llmrun
AlibabaQwen 3.6Qwen3_5ForConditionalGeneration

Qwen3.6 27B — Hardware Requirements & GPU Compatibility

VisionFunctions

Released a few months before its Qwen3.8 sibling, Qwen3.6 27B is Alibaba's dense, roughly 28-billion-parameter model from the Qwen 3.6 generation. Vision input is supported alongside text, so the model can handle image-based prompts as well as standard chat, coding, and reasoning tasks. Like other models in this size class, it is best run locally on a single high-end consumer GPU with quantization rather than lower-end hardware. Qwen3.6 27B carries a 256K token context window for handling lengthy inputs, and is released under the Apache 2.0 license, permitting free commercial and research use. It sits alongside the larger Qwen3.6 35B A3B mixture-of-experts model as one of two Qwen 3.6 options with local-deployment potential.

27.8B params · dense262K contextApache 2.02.3M downloads2.3K likes3.4M quant downloads
A

Runs well on a 24 GB GPU

Runs from12 GB IQ3_XXS · 16KBest on24 GB Q4_K_M · ~38 tok/s · 64KMacs from16 GB IQ3_XXS · ~12 tok/s

Score 111.2#24 of 100 open#2 of 35 that fit 24 GBApache 2.0

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

llmrun Composite Score →

111.2

Benchmarks · tier chart ↓
Among 100 open modelsHow the Composite Score is built

Run Qwen3.6 27B

One recommended way. Every other method, OS and quant is in the step-by-step guide.

Recommended

Ollama
one command · needs Ollama installed
$ollama run hf.co/unsloth/Qwen3.6-27B-GGUF:Q4_K_M

Q4_K_M fits fully on the RTX 4090 · 6.1 GB free · 16K context

Download: Qwen3.6-27B-Q4_K_M.gguf16.82 GBunsloth

install, OS, other quants

Other ways

each opens step-by-step

Run in cloud

Fits on RTX A5000 (24 GB) (6 GB headroom) · Q4_K_M

Generation speed
~29 tok/s
generation speed
Cost per 1M output tokens
$1.55
per 1M output tokens
Deployon RunPodBest optionfrom $0.16/hr (opens in new tab)
Compare GPUs →
or

How Much VRAM Does Qwen3.6 27B Need?

Select a quantization level; the listed hardware adheres to it.

QuantizationVRAM+32KOn RTX 4090 (reference)
12.6 GB13.9 GB2-bit · K-quant — 2-bit quantization with K-quant improvementsFits: 128K context · ~52 tok/s
12.9 GB14.2 GB3-bit small — 3-bit small quantizationFits: 128K context · ~51 tok/s
14.3 GB15.6 GB3-bit medium — 3-bit medium quantizationFits: 128K context · ~46 tok/s
14.6 GB16 GB4-bit · legacy — 4-bit legacy quantizationFits: 128K context · ~45 tok/s
17.4 GB18.8 GB4-bit medium — 4-bit medium quantization — most popular sweet spotFits: 64K context · ~38 tok/s
20.5 GB21.9 GB5-bit medium — 5-bit medium quantization — good quality/size tradeoffFits: 64K context · ~32 tok/s
23.7 GB25 GB6-bit, very good — 6-bit quantization, very good qualityFits: 8K context · ~28 tok/s
28.5 GB29.9 GB8-bit, near-lossless — 8-bit quantization, near-losslessDoes not fit: needs 29 GB

Selected Q4_K_M

4-bit medium quantization

17.9GBat 16K context

Weights
16.7 GB
KV cache
0.9 GB
Overhead
0.3 GB

Fits RTX 4090 (reference) · 6.1 GB free · ~38 tok/s

≥50 tok/s needs ≥1.34 TB/s · RTX 5090 ~67

Which Hardware Can Run Qwen3.6 27B?

Q4_K_M · 17.4 GB

Qwen3.6 27B (Q4_K_M) requires 17.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 23+ GB is recommended. Using the full 262K context window can add up to 13.9 GB, bringing total usage to 31.3 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run Qwen3.6 27B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Mini M5 Pro (24 GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M
8 GB · RTX 4060Doesn't fit — smallest quant needs 8.8 GB8 GB tier →
12 GB · RTX 3060 12GBIQ3_XXS · 12 GB · ~20 tok/s · 16K12 GB tier →
16 GB · RTX 5060 Ti 16GBQ3_K_L · 15.4 GB · ~19 tok/s · 16K16 GB tier →
24 GB · RTX 4090referenceQ4_K_M · 17.9 GB · ~38 tok/s · 64K24 GB tier →
Mac 32 GB · Mac Mini M4Q4_K_M · 17.9 GB · ~5 tok/s · 128KMacs →
Mac 64 GB · Mac Studio M4 MaxQ4_K_M · 17.9 GB · ~22 tok/s · 256KMacs →

How good is Qwen3.6 27B?

The llmrun Score merges 11 public benchmark boards into a single figure, where 100 represents the average for open models.

Score vs VRAM in the 24 GB tier

  • Qwen3.6 27B
  • Other models
  • Best Score for its VRAM

#2 of 35 that fit 24 GB

Each dot is a model at the best quant that fits a 24 GB GPU. The line joins the models with the best Score for their VRAM; a dot below it is beaten by a model that needs no more memory.

Full 24 GB ranking →

Benchmarks

The 6 boards that weigh most in the llmrun Score, of 11 with a result for this model; badges show who ran the test.

  • GPQA Diamond

    Epoch AI · Apr 2026Independent

    85.9%

    #19/98 open

    Independent
  • LiveBench Coding

    LiveBench · Jun 2026Independent

    71.8

    #13/19 open

    Independent
  • LiveBench Reasoning

    LiveBench · Jun 2026Independent

    70.3

    #19/19 open

    Independent
  • AIME 2024/2025

    Epoch AI · Apr 2026Independent

    91.1%

    #12/86 open

    Independent
  • FrontierMath

    Epoch AI · Apr 2026Independent

    35.1%

    #14/17 open

    Independent
  • LiveBench Math

    LiveBench · Jun 2026Independent

    79.9

    #16/19 open

    Independent
All 11 Qwen3.6 27B benchmarks

In the 24 GB class

35 models fit 24 GB

Same Score, only models that fit 24 GB at their best quant: the ranking a 24 GB owner actually chooses from.

  1. #1

    Qwen3.8 27BAlibaba

    122.1

    ~38 tok/s · 17.9 GB

  2. #2

    Qwen3.6 27BAlibaba

    111.2

    ~38 tok/s · 17.9 GB

  3. #3

    Gemma 4 31B ITGoogle

    106.8

    ~32 tok/s · 21.7 GB

  4. #4

    Qwen3.6 35B A3BAlibaba

    106.8

    ~151 tok/s · 22.0 GB

  5. #5

    Gemma 4 26B A4B ITGoogle

    105.8

    ~171 tok/s · 16.5 GB

  6. #6

    Qwen3.5 35B A3BAlibaba

    105.8

    ~151 tok/s · 22.0 GB

Full 24 GB ranking →RTX 4090 page →

Alternatives to Qwen3.6 27B

Qwen 3.6 family · 15 models →

Official models with an llmrun Score, each answering one question; numbers are at the best quant that fits a 24 GB GPU.

Similar models

10
ModelScoreVRAMtok/sWhy it is similar
Gemma 4 26B A4B ITGoogle105.816.5 GB~171similar score
Qwen3.5 35B A3BAlibaba105.822 GB~151similar score
Qwen3.5 27BAlibaba103.817.9 GB~38similar score
Seed OSS 36B InstructByteDance-Seed102.522.8 GB~31similar score
Qwen3 30B A3B Thinking 2507Alibaba100.719.4 GB~173similar score
Qwen3.5 9BAlibaba99.46.6 GB~103similar score
Qwen3 32BAlibaba98.622.6 GB~32similar score
Qwen3 14BAlibaba98.011.9 GB~69similar score
DeepSeek R1 Distill Qwen 32BDeepSeek97.223 GB~34similar score
QwQ 32BAlibaba96.223 GB~34similar score
Fine-tunes & variants(14)

Specifications

Publisher
Alibaba
Family
Qwen 3.6
Parameters
27.8B
Architecture
Qwen3_5ForConditionalGeneration
Context Length
262,144 tokens
Vocabulary Size
248,320
Release Date
2026-04-21
License
Apache 2.0

Resources

Frequently Asked Questions

How much VRAM does Qwen3.6 27B need?

Qwen3.6 27B requires 17.4 GB of VRAM at Q4_K_M, or 56.3 GB at BF16. Full 262K context adds up to 13.9 GB (31.3 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 27.8B × 4.8 bits ÷ 8 = 16.7 GB

KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)

Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.

KV Cache + Overhead ≈ 14.6 GB (at full 262K context)

VRAM usage by quantization

17.4 GB
31.3 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 4090 run Qwen3.6 27B?

Yes, at Q6_K (23.7 GB) or lower. Higher quantizations like Q8_0 (28.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Qwen3.6 27B?

For Qwen3.6 27B, Q4_K_M (17.4 GB) offers the best balance of quality and VRAM usage. Q4_K_L (17.8 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 8.4 GB.

VRAM requirement by quantization

IQ2_XXS
8.4 GB
Q2_K
12.6 GB
IQ4_XS
15.7 GB
Q4_K_M ★
17.4 GB
Q4_K_L
17.8 GB
BF16
56.3 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Qwen3.6 27B on a Mac?

Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Qwen3.6 27B, the smallest at IQ3_XXS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 17.4 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M5 Pro (24 GB).

Can I run Qwen3.6 27B locally?

Yes — Qwen3.6 27B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Qwen3.6 27B?

At Q4_K_M, Qwen3.6 27B can reach ~276 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~38 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: NVIDIA B200 → 8000 ÷ 17.4 × 0.65 = ~299 tok/s

Estimated speed at Q4_K_M (17.4 GB)

~299 tok/s
~38 tok/s
~299 tok/s
~276 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Qwen3.6 27B?

At Q4_K_M, the download is about 16.67 GB. The full-precision BF16 version is 55.56 GB. The smallest option (IQ2_XXS) is 7.64 GB.

Which GPUs can run Qwen3.6 27B?

8 consumer GPUs can run Qwen3.6 27B at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU has plenty of headroom for comfortable inference.

Which devices can run Qwen3.6 27B?

122 devices with unified memory can run Qwen3.6 27B at Q4_K_M (17.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.