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Run LLM Locally

Mistral 7B Instruct v0.3 on the RTX 4060 Ti 8 GB

With 8 GB of VRAM, the RTX 4060 Ti 8 GB runs Mistral 7B Instruct v0.3 at Q4_K_M entirely on the GPU with an 8K context (about 5.9 GB needed). At Q8_0 it does not fit in VRAM.

Mistral 7B Instruct v0.3

Q4_K_M · 8K context

Excellent

The model needs about 5.9 GB and fits in the 7.7 GB available, with 1.7 GB to spare.

Your hardware
RTX 4060 Ti 8 GB · 64 GB RAM
Estimated memory
~5.9 GB estimate
VRAM (usable memory)
7.7 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
42–56 tok/s estimate
Largest context in fast memory
21K tokens
Confidence: low

Based on: memory bandwidth (288 GB/s) and model size

Memory breakdown

~5.9 GB (5.6 GB–6.3 GB) needed · estimate

Fits entirely in VRAM · 1.7 GB left

  • Model weights4.1 GB
  • KV cache1 GB
  • Runtime overhead0.6 GB
  • Safety margin0.3 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

Other quantizations on the RTX 4060 Ti 8 GB

Mistral 7B Instruct v0.3 on RTX 4060 Ti 8 GB + 64 GB RAM · 8K context
Mistral 7B Instruct v0.3 Q8_08K~9.2 GB · 9–17 tok/s estimateCPU/GPU offload
Mistral 7B Instruct v0.3 Q6_K8K~7.5 GB · 32–43 tok/s estimateCompatible with limits
Mistral 7B Instruct v0.3 Q5_K_M8K~6.7 GB · 36–49 tok/s estimateCompatible
Mistral 7B Instruct v0.3 Q4_K_M8K~5.9 GB · 42–56 tok/s estimateExcellent
Mistral 7B Instruct v0.3 IQ4_XS8K~5.5 GB · 46–61 tok/s estimateExcellent
Mistral 7B Instruct v0.3 Q3_K_M8K~5.2 GB · 50–67 tok/s estimateExcellent

Alternatives

Where to buy a RTX 4060 Ti 8 GB

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GeForce RTX 4060 Ti 8 GB

8 GB GDDR6 · 288 GB/s · 160 W

ExcellentRuns Mistral 7B Instruct v0.3 Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RTX 4060 Ti 8 GB run Mistral 7B Instruct v0.3?
The model needs about 5.9 GB and fits in the 7.7 GB available, with 1.7 GB to spare.
Which quantization should I use on the RTX 4060 Ti 8 GB?
Q6_K is the highest-precision quantization that fits entirely in VRAM at 8K context. ~7.5 GB / 7.7 GB.
What is the largest context on the RTX 4060 Ti 8 GB?
At Q4_K_M, about 21K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Mistral 7B Instruct v0.3 (RTX 4060 Ti 8 GB)?
We estimate 42–56 tok/s (estimate, confidence low). Based on: memory bandwidth (288 GB/s) and model size.