Will It Run AI
qwen, qwen-3-5, 9b, dense, vram, gpu-requirements, apple-silicon

Qwen 3.5 9B VRAM Requirements — Best 8B-Class Dense Model (Q4, Q5, Q6, Q8)

Qwen 3.5 9B needs ~5.5 GB at Q4_K_M and ~9.6 GB at Q8_0. Runs well on 8 GB GPUs, comfortably on 12 GB. Full VRAM table, Mac fit, and tokens/second benchmarks.

If you are searching for Qwen 3.5 9B VRAM requirements or "will it run on my 8 GB / 12 GB / 16 GB GPU", here are the exact numbers.

Quick answers

  • Q4_K_M: ~5.5 GB — fits on any 8 GB GPU (RTX 4060, RTX 3060)
  • Q5_K_M: ~6.5 GB — comfortable on 8 GB, ideal on 12 GB
  • Q6_K: ~7.4 GB — best on 12 GB+ (RTX 4070, RTX 3060 12GB)
  • Q8_0: ~9.6 GB — comfortable on 12 GB+, near-lossless quality
  • FP16: ~18.5 GB — runs on 24 GB (RTX 4090) or Apple Silicon 24 GB+
  • Speed: 40-60 tok/s on RTX 4060, 60-80 on RTX 4070, 90-120 on RTX 4090

Qwen 3.5 9B specifications

Qwen 3.5 9B is the sweet spot of the Qwen 3.5 lineup for mainstream consumer hardware. At ~5.5 GB in Q4 it runs on virtually any modern gaming GPU while delivering quality that competes with models 3-4× its size in chat and coding benchmarks.

SpecValue
Total parameters9 billion
ArchitectureDense transformer
Context window262,144 tokens (native)
ProviderAlibaba Cloud
LicenseOpen weights (Apache 2.0)
ReleaseFebruary 2026
GGUF providersUnsloth, LM Studio Community, bartowski, Qwen team
MLX providermlx-community

VRAM by quantization

QuantizationVRAM (weights)8 GB GPU12 GB GPU16 GB GPU24 GB GPU
Q4_K_M5.5 GB✅ ~2 GB headroom✅ comfortable✅✅
Q5_K_M6.5 GB✅ ~1 GB headroom✅✅✅
Q6_K7.4 GB⚠️ tight✅ ~4 GB headroom✅✅
Q8_09.6 GB❌✅ ~2 GB headroom✅✅
FP1618.5 GB❌❌❌✅

KV cache reminder: add ~1 GB per 8K of context. At 32K context + Q8_0 on a 12 GB GPU, you are already pushing the limits — drop to Q6_K if you run long conversations.

Hardware compatibility

8 GB GPUs — mainstream gaming tier

GPUBest quantSpeed
RTX 4060 8GBQ5_K_M~40-55 tok/s
RTX 3060 Ti 8GBQ4_K_M~35-45 tok/s
RTX 3070 8GBQ5_K_M~45-60 tok/s
RTX 4060 Ti 8GBQ5_K_M~42-55 tok/s
Arc B580 12GBQ6_K~30-40 tok/s (Vulkan)

12 GB GPUs — ideal for 9B

GPUBest quantSpeed
RTX 4070 12GBQ6_K~60-75 tok/s
RTX 4070 Super 12GBQ6_K~70-85 tok/s
RTX 3060 12GBQ6_K~35-45 tok/s
RTX 3080 12GBQ8_0~55-70 tok/s
RTX 4070 Ti 12GBQ8_0~75-90 tok/s

16 GB+ GPUs — Q8 near-lossless

GPUBest quantSpeed
RTX 4060 Ti 16GBQ8_0~45-55 tok/s
RTX 5080 16GBQ8_0~100-130 tok/s
RTX 4080 Super 16GBQ8_0~90-115 tok/s
RTX 4090 24GBQ8_0 (+ FP16 viable)~110-140 tok/s
RTX 5090 32GBFP16~150-200 tok/s

Apple Silicon guide

Qwen 3.5 9B is one of the friendliest models for Mac — it fits even on the smallest M4 configurations.

MacRAMBest quantSpeed
M4 16GB (MacBook Air)16 GBQ4-Q5~25-35 tok/s
M4 Pro 24GB24 GBQ8_0~30-40 tok/s
M4 Max 36GB36 GBFP16~40-55 tok/s
M4 Max 64GB64 GBFP16~45-60 tok/s

Tip for MacBook Air M4 16GB users: stick to Q4_K_M or Q5_K_M and close memory-heavy apps (Chrome, Docker) before running inference. The MacBook Air M4 24GB version gives you enough headroom to run Q8_0 while keeping a browser open — worth the upgrade if local LLMs are a daily workflow.

Setup commands

Ollama (easiest)

ollama run qwen3.5:9b

LM Studio (GUI)

Search "Qwen 3.5 9B" in LM Studio's Discover tab. Pick Q4_K_M for 8 GB cards or Q6_K for 12 GB+.

llama.cpp

huggingface-cli download unsloth/Qwen3.5-9B-GGUF \
  Qwen3.5-9B-Q5_K_M.gguf --local-dir models/

./llama-cli -m models/Qwen3.5-9B-Q5_K_M.gguf \
  -n 512 --color -cnv \
  -p "You are a concise coding assistant."

MLX on Mac

pip install mlx-lm
mlx_lm.generate --model mlx-community/Qwen3.5-9B-MLX-4bit \
  --prompt "Write a Python one-liner to deduplicate a list."

vLLM (serving)

vllm serve unsloth/Qwen3.5-9B-GGUF \
  --quantization gguf \
  --max-model-len 32768

Qwen 3.5 9B vs alternatives

vs Llama 3.1 8B

MetricQwen 3.5 9BLlama 3.1 8B
VRAM at Q45.5 GB~4.9 GB
Context262K128K
MMLU~75%~68%
Multilingual100+ languages~25 languages
CodingStrongerGood

Qwen 3.5 9B is the clear pick for fresh 2026 deployments, especially if you need multilingual or coding performance.

vs Gemma 3 12B

MetricQwen 3.5 9BGemma 3 12B
VRAM at Q45.5 GB6.7 GB
Context262K128K
MMLU~75%~72%
LicenseApache 2.0Gemma License

Qwen 3.5 9B is more permissively licensed and beats Gemma 3 12B while using less VRAM.

vs Qwen 3.5 27B (bigger dense sibling)

Step up to 27B when you need deeper reasoning and have 24 GB+ VRAM. See Qwen 3.5 27B VRAM Requirements.

Check compatibility

Related guides

Frequently Asked Questions

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B needs ~5.5 GB at Q4_K_M, ~6.5 GB at Q5_K_M, ~7.4 GB at Q6_K, and ~9.6 GB at Q8_0. Full FP16 requires ~18.5 GB. Add 1 GB for KV cache at standard context lengths.

Can Qwen 3.5 9B run on 8 GB VRAM?

Yes. Qwen 3.5 9B at Q4_K_M (~5.5 GB) fits comfortably on an 8 GB GPU like the RTX 4060. Q5_K_M (~6.5 GB) also fits. For Q6_K or Q8_0 you need 12 GB+ VRAM.

What is the best GPU for Qwen 3.5 9B?

For pure value, an RTX 4060 8GB handles Q4-Q5 comfortably. For best throughput, an RTX 4070 12GB runs Q6_K at 50-70 tokens/second. If you want headroom for coding sessions with long context, an RTX 4090 24GB runs Q8_0 with generous context.

Does Qwen 3.5 9B fit on MacBook Air M4 16GB?

Yes, comfortably. Qwen 3.5 9B at Q4_K_M (~5.5 GB) leaves ~7 GB for macOS, apps, and context. Expect 25-35 tokens/second on MacBook Air M4 16GB via MLX — fast enough for interactive chat and coding.

Qwen 3.5 9B vs Llama 3.1 8B — which is better?

Qwen 3.5 9B beats Llama 3.1 8B on most benchmarks: +8% on MMLU, significantly stronger at multilingual (100+ languages), and noticeably better at coding. Llama 3.1 8B has a larger community ecosystem. For a fresh 2026 local chat/coding assistant, Qwen 3.5 9B is the stronger pick.

What quantization should I use for Qwen 3.5 9B?

If you have 8 GB VRAM, use Q4_K_M (~5.5 GB, minor quality loss). With 12 GB, Q6_K (~7.4 GB) is near-lossless. With 16 GB+, Q8_0 (~9.6 GB) is effectively identical to full precision. For coding or structured output, prefer Q5_K_M or higher.