Yes, the RTX 4060 Ti can run the DeepSeek R1 7B distilled model in INT8 quantisation with a usable context window. With 16GB GDDR6 VRAM, the RTX 4060 Ti is the entry point for meaningful DeepSeek hosting, though it cannot handle the larger distilled variants or the full 671B model.
The Short Answer
YES for DeepSeek R1 7B distilled (INT8/INT4). YES for 1.5B (FP16). NO for 14B+ variants.
The RTX 4060 Ti with 16GB VRAM slots neatly into the gap where the 7B distilled model fits in INT8 quantisation. At roughly 7.5GB for model weights in INT8, you have about 8.5GB remaining for KV cache, enabling a context window of approximately 8192 tokens. In INT4, the model drops to 4.5GB for weights, freeing even more room for longer contexts.
The 14B distilled variant needs about 15GB in INT8 for weights alone, which exceeds the budget before any context allocation. This card is firmly in the 7B territory for DeepSeek.
VRAM Analysis
| Model Variant | FP16 VRAM | INT8 VRAM | INT4 VRAM | RTX 4060 Ti (16GB) |
|---|---|---|---|---|
| DeepSeek R1 1.5B | ~3.2GB | ~1.8GB | ~1.2GB | Fits (FP16) |
| DeepSeek R1 7B | ~14GB | ~7.5GB | ~4.5GB | INT8 or INT4 |
| DeepSeek R1 14B | ~28GB | ~15GB | ~8.5GB | INT4 only, tight |
| DeepSeek R1 32B | ~64GB | ~34GB | ~18GB | No |
| DeepSeek R1 671B | ~1.3TB | ~670GB | ~340GB | No |
The 14B variant in INT4 (8.5GB weights) could technically load but leaves only 7.5GB for KV cache, limiting context to about 4096 tokens. For DeepSeek’s extended reasoning chains, this is restrictive. Check our DeepSeek VRAM requirements guide for the complete picture.
Performance Benchmarks
| Configuration | GPU | Tokens/sec (output) | Max Context |
|---|---|---|---|
| R1 7B INT8 | RTX 4060 Ti (16GB) | ~22 tok/s | 8192 |
| R1 7B Q4_K_M | RTX 4060 Ti (16GB) | ~32 tok/s | 16384 |
| R1 1.5B FP16 | RTX 4060 Ti (16GB) | ~55 tok/s | 16384 |
| R1 7B FP16 | RTX 3090 (24GB) | ~35 tok/s | 32768 |
| R1 7B INT4 | RTX 4060 (8GB) | ~15 tok/s | ~3072 |
At 22 tok/s in INT8, the RTX 4060 Ti delivers responsive inference for the 7B model. The INT4 quantisation bumps this to 32 tok/s with slightly lower quality but longer available context. Both are above the threshold for comfortable interactive use. View detailed comparisons on our benchmarks page.
Setup Guide
Deploy DeepSeek R1 7B on the RTX 4060 Ti with Ollama or vLLM:
# Ollama: Quick setup with INT8
ollama run deepseek-r1:7b-q8_0
# vLLM: Production serving with AWQ quantisation
pip install vllm
vllm serve deepseek-ai/DeepSeek-R1-Distill-Qwen-7B \
--quantization awq \
--max-model-len 8192 \
--gpu-memory-utilization 0.90 \
--host 0.0.0.0 --port 8000The vLLM option gives you an OpenAI-compatible API with continuous batching. For Ollama, the Q8_0 quantisation maintains high quality while staying well within the 16GB budget. Monitor VRAM with nvidia-smi to verify you have headroom for your target context length.
Recommended Alternative
If you want DeepSeek 7B in full FP16 with maximum context, the RTX 3090 with 24GB is the upgrade that unlocks 32K context at 35+ tok/s. For the 14B and 32B distilled variants, you need multi-GPU setups available through our dedicated GPU servers.
For other workloads on the 4060 Ti, see whether it can run SDXL or run LLaMA 3 8B. If you are comparing against the base RTX 4060, our RTX 4060 DeepSeek analysis shows why the extra 8GB matters. For newer hardware options, check the RTX 5080 DeepSeek analysis. Our best GPU for LLM inference guide covers the full landscape.
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