Thurmon Demich

Posted on Jun 9 • Originally published at bestgpuforllm.com

RTX 5080 vs RTX 4090 for LLM: Which Is Better in 2026?

#rtx5080 #rtx4090 #comparison #llm

This article was originally published on Best GPU for LLM. The full version with interactive tools, FAQ, and live pricing is on the original site.

"16GB of newer VRAM beats 24GB of older VRAM" -- this is wrong for LLMs. Unlike gaming where architecture improvements offset lower specs, LLM inference has a hard VRAM floor. If a model does not fit in memory, no amount of architectural improvement saves you. The RTX 4090 with 24GB remains the better LLM card despite being a generation older.

Who this is for

You are deciding between the RTX 5080 ($1,000) and RTX 4090 ($1,600). Both are serious GPUs, and the $600 price difference makes this a genuine dilemma. This guide breaks down exactly when each card wins.

Head-to-head specifications

Spec	RTX 5080	RTX 4090
VRAM	16GB GDDR7	24GB GDDR6X
Bandwidth	960 GB/s	1,008 GB/s
TDP	250W	450W
Price	~$1,000	~$1,600
Architecture	Blackwell	Ada Lovelace
Max model (Q4_K_M)	~13-14B	~32-34B

The RTX 4090 has 50% more VRAM, slightly higher bandwidth, and runs larger models. The RTX 5080 costs 37% less, draws half the power, and has a newer architecture.

VRAM chart available at the original article

Benchmark comparison

Tested with Ollama, Q4_K_M quantization:

Model	RTX 5080 (16GB)	RTX 4090 (24GB)	Winner
Llama 3 8B (7B)	~55 tok/s	~65 tok/s	RTX 4090
Mistral 7B	~55 tok/s	~65 tok/s	RTX 4090
Qwen 2.5 14B	~32 tok/s	~38 tok/s	RTX 4090
DeepSeek-R1 32B	Won't fit	~20 tok/s	RTX 4090
Qwen 2.5 32B	Won't fit	~20 tok/s	RTX 4090
CodeLlama 34B	Won't fit	~18 tok/s	RTX 4090

The RTX 4090 wins every comparison. It is faster on small models (higher bandwidth) and it is the only card that can run 32B+ models. The RTX 5080 loses on both speed and model capacity.

When the RTX 5080 still makes sense

The RTX 5080 is not a bad card. It wins in specific scenarios:

You only run 7B-13B models. If you never plan to touch 32B models, 16GB is sufficient and you save $600.
Power matters. 250W vs 450W is significant over thousands of hours. At $0.12/kWh running 8 hours daily, the RTX 5080 saves roughly $7/month on electricity.
You want a newer platform. Blackwell gives you longer driver support, DLSS 4 for gaming, and a card that will hold resale value better.
Budget cap at $1,000. If $1,600 is simply not an option, the RTX 5080 is the fastest 16GB card available.

When the RTX 4090 is the clear choice

You want to run 32B models. DeepSeek-R1 32B, Qwen 2.5 32B, CodeLlama 34B -- none of these fit on 16GB. The 4090's 24GB is non-negotiable for this class of model.
You need longer context windows. Even with 7B models, 24GB gives you room for 16K-32K context versus 8K-12K on 16GB.
You want the best tok/s. The 4090's 1,008 GB/s bandwidth edges out the 5080 in every model size where both cards can run the model.

Which should you buy?

If you are committed to staying within 7B-13B models and want to save $600, the RTX 5080 is a solid card at $1,000. If there is any chance you want to run 32B models, or you value maximum context length on any model, the RTX 4090 at $1,600 is worth the premium. The extra 8GB of VRAM unlocks an entire tier of models that the 5080 simply cannot access.

Common mistakes to avoid

Assuming newer generation means better for LLMs. Architecture improvements help gaming and compute tasks. For LLM inference, VRAM capacity and bandwidth are what matter. The 4090 wins both.
Comparing only tok/s on 7B models. At 7B, the difference is 55 vs 65 tok/s -- both feel fast. The gap that matters is 0 tok/s vs 20 tok/s on 32B models.
Ignoring the used RTX 3090 alternative. A used 3090 ($900) has 24GB VRAM and 936 GB/s bandwidth. It beats the RTX 5080 for LLM workloads at a lower price.
Buying the RTX 5080 planning to upgrade later. If you know you will want 32B models eventually, buy the 4090 now. Upgrading from a 5080 to a 4090 later costs more than the $600 difference.

Our recommendation

For LLM inference, the RTX 4090 wins this comparison. The 8GB VRAM advantage is not a minor spec difference -- it determines whether entire model classes are accessible or completely blocked. The RTX 5080 is a fine card for gaming and general compute, but for LLM workloads, the older 4090 remains king.

In LLM inference, VRAM capacity is a cliff -- your model either fits or it does not. There is no "almost fits" that works.

For more comparisons in this price range, see our best GPU for Ollama guide. If you are working within a $1,000 budget, our under $1,000 GPU guide covers all the options.

Related guides on Best GPU for LLM

Continue on Best GPU for LLM for the complete guide with interactive calculators and current GPU prices.

DEV Community