Qwen 3.5 9B VRAM Requirements

Qwen 3.5 9B needs about 6.4 GB of VRAM at Q4_K_M with an 8K context: 5.1 GB for weights, 0.3 GB for the KV cache and 1.0 GB of runtime overhead. At Q8_0 it needs 10.2 GB and at full FP16 precision 18.0 GB. The smallest GPU that runs it fully is the RTX 3050 8GB.

By Alibaba · Updated October 14, 2026 · Values from config.json on Hugging Face

6.4 GB VRAM

Qwen 3.5 9B at Q4_K_M with 8K context and the KV cache in FP16.

  • Weights 5.1 GB
  • KV cache 0.3 GB
  • Runtime overhead 1.0 GB

Which GPUs can run it

Qwen 3.5 9B VRAM by quantization

Total memory with an FP16 KV cache. "Smallest tier" is the smallest standard VRAM size with a 6% safety margin at 8K.

QuantizationBits/weightWeightsTotal at 8KTotal at 32KSmallest tier
Q4_K_M4.855.1 GB6.4 GB7.1 GB8 GB
Q5_K_M5.695.9 GB7.3 GB8.0 GB8 GB
Q6_K6.596.9 GB8.2 GB9.0 GB10 GB
Q8_08.508.9 GB10.2 GB10.9 GB12 GB
FP16 / BF1616.0016.7 GB18.0 GB18.8 GB20 GB
Q3_K_M3.914.1 GB5.4 GB6.2 GB6 GB
Q2_K3.003.1 GB4.5 GB5.2 GB6 GB

VRAM by context length

At Q4_K_M. Hybrid attention: only 8 of 32 layers use full attention and keep a KV cache (4 KV heads × 256 dimensions). The other 24 use linear attention (Gated DeltaNet) with a fixed state of about 50 MB that does not grow with context.

ContextKV cache (FP16)KV cache (Q8_0)Total, FP16 cacheTotal, Q8_0 cache
4K tokens0.2 GB0.1 GB6.3 GB6.2 GB
8K tokens0.3 GB0.2 GB6.4 GB6.3 GB
32K tokens1.0 GB0.6 GB7.1 GB6.7 GB
128K tokens4.0 GB2.2 GB10.1 GB8.3 GB
256K tokens8.0 GB4.3 GB14.1 GB10.4 GB

GPUs that can run Qwen 3.5 9B

At Q4_K_M with 8K context. "Best quant" is the highest-quality format that fits fully; "max context" is at Q4_K_M.

GPUUsable memoryStatusBest quantMax contextSpeed
RTX 3050 8GB8.0 GBFitsQ5_K_M44K~26 tok/s
RTX 4060 8GB8.0 GBFitsQ5_K_M44K~31 tok/s
RTX 4060 Ti 8GB8.0 GBFitsQ5_K_M44K~33 tok/s
Radeon RX 7600 8GB8.0 GBFitsQ5_K_M44K~28 tok/s
RTX 3060 Ti 8GB8.0 GBFitsQ5_K_M44K~51 tok/s
RTX 3070 8GB8.0 GBFitsQ5_K_M44K~51 tok/s
RTX 5060 8GB8.0 GBFitsQ5_K_M44K~51 tok/s
RTX 5060 Ti 8GB8.0 GBFitsQ5_K_M44K~51 tok/s
RTX 3070 Ti 8GB8.0 GBFitsQ5_K_M44K~69 tok/s
Intel Arc B570 10GB10.0 GBFitsQ6_K104K~33 tok/s
RTX 3080 10GB10.0 GBFitsQ6_K104K~86 tok/s
Apple M4 16GB10.7 GBFitsQ6_K125K~14 tok/s
RTX 2080 Ti 11GB11.0 GBFitsQ8_0134K~70 tok/s
RTX 2060 12GB12.0 GBFitsQ8_0164K~38 tok/s

About Qwen 3.5 9B

A dense 9B model that replaces Qwen 3 8B for most local users. Hybrid attention means only 8 layers keep a KV cache, so it can use a 64K context on a 12 GB card. The 248K-token vocabulary adds about 2 GB of embedding weights in FP16, which is why it is slightly larger than its name suggests.

SpecValue
Parameters8.95B
Layers32
Hidden size4096
Attention heads / KV heads16 / 4
Head dimension256
Vocabulary248,320
Max context (used here)256K tokens
Linear-attention layers24 of 32 (Gated DeltaNet, fixed 50 MB state)
KV cache per token (FP16)32 KB
Released2026-03
LicenseApache 2.0

Qwen 3.5 9B VRAM: common questions

Can Qwen 3.5 9B run on an 8GB GPU?

Yes at Q4_K_M with a moderate context. The weights are about 5 GB, and because only 8 of 32 layers store a cache, even a 32K context adds only about 1 GB.

How much VRAM does Qwen 3.5 9B need?

About 6.4 GB at Q4_K_M with an 8K context, 10.2 GB at Q8_0 and 18.0 GB at FP16. Longer contexts add 0.03 GB per 1,000 tokens with an FP16 cache.

What is the cheapest GPU that can run Qwen 3.5 9B?

The smallest listed device that fits it fully at Q4_K_M with 8K context is the RTX 3050 8GB (8.0 GB usable).

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