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⇱ RTX 4060 TI 16GB Local LLM Benchmarks, Context Scaling & Supported Models 2026 – Hardware Corner

Tier 1 Enthusiast

RTX 4060 Ti 16GB LLM Performance

Local LLM Performance: 22.4 t/s average on 14B models at 16k context. Updated Benchmarks: March 2026.

Gen (14B 4-bit) 22.4 t/s
PP (14B 4-bit) 918 t/s
Max Model 20B
VRAM
16 GB GDDR6
Bandwidth 288 GB/s
Token Gen (14B @ 4k Ctx)

22.4T/s

Prompt Proc (14B @ 4k Ctx)

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

16GB of VRAM allows full VRAM offload for models up to ~20B parameters using 4-bit quantization.
Achieves around 22.4 tokens/sec generation on 14B models at 16K context, making it a strong mid-range option for local inference.
Handles long-context workloads effectively, running gpt-oss 20B (MXFP4) with up to 128K context fully in VRAM.
Prompt processing reaches ~918 tokens/sec on 14B models, enabling fast document ingestion and large prompt workflows.
Memory bandwidth of 288 GB/s limits performance compared to higher-tier GPUs but remains sufficient for smooth 8B–14B model usage.

Current Price in US

$400

Avg. Market Value

Current Pricing

Hardware Specs
VRAM 16GB GDDR6
Capable of running 20B model
Bandwidth 288 GB/s
Architecture Ada Lovelace
Memory speed 18 Gbps
Memory bus 128 bit
TDP 165 W
Suggested PSU 550 W
Price/GB VRAM $25.00
Price/(t/s) with 14B @ 16k $17.89

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.

gpt-oss 20B (MXFP4) Max 128k
Token Generation 31.1 t/s @ 128k context
Prompt Processing 780.2 t/s @ 128k context
Qwen3 14B (Q4_K) Max 32k
Token Generation 17.9 t/s @ 32k context
Prompt Processing 541.4 t/s @ 32k context
Qwen3 8B (Q4_K) Max 64k
Token Generation 13.0 t/s @ 64k context
Prompt Processing 392.1 t/s @ 64k context

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.

Prompt Processing
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 —
Token Generation
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.