RTX 3090 LLM Performance
Local LLM Performance: 52.1 t/s average on 14B models at 16k context. Updated Benchmarks: March 2026.
52.1T/s
1,679T/s
Summary
Even in 2026, we find the NVIDIA RTX 3090 remains a powerhouse for local LLM workloads. In our testing, its 24GB of VRAM and high memory bandwidth allowed us to comfortably run 35B parameter models using 4-bit quantization (Q4). Based on our market analysis, we consider it the most cost-effective second-hand entry point for anyone serious about local LLM work.
Key Insights
Relative Performance (Token Generation)
Current Price in US
$1,000
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 3090 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)
CUDA, -fa 1
|
4,049.6 | 2,572.5 | 1,714.6 | 1,014.3 | 570.0 | — |
|
Qwen3 14B (Q4_K)
CUDA, -fa 1
|
2,459.0 | 1,678.7 | 1,175.7 | 734.1 | — | — |
|
gpt-oss 20B (MXFP4)
CUDA, -fa 1
|
4,400.3 | 3,243.6 | 2,547.2 | 1,720.6 | 923.8 | — |
| Gemma4 26B (Q4_K) | 3,625.6 | 3,068.9 | 2,453.4 | 1,765.1 | 1,147.1 | 671.4 |
| Qwen3.5 27B (Q4_K) | 1,104.2 | 977.4 | 848.2 | 678.9 | — | — |
|
Qwen3 30B A3B (Q4_K)
CUDA, -fa 1
|
2,988.6 | 1,959.0 | 1,336.8 | 800.9 | — | — |
| Gemma4 31B (Q4_K) | 1,155.8 | 913.2 | 723.7 | — | — | — |
|
Qwen3 32B (Q4_K)
CUDA, -fa 1
|
1,087.9 | 767.8 | — | — | — | — |
| Qwen3.5 35B (MXFP4) | 2,622.1 | 2,381.3 | 2,121.6 | 1,749.8 | 1,288.9 | — |
| Model | 4k Ctx | 16k Ctx | 32k Ctx | 64k Ctx | 128k Ctx | 256k Ctx |
|---|---|---|---|---|---|---|
|
Qwen3 8B (Q4_K)
CUDA, -fa 1
|
115.3 | 87.5 | 67.9 | 46.6 | 28.1 | — |
|
Qwen3 14B (Q4_K)
CUDA, -fa 1
|
70.0 | 52.1 | 38.6 | 25.4 | — | — |
|
gpt-oss 20B (MXFP4)
CUDA, -fa 1
|
147.5 | 128.5 | 112.6 | 89.6 | 62.2 | — |
| Gemma4 26B (Q4_K) | 119.4 | 115.0 | 107.5 | 98.9 | 83.0 | 64.4 |
| Qwen3.5 27B (Q4_K) | 33.5 | 32.3 | 31.0 | 28.8 | — | — |
|
Qwen3 30B A3B (Q4_K)
CUDA, -fa 1
|
153.6 | 113.8 | 87.2 | 58.3 | — | — |
| Gemma4 31B (Q4_K) | 34.7 | 33.5 | 31.4 | — | — | — |
|
Qwen3 32B (Q4_K)
CUDA, -fa 1
|
35.1 | 30.3 | — | — | — | — |
| Qwen3.5 35B (MXFP4) | 111.2 | 107.1 | 101.2 | 93.1 | 79.4 | — |
Frequently Asked Questions
Common questions about running LLMs on the RTX 3090.
Can the RTX 3090 run large language models locally?
Yes. The NVIDIA RTX 3090 is one of the most capable consumer GPUs for local LLM inference thanks to its 24 GB VRAM and 986 GB/s memory bandwidth.
- 7B–14B models at high speeds
- 30B–34B models fully in VRAM using Q4 quantization
- Larger models with CPU offloading or multi-GPU setups
What is the largest LLM that fits entirely in RTX 3090 VRAM?
With 24 GB VRAM, the largest models that typically fit fully in VRAM are around 30B–34B parameters using 4-bit quantization. Examples: - Qwen3 32B (Q4_K) - Qwen3 30B A3B (Q4_K) These models can run fully on the RTX 3090 without CPU offloading.
Can the RTX 3090 run 70B models?
Yes, but not fully in VRAM. A 70B parameter model typically requires 48–80 GB VRAM depending on quantization. On a single RTX 3090 you can: - run 70B models with CPU offloading - use multi-GPU setups - use aggressive quantization formats However, generation speed will usually drop to single-digit tokens per second.
What is the recommended power supply?
A minimum of 750W Gold is recommended for a single card, or 1000W+ if running with a high-end CPU.
Is the RTX 3090 still good for local LLMs in 2026?
Yes. Even in 2026 the RTX 3090 remains one of the best value GPUs for local LLM workloads, especially on the second-hand market. Key reasons include its 24 GB of VRAM, very high memory bandwidth, and strong CUDA support across modern inference frameworks. Because of this, many enthusiasts still build multi-RTX 3090 AI servers for local model inference.
Does the RTX 3090 support Flash Attention?
Yes. The RTX 3090 supports Flash Attention 2, which improves prompt processing performance and reduces memory overhead in supported inference frameworks. This can significantly accelerate long-context workloads when the inference stack supports it.
Which frameworks run best on the RTX 3090 for LLMs?
Popular inference frameworks for RTX 3090 systems include llama.cpp, Ollama, vLLM, and ExLlamaV2. For GGUF models specifically, llama.cpp with CUDA acceleration often delivers the best performance and efficiency on RTX 3090 hardware.
What power supply is recommended for an RTX 3090 LLM workstation?
The RTX 3090 can draw around 350 W or more under heavy inference workloads. For a single-GPU workstation an 850 W power supply is typically recommended, preferably from a high-quality brand. Systems with powerful CPUs or multiple GPUs should include additional headroom. Multi-RTX 3090 setups commonly require power supplies in the 1200 W to 1600 W range.
Is the RTX 3090 good for long-context models?
Yes. Thanks to its large 24 GB VRAM and high memory bandwidth, the RTX 3090 performs well with extended context lengths. In testing it can handle models such as gpt-oss 20B at around 128k context and many 30B models at around 64k context. Prompt processing performance remains strong even with large context sizes.
Is the RTX 3090 a good used GPU for LLM enthusiasts?
Yes. The RTX 3090 is widely considered one of the best second-hand GPUs for local AI workloads. Its large 24 GB VRAM, mature CUDA ecosystem, and strong inference performance per dollar make it a popular entry point for enthusiasts building local LLM systems.