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Run LLM Locally

Qwen3.8 27B on the RTX 4060

Qwen3.8 27B at Q4_K_M needs about 18.2 GB, more than the 8 GB of the RTX 4060: part of the model runs from system RAM, which slows generation. At Q3_K_M it does not fit in VRAM.

Qwen3.8 27B

Q4_K_M · 8K context

Possible but slow

The model needs about 18.2 GB but only 7.7 GB of VRAM is usable: 10.5 GB must be loaded into system RAM. It runs, 42% on the GPU, but more slowly.

Your hardware
RTX 4060 8 GB · 64 GB RAM
Estimated memory
~18.2 GB estimate
VRAM (usable memory)
7.7 GB
GPU only
No
CPU/GPU offload
Required (10.5 GB)
Generation speed
1.8–3.5 tok/s estimate
Largest context in fast memory
None
Confidence: low

Based on: memory bandwidth (272 GB/s) and model size · system RAM bandwidth (~51 GB/s)

Memory breakdown

~18.2 GB (17.4 GB–18.9 GB) needed · estimate

10.5 GB must be offloaded to system RAM

  • Model weights15.7 GB
  • KV cache0.6 GB
  • Runtime overhead1 GB
  • Safety margin0.9 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
  • split CPU/GPU runs vary a lot with the CPU and PCIe link
  • system RAM speed unknown (dual-channel DDR4-3200 assumed)

Other quantizations on the RTX 4060

Qwen3.8 27B on RTX 4060 + 64 GB RAM · 8K context
Qwen3.8 27B Q8_08K~30.1 GB · 0.9–1.7 tok/s estimateNot recommended
Qwen3.8 27B Q6_K8K~23.7 GB · 1.2–2.5 tok/s estimatePossible but slow
Qwen3.8 27B Q5_K_M8K~21.1 GB · 1.4–3 tok/s estimatePossible but slow
Qwen3.8 27B Q4_K_M8K~18.2 GB · 1.8–3.5 tok/s estimatePossible but slow
Qwen3.8 27B IQ4_XS8K~16.9 GB · 2–4 tok/s estimatePossible but slow
Qwen3.8 27B Q3_K_M8K~15.3 GB · 2.5–4.5 tok/s estimatePossible but slow

Alternatives

Hardware that runs Qwen3.8 27B Q4_K_M entirely in memory

Prices and links follow the market you choose.

Radeon RX 7900 XT

AMD

CompatibleRuns Qwen3.8 27B Q4_K_M entirely in memory

Price not available

Availability unknown

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AMD Ryzen AI Max+ 395

Unified memory PC

CompatibleRuns Qwen3.8 27B Q4_K_M entirely in memory

Price not available

Availability unknown

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No current offer imported for this market: the link opens a marketplace search.

Frequently asked questions

Can the RTX 4060 run Qwen3.8 27B?
The model needs about 18.2 GB but only 7.7 GB of VRAM is usable: 10.5 GB must be loaded into system RAM. It runs, 42% on the GPU, but more slowly.
Which quantization should I use on the RTX 4060?
None fits entirely in VRAM at 8K context; the smallest, Q3_K_M, still needs offload.
What is the largest context on the RTX 4060?
At Q4_K_M, the model does not fit entirely in VRAM even at a short context.
How fast is Qwen3.8 27B (RTX 4060)?
We estimate 1.8–3.5 tok/s (estimate, confidence low). Based on: memory bandwidth (272 GB/s) and model size, system RAM bandwidth (~51 GB/s).