RTX 3090 vs RTX 4090 for Local 27B LLM Inference

The RTX 4090 is roughly 40–60% faster than the RTX 3090 for local 27B LLM inference, and both cards can run a 27B model with 4-bit quantization in VRAM. The 4090’s higher memory bandwidth (1,008 GB/s vs 936 GB/s) and much faster compute cores make it the better value if you generate tokens frequently, but the 3090 is the budget pick if you can tolerate slower speeds and find one used.

Background: Why 27B Models Are a Sweet Spot

27B parameter models (like Llama-3-8B’s larger cousins or Mistral-based 27B variants) sit between the 7B/8B models that run on almost anything and the 70B+ models that need multiple GPUs or heavy quantization. For a single consumer GPU, 27B is the largest size that fits comfortably in 24GB VRAM when quantized to 4-bit (about 14–16GB total). This makes it a popular choice for local coding assistants, roleplay, and general chat without cloud dependencies.

VRAM: Both Have 24GB, But That’s Not the Full Story

Both the RTX 3090 and RTX 4090 have 24GB of GDDR6X VRAM, so a 27B model with 4-bit quantization (e.g., Q4_K_M) fits on either card. However, the 4090 uses faster GDDR6X with a 384-bit bus, giving 1,008 GB/s bandwidth versus the 3090’s 936 GB/s. That 7.7% bandwidth advantage matters less than the compute difference, but it still helps with prompt processing and long context windows.

If you want to run the model at 8-bit quantization (about 27–30GB), neither card fits it alone; you’d need to offload layers to system RAM, which drastically slows inference. For 4-bit, both cards work, but the 4090’s higher bandwidth reduces the time spent moving weights from VRAM to the compute units.

Inference Speed: Tokens per Second Compared

Token generation speed (the rate at which the model outputs text) is mostly limited by memory bandwidth, because autoregressive generation is memory-bound. The 4090’s 1,008 GB/s vs the 3090’s 936 GB/s gives it a theoretical ~7.7% advantage in raw token throughput, but in practice, the 4090 often achieves 20–30% higher tokens per second due to better memory latency and driver optimizations.

Prompt processing (the initial pass that reads your input) is more compute-bound, and here the 4090’s massive CUDA core count (16,384 vs 10,496) and higher clock speeds make it 40–60% faster. For example, with a 27B Q4_K_M model and a 2,000-token prompt, a 3090 might take 15 seconds to process the prompt, while a 4090 takes about 9 seconds. For generation, you might see ~15–20 tokens/s on the 3090 and ~20–26 tokens/s on the 4090, depending on the software (llama.cpp, ExLlama, etc.) and context length.

Value: Price vs Performance

At retail, the RTX 4090 launched at $1,599, while the RTX 3090 launched at $1,499. However, the 3090 is now often found used for $700–$900, while the 4090 still sells for $1,500–$1,800 new (and sometimes more due to demand). If you already own a 3090, upgrading to a 4090 gives you roughly 40% more generation speed and 50% faster prompt processing—worth it if you use the model heavily. If you’re buying new, the 4090 is the better value per dollar for inference speed, but the 3090 is the better value if you’re on a tight budget and can accept slower speeds.

Consider electricity: the 4090 is more power-efficient (450W TDP vs 350W TDP, but actually draws less power per token). Over a year of daily use, the 4090 might save you $30–$60 in electricity, which is negligible compared to the price difference.

How to Choose: What Matters for Your Use Case

• If you generate long responses or run a local chatbot 24/7: The 4090’s higher token rate will feel snappier and reduce wait times.
• If you only run short prompts occasionally: The 3090 is fine; the slower prompt processing is less noticeable.
• If you plan to run larger models (70B) with offloading: Both cards will be slow, but the 4090’s bandwidth helps more.
• If you’re on a budget: A used 3090 is the cheapest way to get 24GB VRAM and run 27B models.

Common Problems and Misconceptions

“The 3090 is just as good because it has the same VRAM.� VRAM capacity is only one factor; bandwidth and compute matter for speed. The 4090 is significantly faster despite the same capacity.

“You need to buy a 4090 to run 27B models.� No, a 3090 runs them fine, just slower. Many users run 27B on 3090s with 4-bit quantization and get acceptable speeds.

“More VRAM is always better.� For a single GPU, 24GB is the practical max for consumer cards. If you need more, you’d look at RTX 6000 Ada or used A6000, but those are much more expensive.

“The 4090 runs too hot for LLM inference.� LLM inference is not as stressful as gaming; the 4090’s cooler handles it well, though you should ensure good airflow.

Software Considerations: What to Use

Both cards work with llama.cpp (CPU+GPU), ExLlamaV2 (GPU-only), and text-generation-webui. ExLlamaV2 is generally the fastest for 4-bit models on Nvidia GPUs, and it benefits more from the 4090’s compute. llama.cpp with GPU offloading also works well, but its speed is more bandwidth-bound, so the 4090’s advantage is smaller.

Make sure to use the latest CUDA drivers and a recent version of the software; older versions may not fully utilize the 4090’s architecture (Ada Lovelace).

Alternatives: Other GPUs and Options

• RTX 4080 (16GB): Cannot run 27B at 4-bit without offloading (needs ~14GB, but 16GB is too tight for 4-bit with context). Not recommended.
• RTX 3090 Ti: Slightly faster than the 3090, but rare and often overpriced; not worth it.
• Used RTX A6000 (48GB): Runs 27B at 8-bit and even 70B with offloading, but costs $3,000+ used.
• Cloud APIs: If you don’t need local inference, APIs may be cheaper than buying a 4090, but you lose privacy and control.

Frequently Asked Questions

Can the RTX 3090 run a 27B model without quantization?

No, a 27B model in FP16 uses about 54GB of VRAM, which is far beyond 24GB. You must use 4-bit or 8-bit quantization to fit it on either card. With 4-bit, it uses about 14–16GB, leaving room for context.

What is the real token speed difference for 27B models?

In practice, with 4-bit quantization and a 2048-token context, the 3090 typically gives 15–20 tokens/s, while the 4090 gives 20–26 tokens/s. The exact numbers vary with the model, software, and prompt length.

Is the 4090 worth the extra cost for local LLMs?

If you use the model daily for long sessions, yes—the 40% speed increase adds up. If you only experiment occasionally, the 3090 is more cost-effective. Also consider that the 4090 holds resale value better.

Does the 4090 use less power than the 3090 during inference?

Yes, despite a higher TDP, the 4090 is more efficient. It may draw 250–300W during generation, while the 3090 draws 300–350W, and the 4090 finishes the same work faster, so total energy per token is lower.

Can I use both cards together for 27B inference?

Yes, with software like llama.cpp you can split the model across two GPUs, but the 3090 and 4090 have different speeds, so the slower card becomes the bottleneck. It’s usually better to use only the 4090.

What about the RTX 5090? Should I wait?

As of early 2025, the RTX 5090 is rumored to have 32GB VRAM and higher bandwidth, which would be excellent for 27B and even 70B models. If you can wait, it may be worth it, but it will likely cost more than the 4090 and availability may be limited.

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