Skip to content
Run LLM Locally

Olmo 3.1 32B Instruct on the RTX 4060

Olmo 3.1 32B Instruct at Q4_K_M needs about 21.1 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.

ShareFacebookXWhatsAppLinkedInEmail

Olmo 3.1 32B Instruct

Q4_K_M · 8K context

Possible but slow

The model needs about 21.1 GB but only 7.7 GB of VRAM is usable: 13.5 GB must be loaded into system RAM. It runs, 36% on the GPU, but more slowly.

Your hardware
RTX 4060 8 GB · 64 GB RAM
Estimated memory
~21.1 GB estimate
VRAM (usable memory)
7.7 GB
GPU only
No
CPU/GPU offload
Required (13.5 GB)
Generation speed
1.4–3 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

~21.1 GB (20.4 GB–21.8 GB) needed · estimate

13.5 GB must be offloaded to system RAM

  • Model weights18.1 GB
  • KV cache1.3 GB
  • Runtime overhead0.7 GB
  • Safety margin1 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (CUDA) · flash attention on · weights = real file size · sliding-window layers keep 4,096 tokens

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

Olmo 3.1 32B Instruct on RTX 4060 + 64 GB RAM · 8K context
Olmo 3.1 32B Instruct Q8_08K~35.6 GB · 0.7–1.3 tok/s estimateNot recommended
Olmo 3.1 32B Instruct Q6_K8K~27.9 GB · 1–1.8 tok/s estimateNot recommended
Olmo 3.1 32B Instruct Q5_K_M8K~24.4 GB · 1.2–2 tok/s estimatePossible but slow
Olmo 3.1 32B Instruct Q4_K_M8K~21.1 GB · 1.4–3 tok/s estimatePossible but slow
Olmo 3.1 32B Instruct IQ4_XS8K~19 GB · 1.7–3 tok/s estimatePossible but slow
Olmo 3.1 32B Instruct Q3_K_M8K~17.3 GB · 2–4 tok/s estimatePossible but slow

Alternatives

GPUs that run Olmo 3.1 32B Instruct Q4_K_M entirely in VRAM

Other models that fit the RTX 4060

Hardware that runs Olmo 3.1 32B Instruct Q4_K_M entirely in memory

Prices and links follow the market you choose.

Radeon RX 7900 XTX

AMD

CompatibleRuns Olmo 3.1 32B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

MacBook Pro 14 M3 Pro

Apple Silicon

CompatibleRuns Olmo 3.1 32B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

AMD Ryzen AI Max+ 395

Unified memory PC

CompatibleRuns Olmo 3.1 32B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

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

Frequently asked questions

Can the RTX 4060 run Olmo 3.1 32B Instruct?
The model needs about 21.1 GB but only 7.7 GB of VRAM is usable: 13.5 GB must be loaded into system RAM. It runs, 36% 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 Olmo 3.1 32B Instruct (RTX 4060)?
We estimate 1.4–3 tok/s (estimate, confidence low). Based on: memory bandwidth (272 GB/s) and model size, system RAM bandwidth (~51 GB/s).