RTX 3090 vs RTX 4090 for local LLMs: speed and 24GB limits

A standard RTX 3090 and RTX 4090 both have 24GB of VRAM. Moving from one to the other does not itself give a larger memory budget. If your local model already fits, compare first-token delay and generation speed on the same workload before deciding whether an upgrade is worth its total cost.

Our existing source records report higher decode rates on the 4090 hosts in the tables below. However, those hosts report 48GB, use different CPUs and system memory, and have different loading and output conditions. They do not establish a controlled speed advantage for a standard 24GB 4090. This guide separates what the records show from what a buying decision still needs.

NVIDIA lists 24GB GDDR6X for the RTX 3090 and 24GB GDDR6X for the RTX 4090. We have not independently established why the submitted 4090 hosts report 48GB; a label alone does not prove a modification or representative retail-card performance.

RTX 3090 vs 4090: the short-chat observations

These six runs were received July 2, 2026, using suite-v1, client 0.0.3 and Ollama 0.31.1. All selected rows report Q4_K_M and batch size one. Model sizes are the reported 7.6B, 8.0B and 14.8B values. The table retains both fast and slow requests rather than choosing a model's highest rate from different workloads.

Scroll each table sideways to see timings and token counts.

The 3090 Qwen 7.6B short request reports 39.86 tok/s, while its concurrent-decode row reports 139.16. The older version of this guide used the latter alongside short-chat results for other models. Those are different workloads; the headline 15–30% faster was not a dependable general GPU comparison.

Why did a 136 tok/s result still wait almost 50 seconds?

The 3090 Llama short request generated at 136.20 tok/s after a 49.960-second first-token wait. Its backend fields report about 26.17 seconds loading and 23.78 seconds evaluating the prompt. The corresponding 4090 record reports about 0.30 seconds loading and 0.017 seconds prompt evaluation. This is evidence of different request conditions, not a clean measure of the GPU's prompt-processing advantage.

The 3090 Qwen 7.6B short request likewise reports roughly 22.86 seconds loading and 23.47 seconds prompt evaluation. We have not identified the cause of every timing difference. Do not treat those first requests as a universal 3090 penalty, discard them silently, or average loading delays into an unrelated warm-response comparison.

For your setup, record the first request separately, warm the model, then repeat the same input and output cap several times while keeping cache policy explicit. Our loading and latency checklist explains how to distinguish loading, prompt processing, queuing and generation.

What happened with a longer prompt?

These inputs contain 3,137 or 3,168 tokens. Within each model pair the input counts match, but generated output lengths differ. Each row is still a single observation. The reported rates describe these requests; they do not certify performance at 32K or 128K context, concurrent serving, coding quality or equal end-to-end task latency.

The 3090 7.6B/8B records identify an EPYC 7663 host with 252GB system RAM; the 14.8B record identifies an EPYC 7702P host with 252GB. All three 4090 records identify an EPYC 7763 host with 1008GB. Exact model digests, common power limits, controlled background load, verified GPU placement and repeated trials are not established by these selected records.

Download the reviewed source snapshot. It retains all four workload rows for each cited run, including the original timing fields. The tables select chat-short and chat-long; the receipt date is not proof of the exact execution date.

Will the 4090 fit a larger model than the 3090?

With the same 24GB capacity, exact model artifact, cache precision and runtime, changing the GPU name does not create another 24GB. Budget for weights, KV or recurrent state, runtime buffers and other GPU users together. Parameter count or download size alone cannot establish whether your intended context fits fully in VRAM.

Use the Qwen3.8 24GB planning example to inspect exact-file inputs and context assumptions. It is an estimate, not a measured 3090/4090 fit result. The Qwen model page explains the hybrid-cache calculation and its limits.

When is paying more for the 4090 justified?

• You already own a 3090 and the workload fits: identify the bottleneck before replacing it. Require a repeated test of the same artifact, runtime, input length, output cap and cache policy on the candidate system.
• You are buying your first local rig: compare current complete-system quotes, used-card condition, warranty, power supply, cooling and case compatibility. This page does not assume a fixed purchase-price ratio or current rental rate.
• Your workload exceeds 24GB: compare a smaller acceptable artifact, lower necessary context, supported offload, or a larger-memory configuration. A stock 3090-to-4090 replacement alone does not solve the capacity gap.
• You are renting: compare total cost per completed task, including setup, model loading, storage and idle time. A quoted hourly GPU price and a rate from an unmatched workload cannot establish the cheaper service.

Only pay for speed that changes your actual workflow. For interactive use, first-token latency and useful answer completion matter alongside tok/s; for queued jobs, check sustained throughput under your intended concurrency. Keep task quality constant when comparing costs.

How to make a more comparable test

Record the exact model tag and digest, quantization, runtime version, actual GPU memory, offload logs, power settings and background load. Separate cold loading from warm requests. Keep prompt text, output cap, thinking mode, context allocation and cache policy fixed, run several repetitions and publish all measurements with failures. A fresh install of the current CLI is not an exact reproduction of these July client-0.0.3 records.

Use the benchmark contribution instructions and inspect a saved local result before submitting it. For newer repeated evidence on a different GPU, see our Qwen3.8 and Gemma 4 RTX 5090 study; it does not supply the missing matched 3090/4090 comparison.

Source review: September 16, 2026. This revision corrects mixed-workload, unverified price and nonstandard-host claims in the original July guide. No new 3090 or 4090 benchmark was run.