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NVIDIA RTX 2080 Ti

NVIDIA · 11GB · 2 reports

Engines people use on it: llama.cpp 1 (reports)

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Reported performance on the NVIDIA RTX 2080 Ti

These reports mix GPU counts and memory setups, so a model listed here may have needed extra cards or CPU offloading. The calculator estimates which models fit in 11 GB of VRAM.

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Date ↓Date ↑Token speed ↓Token speed ↑Prefill ↓Prefill ↑Context ↓Context ↑Ranked

Qwen3 14B

2× NVIDIA RTX 2080 Ti · llama.cpp · 32,768 ctx

NVIDIA hardware
generation:
68.1 tokens/s
prompt processing (prefill):
3057.0 tokens/s
quant:
Q4_K_M (GGUF)

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

codingtool-usevisionagentic

User reports Qwen3-14B at 68.1 t/s generation and 3057 t/s prompt processing on a 2x RTX 2080 Ti node. Setup is llama.cpp with Q4_K_M quant, 32768 context, and a Qwen3-0.6B-Q8_0 draft model for speculative decoding with depth 6. The user also reports Pixtral-12B at 77.0 t/s generation and 2862 t/s prompt processing, and a distributed 4-stream aggregate of 118.68 t/s. They ask if Linux would give better t/s and mention P2P is working on Windows.

Oct 14, 2026
Tone: positiveNVIDIA hardware
prompt processing (prefill):
255.0 tokens/s
quant:
W8A8

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is input throughput, not time to first token. Check the full setup before comparing.

User reports custom Turing CUDA kernels for W8A8 INT8 matmul, heterogeneous inference with 4x 11/22GB VRAM and 1TB system RAM, and computation-communication overlap for MoE routing. The work is open-sourced on GitHub.

Aug 28, 2026

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Reported configurations

Individual observations from this page, not expected speeds or a ranking. Select a model to open its report, with the source and offloading conditions. Reported context may be a limit rather than actual input length.

Model / hardwareQuant / engineReported contextGeneration
Qwen3 14B
2× NVIDIA RTX 2080 Ti
Q4_K_M
llama.cpp
32,76868.1 tokens/s