Best AI Models for NVIDIA GeForce RTX 4080 (16.0GB)
16 GB is a comfortable mid-range tier for local AI. Most 7B–13B models run smoothly at good quantization levels, and smaller models can run at near-full precision.
This memory tier strikes a nice balance between price and capability. Popular 7B models like Llama 3 8B, Mistral 7B, and Qwen 2.5 7B all run very well at Q4_K_M quantization with fast inference and reasonable context windows. You can also fit some larger 13B models at Q3–Q4, though you'll want to keep context lengths modest. Small models like Phi 3 Mini (3.8B) practically fly at Q8 or even FP16 quality.
Runs Well
- 7B models at Q4–Q6 quality with good speed
- Small models (3B–4B) at Q8 or FP16
- 9B models (Gemma 2 9B) at Q4_K_M
Challenging
- 13B–14B models need Q3 or lower
- 30B+ models do not fit in VRAM
- Long context (>8K tokens) with larger models
What LLMs Can NVIDIA GeForce RTX 4080 Run?
229 models · 148 excellent · 20 good
Showing compatibility for NVIDIA GeForce RTX 4080
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·135.8 t/s tok/s·131K ctx·RUNS GREAT | Q4_K_M | 3.4 GB | 135.8 t/s | 131K | RUNS GREAT | S96 |
Q4_K_M·228.4 t/s tok/s·131K ctx·RUNS GREAT | Q4_K_M | 2.0 GB | 228.4 t/s | 131K | RUNS GREAT | S100 |
Q4_K_M·74.8 t/s tok/s·41K ctx·RUNS GREAT | Q4_K_M | 6.2 GB | 74.8 t/s | 41K | RUNS GREAT | S86 |
Q4_K_M·75.0 t/s tok/s·262K ctx·RUNS GREAT | Q4_K_M | 6.2 GB | 75.0 t/s | 262K | RUNS GREAT | S86 |
Q4_K_M·245.2 t/s tok/s·131K ctx·RUNS GREAT | Q4_K_M | 1.9 GB | 245.2 t/s | 131K | RUNS GREAT | S100 |
Q4_K_M·263.2 t/s tok/s·262K ctx·RUNS GREAT | Q4_K_M | 1.8 GB | 263.2 t/s | 262K | RUNS GREAT | S100 |
Q4_K_M·80.3 t/s tok/s·37K ctx·RUNS GREAT | Q4_K_M | 5.8 GB | 80.3 t/s | 37K | RUNS GREAT | S88 |
Q4_K_M·139.9 t/s tok/s·33K ctx·RUNS GREAT | Q4_K_M | 3.3 GB | 139.9 t/s | 33K | RUNS GREAT | S96 |
Q4_K_M·162.3 t/s tok/s·131K ctx·RUNS GREAT | Q4_K_M | 2.9 GB | 162.3 t/s | 131K | RUNS GREAT | S97 |
Q4_K_M·213.7 t/s tok/s·131K ctx·RUNS GREAT | Q4_K_M | 2.2 GB | 213.7 t/s | 131K | RUNS GREAT | S100 |
Q4_K_M·396.8 t/s tok/s·8K ctx·RUNS GREAT | Q4_K_M | 2.5 GB | 396.8 t/s | 8K | RUNS GREAT | S100 |
Q4_K_M·72.1 t/s tok/s·RUNS GREAT | Q4_K_M | 6.5 GB | 72.1 t/s | — | RUNS GREAT | S86 |
Q3_K_S·137.8 t/s tok/s·203K ctx·RUNS WELL | Q3_K_S | 14.8 GB | 137.8 t/s | 203K | RUNS WELL | A79 |
Q4_K_M·162.7 t/s tok/s·131K ctx·RUNS GREAT | Q4_K_M | 10.6 GB | 162.7 t/s | 131K | RUNS GREAT | S97 |
Q4_K_M·80.5 t/s tok/s·RUNS GREAT | Q4_K_M | 5.8 GB | 80.5 t/s | — | RUNS GREAT | S88 |
Q4_K_M·78.4 t/s tok/s·262K ctx·RUNS GREAT | Q4_K_M | 5.9 GB | 78.4 t/s | 262K | RUNS GREAT | S87 |
NVIDIA GeForce RTX 4080 Specifications
- Brand
- NVIDIA
- Architecture
- Ada Lovelace
- Compute Capability
- 8.9 (CUDA SM version)
- VRAM
- 16.0 GB GDDR6X
- Memory Bandwidth
- 716.8 GB/s
- CUDA Cores
- 9,728
- Tensor Cores
- 304
- FP16 Performance
- 97.50 TFLOPS
- TDP
- 320W
- Release Date
- 2022-11-16
- MSRP
- $1,199
Get Started
Prompt Processing
Estimated for GPT OSS 20B NPU2, the compute-bound phase that reads your prompt before the first reply token appears.
Short chat
77 ms
512 tok prompt
Long chat
612 ms
4,096 tok prompt
Document / codebase
4.9 s
32,768 tok prompt
Prefill is compute-bound and a different number from the decode tok/s shown elsewhere on this page — how prompt processing works →
Efficiency & Value
Based on GPT OSS 20B NPU2 at ~176.3 tok/s decode.
Tokens per watt
Higher is better.
$ per tok/s (MSRP)
MSRP-based, not street price. Lower is better.
Performance figures are estimates calibrated as of 2026-09-21 — see calibration basis →
GPUs to Consider Over NVIDIA GeForce RTX 4080
Similar GPUs and upgrades with more VRAM or higher bandwidth for AI
NVIDIA GeForce RTX 5090
NVIDIA · Blackwell
NVIDIA GeForce RTX 3090 Ti
NVIDIA · Ampere
NVIDIA GeForce RTX 4090
NVIDIA · Ada Lovelace
AMD Radeon RX 7900 XTX
AMD · RDNA 3
NVIDIA GeForce RTX 5080
NVIDIA · Blackwell
NVIDIA GeForce RTX 3090
NVIDIA · Ampere
Frequently Asked Questions
- Can NVIDIA GeForce RTX 4080 run Gemma 4 26B A4B IT?
Yes, the NVIDIA GeForce RTX 4080 with 16 GB can run Gemma 4 26B A4B IT, Mellum2 12B A2.5B Instruct, Qwen3.5 9B, and 2190 other models. 1584 models run at excellent quality, and 264 at good quality. Check the compatibility table above for the full list with VRAM usage and estimated speed.
- Is NVIDIA GeForce RTX 4080 good for AI?
The NVIDIA GeForce RTX 4080 has 16 GB of GDDR6X, making it very good for running local AI models. It supports 1848 models at good quality or better. With 716.8 GB/s memory bandwidth, it delivers solid token generation speeds. This is a solid mid-range card for running 7B–14B parameter models at good quality.
- How many parameters can NVIDIA GeForce RTX 4080 handle?
With 16 GB, the NVIDIA GeForce RTX 4080 supports models from 3B to 14B parameters depending on quantization level. At Q4_K_M (the recommended sweet spot), you can fit roughly 26B parameters. 7B models run at high quality (Q5/Q6), while 14B models fit comfortably at Q4.
- What quantization should I use on NVIDIA GeForce RTX 4080?
For the best balance of quality and speed on the NVIDIA GeForce RTX 4080, start with Q4_K_M — it preserves ~85% of the original model quality while keeping VRAM usage reasonable. You can step up to Q5_K_M for 7B models to get better quality. For 14B models that just barely fit, Q4_K_M is ideal.
- How fast is NVIDIA GeForce RTX 4080 for AI inference?
With 716.8 GB/s memory bandwidth, the NVIDIA GeForce RTX 4080 achieves approximately 104 tokens/sec on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~52 tok/s. Token generation speed scales inversely with model size — smaller models are significantly faster.
tok/s = (716.8 GB/s ÷ model GB) × efficiency
Smaller models = faster inference. Memory bandwidth is the main bottleneck for token generation speed.
Estimated speed on NVIDIA GeForce RTX 4080
~165 tok/s~207 tok/s~73 tok/s~357 tok/sReal-world results typically within ±20%. Speed depends on quantization kernel, batch size, and software stack.
- What's the best model for NVIDIA GeForce RTX 4080?
The top-rated models for the NVIDIA GeForce RTX 4080 are Gemma 4 26B A4B IT, Mellum2 12B A2.5B Instruct, Qwen3.5 9B. The best choice depends on your use case: coding assistants benefit from code-tuned models, while general chat works well with instruction-tuned models like Llama or Qwen.
- What power supply and cooling does NVIDIA GeForce RTX 4080 need?
The NVIDIA GeForce RTX 4080 has a TDP of 320 W. A good rule of thumb is to provide at least double the GPU's TDP to cover the rest of the system — that means a 650 W PSU or larger. At this power level, a high-airflow case matters: aim for at least two front intake fans and one rear exhaust, with tidy cabling so hot air isn't trapped around the card. LLM inference sustains full GPU load continuously — longer and more consistently than most gaming workloads — so also make sure your CPU cooler can keep up under combined load.