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Run LLM Locally

Qwen3.6 35B-A3B on the RTX 5090

With 32 GB of VRAM, the RTX 5090 runs Qwen3.6 35B-A3B at Q4_K_M entirely on the GPU with an 8K context (about 23 GB needed). At Q8_0 it does not fit in VRAM.

Qwen3.6 35B-A3B

Q4_K_M · 8K context

Excellent

The model needs about 23 GB and fits in the 31 GB available, with 8 GB to spare.

Your hardware
RTX 5090 32 GB · 64 GB RAM
Estimated memory
~23 GB estimate
VRAM (usable memory)
31 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
188–343 tok/s estimate
Largest context in fast memory
256K tokens
Confidence: low

Based on: memory bandwidth (1,792 GB/s) and model size

Memory breakdown

~23 GB (22.2 GB–23.9 GB) needed · estimate

Fits entirely in VRAM · 8 GB left

  • Model weights20.8 GB
  • KV cache0.2 GB
  • Runtime overhead1 GB
  • Safety margin1.1 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (CUDA) · flash attention on · weights = real file size

EstimateData updated 40 days agoSource: Run LLM Locally engine, Hugging Face config.json + model card

What makes this estimate less reliable

  • no benchmark for this hardware and model yet
  • mixture-of-experts: speed depends on expert routing

Other quantizations on the RTX 5090

Qwen3.6 35B-A3B on RTX 5090 + 64 GB RAM · 8K context
Qwen3.6 35B-A3B Q8_08K~38.2 GB · 26–52 tok/s estimateCPU/GPU offload
Qwen3.6 35B-A3B Q6_K8K~31.5 GB · 107–228 tok/s estimateCPU/GPU offload
Qwen3.6 35B-A3B Q5_K_M8K~26.6 GB · 179–321 tok/s estimateCompatible
Qwen3.6 35B-A3B Q4_K_M8K~23 GB · 188–343 tok/s estimateExcellent
Qwen3.6 35B-A3B IQ4_XS8K~20.5 GB · 195–361 tok/s estimateExcellent
Qwen3.6 35B-A3B Q3_K_M8K~18 GB · 202–380 tok/s estimateExcellent

Alternatives

Where to buy a RTX 5090

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GeForce RTX 5090

32 GB GDDR7 · 1,792 GB/s · 575 W

ExcellentRuns Qwen3.6 35B-A3B Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RTX 5090 run Qwen3.6 35B-A3B?
The model needs about 23 GB and fits in the 31 GB available, with 8 GB to spare.
Which quantization should I use on the RTX 5090?
Q5_K_M is the highest-precision quantization that fits entirely in VRAM at 8K context. ~26.6 GB / 31 GB.
What is the largest context on the RTX 5090?
At Q4_K_M, about 256K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Qwen3.6 35B-A3B (RTX 5090)?
We estimate 188–343 tok/s (estimate, confidence low). Based on: memory bandwidth (1,792 GB/s) and model size.