RTX 4080 LLM Performance
Local LLM Performance: 51.2 t/s average on 14B models at 16k context.
51.2T/s
2,295T/s
Summary
In our testing, the NVIDIA GeForce RTX 4080 performed very well for local LLM inference. Its 16GB VRAM and 716 GB/s bandwidth allowed us to run models up to ~20B parameters in 4-bit quantization fully in VRAM, achieving about 51.2 tokens/s generation and 2,200+ tokens/s prompt processing on Qwen3 14B at 16K context. However, the value is questionable at full price. Compared with newer 16GB GPUs like the NVIDIA GeForce RTX 5060 Ti, the real-world performance difference in token generation and prompt processing is relatively small. It becomes a good option mainly if found at a good second-hand price.
Key Insights
Relative Performance (Token Generation)
Current Price in US
$887
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 4080 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) | 6,177.9 | 3,809.8 | 1,968.4 | 937.9 | — | — |
| Qwen3 14B (Q4_K) | 3,574.7 | 2,295.1 | 1,395.7 | — | — | — |
| gpt-oss 20B (MXFP4) | 6,218.5 | 4,329.2 | 2,831.1 | 1,559.0 | 898.5 | — |
| Model | 4k Ctx | 16k Ctx | 32k Ctx | 64k Ctx | 128k Ctx | 256k Ctx |
|---|---|---|---|---|---|---|
| Qwen3 8B (Q4_K) | 102.7 | 77.9 | 59.0 | 39.0 | — | — |
| Qwen3 14B (Q4_K) | 62.0 | 51.2 | 40.6 | — | — | — |
| gpt-oss 20B (MXFP4) | 136.5 | 120.2 | 106.0 | 81.3 | 60.0 | — |
Frequently Asked Questions
Common questions about running LLMs on the RTX 4080.
What is the largest model the RTX 4080 can run fully in VRAM?
With 16GB of VRAM, the RTX 4080 can run models up to roughly 20B parameters in 4-bit quantization fully offloaded to the GPU.
How fast is token generation on the RTX 4080?
In our testing with Qwen3 14B at 16K context using Q4_K quantization, the RTX 4080 produced around 51.2 tokens per second.
How good is prompt processing performance?
Prompt ingestion is one of the strong points of the RTX 4080. On Qwen3 14B at 16K context we measured about 2,295 tokens per second.
Is the RTX 4080 worth buying for local LLM workloads?
It can be a strong option if purchased at the right price, especially on the second-hand market. However, newer GPUs with the same 16GB VRAM capacity sometimes offer similar real-world LLM performance at a lower cost.
Can the RTX 4080 run 30B or 32B models?
No, the RTX 4080 cannot run 30B or 32B models fully in VRAM using typical 4-bit quantization due to its 16GB memory limit. However, if you lower the quantization to 3-bit, it may be possible to load the model, though the output quality will noticeably degrade.