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Qwen 3.5 27B VRAM Requirements — Dense Model Hardware Guide (Q4/Q5/Q6/Q8)

Qwen 3.5 27B needs ~16.5 GB at Q4_K_M on RTX 4090. See also the newer Qwen3.6-27B (April 22, 2026) which needs 16.8 GB Q4 and beats it on coding benchmarks.

If you are searching for Qwen 3.5 27B VRAM requirements, "will it run on my RTX 4090", or GGUF hardware guidance, this page has the exact numbers and realistic fit advice.

New in April 2026: Qwen3.6-27B released April 22 with nearly identical VRAM (16.8 GB Q4) but SWE-bench Verified 77.2% vs Qwen 3.5 27B's ~62%. If you already run Qwen 3.5 27B, upgrading to 3.6 is a free quality win on the same hardware.

Quick answers

  • Q4_K_M: ~16.5 GB — fits comfortably on 24 GB cards (RTX 4090, 3090)
  • Q5_K_M: ~19.4 GB — still fits on 24 GB, tighter context
  • Q6_K: ~22.1 GB — tight on 24 GB, comfortable on 32 GB+
  • Q8_0: ~28.9 GB — needs 32 GB+ (RTX 5090) or Apple Silicon 64 GB+
  • FP16: ~55.4 GB — datacenter GPU (A100/H100 80GB) or Mac Max 64 GB+
  • Speed expectation: 30-40 tok/s on RTX 4090 at Q4, 50-65 tok/s on RTX 5090

Qwen 3.5 27B specifications

Qwen 3.5 27B is the dense flagship in the size class that fits a single consumer 24 GB GPU. Unlike the 35B-A3B MoE sibling, every forward pass activates the full 27B parameter set — that means slightly slower inference but more consistent quality on complex reasoning, multi-step problems, and coding tasks.

SpecValue
Total parameters27 billion
ArchitectureDense transformer
Context window262,144 tokens (native, extensible to ~1M)
ProviderAlibaba Cloud
LicenseOpen weights (Apache 2.0)
Release dateFebruary 2026
Top GGUF providersUnsloth, LM Studio Community, bartowski
MLX providermlx-community

VRAM by quantization

Weights-only numbers calibrated against published GGUF files. Add 1-2 GB for KV cache at 4K-8K context, or scale up for longer contexts.

QuantizationVRAM (weights)24 GB GPU32 GB GPUApple Silicon 36 GB
Q4_K_M16.5 GB✅ ~7 GB headroom✅ comfortable✅ comfortable
Q5_K_M19.4 GB✅ ~4 GB headroom✅ comfortable✅ comfortable
Q6_K22.1 GB⚠️ tight (1.9 GB headroom)✅ ~10 GB✅ ~13 GB
Q8_028.9 GB❌ overflows✅ tight✅ ~6 GB
FP1655.4 GB❌❌❌ (needs 64 GB+)

Context window impact

Qwen 3.5 27B supports native 262K context. KV cache at that scale is significant. Rules of thumb:

  • 4K context: +1 GB KV cache
  • 16K context: +3 GB KV cache
  • 32K context: +6 GB KV cache
  • 64K context: +12 GB KV cache
  • 128K+ context: partial offloading needed on a 24 GB card

For most interactive use (chat, coding assistance), 8K-16K context is plenty — keeping the total VRAM budget comfortably under 22 GB.

Hardware compatibility matrix

16 GB GPUs — tight, not recommended

GPUQ4 fitWorkaround
RTX 4060 Ti 16GB⚠️ marginalNeeds partial CPU offload for context >2K
RTX 5080 16GB⚠️ marginalShort contexts only, slow otherwise

Prefer 9B for this tier if you want comfortable fit.

24 GB GPUs — sweet spot

GPUQ4Q5Q6Speed at Q4
RTX 4090 24GB✅✅⚠️~35-45 tok/s
RTX 3090 24GB✅✅⚠️~25-35 tok/s
RTX 3090 Ti 24GB✅✅⚠️~30-40 tok/s
RX 7900 XTX 24GB✅✅⚠️~25-35 tok/s
L4 24GB✅✅⚠️~20-28 tok/s
A10 24GB✅✅⚠️~22-32 tok/s

32 GB GPUs — any quantization

GPUQ4Q5Q6Q8Speed at Q4
RTX 5090 32GB✅✅✅✅ tight~55-65 tok/s
R9700 32GB✅✅✅✅ tight~40-55 tok/s

48 GB+ GPUs

Any professional 48 GB GPU (A6000, RTX 6000 Ada, L40) runs every quantization comfortably. For FP16 full precision you need 80 GB (A100, H100) or go Apple Silicon 64 GB+.

Apple Silicon guide

MacRAMQ4 fitQ6 fitFP16 fitSpeed at Q4
M4 16GB16 GB❌❌❌N/A
M4 Pro 24GB24 GB⚠️ tight❌❌~10-15 tok/s
M4 Max 36GB36 GB✅✅❌~18-24 tok/s
M4 Max 64GB64 GB✅✅✅~20-28 tok/s
M4 Max 128GB128 GB✅✅✅~22-30 tok/s

For Mac users, MLX delivers 15-25% better throughput than GGUF at the same quantization on Apple Silicon. Pick mlx-community/Qwen3.5-27B-MLX-4bit via LM Studio or mlx-lm.

Setup commands

Ollama

ollama run qwen3.5:27b

llama.cpp with Unsloth Q4_K_M

git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp && make -j

huggingface-cli download unsloth/Qwen3.5-27B-GGUF \
  Qwen3.5-27B-Q4_K_M.gguf --local-dir models/

./llama-cli -m models/Qwen3.5-27B-Q4_K_M.gguf \
  -n 512 --color -cnv -p "You are a careful reasoning assistant."

MLX on Mac

pip install mlx-lm
mlx_lm.generate --model mlx-community/Qwen3.5-27B-MLX-4bit \
  --prompt "Explain gradient descent in one paragraph."

vLLM (production serving)

vllm serve unsloth/Qwen3.5-27B-GGUF \
  --quantization gguf \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.92

How Qwen 3.5 27B compares

vs Qwen 3 32B (previous generation)

MetricQwen 3.5 27BQwen 3 32B
Parameters27B32B
VRAM at Q416.5 GB19.1 GB
Context262K native131K native
Quality (MMLU)matches or beatsbaseline

Qwen 3.5 27B is the clear upgrade — lower VRAM, longer context, better quality.

vs Qwen 3.5 35B-A3B (MoE sibling)

See the Qwen 3.5 35B-A3B guide for the MoE counterpart. Short version: 35B-A3B is faster (~70 tok/s vs 35 tok/s on RTX 4090) but uses ~5 GB more VRAM. 27B dense is often stronger on complex reasoning and coding.

vs Gemma 3 27B

MetricQwen 3.5 27BGemma 3 27B
VRAM at Q416.5 GB15.1 GB
Context262K128K
Multilingual✅✅ (100+ languages)✅
Coding✅✅✅

Qwen 3.5 27B wins on context length and coding; Gemma 3 27B is slightly lighter on VRAM.

Check compatibility

Related guides

Frequently Asked Questions

How much VRAM does Qwen 3.5 27B need?

Qwen 3.5 27B needs ~16.5 GB at Q4_K_M, ~19.4 GB at Q5_K_M, ~22.1 GB at Q6_K, and ~28.9 GB at Q8_0. Full FP16 requires ~55.4 GB. Add 1-2 GB for KV cache at default context, or up to 8 GB if you push toward the 262K context limit.

Can Qwen 3.5 27B run on RTX 4090?

Yes. The RTX 4090 (24 GB) runs Qwen 3.5 27B at Q4_K_M with ~7 GB of headroom — enough for a 16K-32K context window. Expect 30-40 tokens/second for the dense 27B at Q4, which is interactive chat speed.

Qwen 3.5 27B GGUF — which quantization should I use?

Q4_K_M is the standard choice and fits on any 24 GB GPU. Step up to Q5_K_M or Q6_K if you have 32 GB+ VRAM and want near-lossless quality. For coding and structured output tasks, prefer Q5 or higher because dense models are slightly more sensitive to aggressive quantization on domain-specific tasks than MoE models.

Does Qwen 3.5 27B fit on Mac?

An M4 Pro 24 GB runs Q4_K_M tightly (16.5 GB leaves ~3 GB after macOS overhead). An M4 Max 36 GB handles Q6 comfortably. An M4 Max 64 GB can run FP16 at ~55 GB for full-precision quality. MLX is the recommended framework on Apple Silicon for best throughput.

Qwen 3.5 27B vs Qwen 3.5 35B-A3B — which should I pick?

If you want faster inference and longer context interactions, pick 35B-A3B (MoE). If you want predictable latency, deeper reasoning quality, and 4-5 GB less VRAM usage, pick 27B dense. For coding or math-heavy workloads, many users prefer 27B dense — its activation of all 27B parameters per token gives it an edge on complex reasoning chains.

What is the difference between Qwen 3.5 27B and Qwen 3 32B?

Qwen 3.5 27B is the newer, refreshed dense model with updated training data and better instruction-following. Qwen 3 32B is the older 32B dense from the Qwen 3 family. Qwen 3.5 27B uses 5 B fewer parameters (16.5 GB vs 19.1 GB at Q4) while matching or beating Qwen 3 32B on most benchmarks.