashML
API 0.10 0.90 0.050 - - 100.00%
CoreWeave
API 0.25 1.25 0.250 - - 100.00%
Parasail
API 0.15 1.00 0.050 - - 99.99%
Darkbloom
API 0.05 0.70 0.025 - - 99.96% cheapest
DeepInfra
API 0.10 0.95 0.100 - - 99.83%
DekaLLM
API 0.10 1.00 0.050 - - 99.81%
SiliconFlow
API 0.24 1.80 0.150 - - 99.75%
AtlasCloud
API 0.19 1.11 0.186 - - 99.21%
Phala avoid
API 0.20 1.27 0.056 - - 59.67%

Default order: throughput among 95%+ uptime providers, then latency; subscriptions last. Sort by any column. Subscription rows show $/mo in the Value column - per-token columns are "-". Affiliate links are marked sponsored / nofollow. Confirm current pricing on the provider's site before committing.

Detailed API pricing page + JSON endpoint →

See who runs Alibaba in production →

PRICE HISTORY

Inference cost over time

Data accumulates from the first daily sync - longer ranges populate over time. Prices come from OpenRouter snapshots, not a historical API.

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