RTX 4060 Ti 16GB LLM Performance
Local LLM Performance: 22.4 t/s average on 14B models at 16k context. Updated Benchmarks: March 2026.
22.4T/s
918T/s
Summary
The RTX 4060 Ti 16GB is a surprisingly capable mid-range GPU for local LLM inference. It handles models up to ~20B parameters in 4-bit quantization, with 22.4 t/s token generation and ~918 t/s prompt processing on 14B models at 16K context. It can even run gpt-oss 20B with up to 128K context. While its 288 GB/s memory bandwidth limits raw speed versus high-end GPUs, its efficiency and price make it one of the most accessible options for serious local LLM workloads.
Key Insights
Relative Performance (Token Generation)
Current Price in US
$400
Avg. Market Value
Current Pricing
Biggest LLMs You Can Run on This GPU
The models below represent the largest language models that fit fully in VRAM on this GPU using 4-bit quantization (GGUF). Benchmarks include token generation and prompt processing speeds measured at their maximum supported context length.
Note: Context values are grouped into standard tiers (4K, 16K, 32K, 64K, 128K). Models may support slightly higher context, but they remain in the lower tier unless they reach the next bracket.
RTX 4060 Ti 16GB local LLM Inference Performance vs Similar GPUs
Compare prompt ingestion and token generation speeds against similar GPUs across widely used local models and extended context lengths up to 256K.
Local LLM Benchmarks
Prompt processing (t/s) and token generation speed (t/s) across different open weight models and context lengths.
| Model | 4k Ctx | 16k Ctx | 32k Ctx | 64k Ctx | 128k Ctx | 256k Ctx |
|---|---|---|---|---|---|---|
| Qwen3 8B (Q4_K) | 2,675.2 | 1,480.8 | 760.3 | 392.1 | — | — |
| Qwen3 14B (Q4_K) | 1,645.7 | 917.6 | 541.4 | — | — | — |
| gpt-oss 20B (MXFP4) | 3,274.2 | 2,552.9 | 1,964.9 | 1,332.2 | 780.2 | — |
| Model | 4k Ctx | 16k Ctx | 32k Ctx | 64k Ctx | 128k Ctx | 256k Ctx |
|---|---|---|---|---|---|---|
| Qwen3 8B (Q4_K) | 45.8 | 34.3 | 25.5 | 13.0 | — | — |
| Qwen3 14B (Q4_K) | 27.4 | 22.4 | 17.9 | — | — | — |
| gpt-oss 20B (MXFP4) | 63.2 | 57.8 | 51.5 | 41.1 | 31.1 | — |
Frequently Asked Questions
Common questions about running LLMs on the RTX 4060 Ti 16GB.
What is the largest model the RTX 4060 Ti 16GB can run?
In our testing it can run models up to about 20B parameters in 4-bit quantization with full VRAM offload. Examples include gpt-oss 20B running with extended context windows.
How fast is token generation on 14B models?
We measured an average of about 22.4 tokens per second on 14B models using 4-bit quantization at around 16K context.
Can it handle long-context models?
Yes. The GPU can run models like gpt-oss 20B in MXFP4 quantization with up to 128K context while remaining fully in VRAM.
Is the RTX 4060 Ti 16GB good value for local LLM workloads?
Yes. With 16GB of VRAM and a typical price around $400, it offers strong performance for 8B–14B models and reasonable capability for 20B models, making it one of the more accessible GPUs for local LLM setups.