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Run LLM Locally

Llama 3.3 70B Instruct on the RTX 5060 Ti 16 GB

Llama 3.3 70B Instruct at Q4_K_M needs about 45.1 GB, more than the 16 GB of the RTX 5060 Ti 16 GB: part of the model runs from system RAM, which slows generation. At Q3_K_M it does not fit in VRAM.

Llama 3.3 70B Instruct

Q4_K_M · 8K context

Not recommended

The model needs about 45.1 GB but only 15.5 GB of VRAM is usable: 29.5 GB must be loaded into system RAM. It runs, 34% on the GPU, but more slowly.

Estimated speed is at most 1.3 tokens/s: too slow to be usable for chat.

Your hardware
RTX 5060 Ti 16 GB · 64 GB RAM
Estimated memory
~45.1 GB estimate
VRAM (usable memory)
15.5 GB
GPU only
No
CPU/GPU offload
Required (29.5 GB)
Generation speed
0.7–1.3 tok/s estimate
Largest context in fast memory
None
Confidence: low

Based on: memory bandwidth (448 GB/s) and model size · system RAM bandwidth (~51 GB/s)

Memory breakdown

~45.1 GB (43.9 GB–46.3 GB) needed · estimate

29.5 GB must be offloaded to system RAM

  • Model weights39.6 GB
  • KV cache2.5 GB
  • Runtime overhead0.8 GB
  • Safety margin2.1 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (CUDA) · flash attention on · weights = real file size

EstimateData updated 24 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 5060 Ti 16 GB

Llama 3.3 70B Instruct on RTX 5060 Ti 16 GB + 64 GB RAM · 8K context
Llama 3.3 70B Instruct Q8_08K~76.8 GB · No dataIncompatible
Llama 3.3 70B Instruct Q6_K8K~60.1 GB · 0.4–0.8 tok/s estimateNot recommended
Llama 3.3 70B Instruct Q5_K_M8K~52.3 GB · 0.5–1 tok/s estimateNot recommended
Llama 3.3 70B Instruct Q4_K_M8K~45.1 GB · 0.7–1.3 tok/s estimateNot recommended
Llama 3.3 70B Instruct IQ4_XS8K~40.6 GB · 0.8–1.5 tok/s estimateNot recommended
Llama 3.3 70B Instruct Q3_K_M8K~37 GB · 0.9–1.7 tok/s estimateNot recommended

Alternatives

Hardware that runs Llama 3.3 70B Instruct Q4_K_M entirely in memory

Prices and links follow the market you choose.

RTX 6000 Ada Generation

NVIDIA

CompatibleRuns Llama 3.3 70B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

Radeon PRO W7900

AMD

CompatibleRuns Llama 3.3 70B 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 Llama 3.3 70B 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 5060 Ti 16 GB run Llama 3.3 70B Instruct?
The model needs about 45.1 GB but only 15.5 GB of VRAM is usable: 29.5 GB must be loaded into system RAM. It runs, 34% on the GPU, but more slowly. Estimated speed is at most 1.3 tokens/s: too slow to be usable for chat.
Which quantization should I use on the RTX 5060 Ti 16 GB?
None fits entirely in VRAM at 8K context; the smallest, Q3_K_M, still needs offload.
What is the largest context on the RTX 5060 Ti 16 GB?
At Q4_K_M, the model does not fit entirely in VRAM even at a short context.
How fast is Llama 3.3 70B Instruct (RTX 5060 Ti 16 GB)?
We estimate 0.7–1.3 tok/s (estimate, confidence low). Based on: memory bandwidth (448 GB/s) and model size, system RAM bandwidth (~51 GB/s).