RTX 3090 vs 4090 vs 5090 for Local AI

The RTX 3090, RTX 4090 and RTX 5090 are often compared as local-AI upgrades, but they do not form a simple capacity ladder. The RTX 3090 and RTX 4090 both have 24GB of dedicated VRAM; the RTX 5090 increases that ceiling to 32GB. A faster 24GB card can finish a compatible workload sooner, but it does not make a model requiring more than 24GB fit.

Start with the LLM VRAM calculator and hardware recommender. It builds a planning range from model size, weight precision, context length and concurrency before price enters the decision.

Specification comparison

GPU	Architecture	Memory	Founders Edition graphics-card power	Local-AI decision
RTX 3090	Ampere	24GB GDDR6X	350W	24GB capacity with mature CUDA support; usually considered through the used market
RTX 4090	Ada Lovelace	24GB GDDR6X	450W	Same nominal capacity as the 3090; a speed upgrade rather than a fit upgrade
RTX 5090	Blackwell	32GB GDDR7	575W	Adds 8GB of dedicated VRAM and newer-generation performance, with higher system demands

NVIDIA lists the RTX 3090 as Ampere with 24GB GDDR6X and 350W graphics-card power.[1] NVIDIA lists the RTX 4090 as Ada Lovelace with 24GB GDDR6X; its launch material states 450W power consumption.[2][3] NVIDIA lists the RTX 5090 as Blackwell with 32GB GDDR7, 1,792GB/s memory bandwidth and 575W total graphics power.[4]

The 3090-to-4090 upgrade does not add VRAM

Both cards expose 24GB of dedicated memory.[1][2] If a model, KV cache and runtime allocations exceed that ceiling, moving from a 3090 to a 4090 does not solve the capacity problem. You would need a smaller model or quantization, a shorter context, cache changes, offloading, workload partitioning, or hardware with more usable memory.

If the workload already fits, speed can still matter. The generations use different architectures and compute resources, and the 4090 has higher published memory bandwidth than the 3090 family. But tokens per second depend on the model, runtime, quantization, prompt length, batch or concurrency, and software build. Use the benchmark database only when those settings are visible; do not turn gaming charts or headline AI TOPS into a local-LLM ranking.

The RTX 5090 adds capacity, but 32GB is still a ceiling

The RTX 5090’s 32GB is one-third more nominal VRAM than 24GB. That can create useful headroom for a larger quantization, longer context or more runtime overhead. It does not make every workload that misses 24GB fit. For example, the raw weights of a 70B model at four bits are about 35GB before the KV cache and runtime overhead, so a simple “70B × 4-bit” calculation already exceeds 32GB.

Use the exact artifact size and runtime rather than parameter count alone. The KV-cache guide explains why context can consume the remaining margin after weights load.

Exact Qwen3.5 boundaries on 24GB and 32GB

Complete artifact	Files	RTX 3090 / 4090 24GB	RTX 5090 32GB
Qwen3.5-27B community Q4_K_M plus F16 projector	17.668GB	6.332GB file margin	14.332GB file margin
Qwen3.5-27B community Q8_0 plus F16 projector	29.523GB	Does not fit by files	2.477GB file margin; unsafe runtime boundary
Qwen3.5-27B official BF16	55.563GB	Does not fit by files	Does not fit by files
Qwen3.5-35B-A3B official GPTQ-Int4	24.420GB	Does not fit by files	7.580GB file margin
Qwen3.5-35B-A3B community Q4_K_M plus F16 projector	22.915GB	1.085GB file margin; unsafe runtime boundary	9.085GB file margin
Qwen3.5-35B-A3B community Q8_0 plus F16 projector	37.802GB	Does not fit by files	Does not fit by files
Qwen3.5-35B-A3B official BF16	71.904GB	Does not fit by files	Does not fit by files

The 24GB-to-32GB step changes two listed file-fit outcomes: Qwen3.5-27B Q8-plus-projector and Qwen3.5-35B-A3B official GPTQ cross the 24GB boundary. The 27B Q8 pair still leaves only 2.477GB on RTX 5090 before hybrid-attention state, vision processing, CUDA context, workspaces, allocator behavior or concurrency. Conversely, the 35B Q4 pair technically fits 24GB by files but leaves only 1.085GB, so neither a successful download nor a nominal file fit is a deployment guarantee.

Use the Qwen3.5-27B guide and Qwen3.5-35B-A3B guide for artifact provenance, hybrid-attention cache limits and multimodal safeguards.

Power, cooling and the rest of the PC

The reference power figures rise from 350W to 450W to 575W across these cards.[1][3][4] They are not whole-system consumption. Your CPU, drives, fans, transient behavior and power-supply design also matter. NVIDIA’s RTX 3090 reference guidance calls for a 750W system power supply in its stated test configuration; partner cards can specify different requirements.[1]

Check the exact board—not just the GPU name—for dimensions, slot thickness, connectors, recommended PSU, cooling clearance and warranty. A used RTX 3090 also deserves inspection for identity, physical condition, stability evidence, seller history and return terms. Use the used-GPU buying checklist before treating a lower asking price as lower total risk.

Current 24GB-versus-32GB workload: Muse Glimmer 30B targets 24GB for its official 17GB Q4 route and 32GB for the dynamic Q4 route; the guide separates text, vision, drafter and context memory.

Which one should you choose?

• Your workload needs more than 24GB but fits within 32GB: only the RTX 5090 clears that nominal capacity requirement among these three.
• Your workload fits within 24GB and throughput is the problem: compare matching 3090, 4090 and 5090 measurements with the same model and runtime before pricing the upgrade.
• Your workload exceeds 32GB: do not force this three-card comparison. Consider 48GB or larger professional GPUs, supported multi-GPU execution, unified-memory systems, offloading, or a smaller model.
• Your budget points toward a used 3090: include PSU, cooling, case fit, seller terms and the cost of failure—not only dollars per GB.

Continue with the individual RTX 3090, RTX 4090 and RTX 5090 pages, or compare the current screened hardware offers. Saved offers are not real-time stock; confirm the seller’s current details before buying.

Sources

1. NVIDIA GeForce RTX 3090 family specifications
2. NVIDIA GeForce RTX 4090 specifications
3. NVIDIA RTX 40-series launch announcement
4. NVIDIA GeForce RTX 5090 specifications

Reviewed October 25, 2026. Manufacturer specifications describe reference or Founders Edition designs where stated. This comparison does not claim independent benchmark results.

Considering two 24GB cards? The multi-GPU VRAM guide explains layer/tensor splitting, per-device headroom, topology and why 48GB aggregate is not one automatic pool.

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Choosing these cards for a 32B model? The 32B VRAM requirements guide shows why 24GB is a practical 4-bit target, 32GB adds 6-bit/context flexibility, and neither capacity alone proves speed.

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Buying one of these cards for a 32B model? Use the 24GB, 32GB and 48GB 32B-GPU decision hub after checking the exact artifact and context requirement.

For the 24GB-GPU-versus-128GB-system decision, continue with RTX 4090 vs DGX Spark.

For the used 24GB card versus complete 128GB system decision, continue with RTX 3090 vs DGX Spark.

Choosing specifically between the two 24GB cards? Use the Best 24GB GPU for local AI buyer guide for a value-first RTX 3090 versus performance-first RTX 4090 decision, exact Qwen3.5 boundaries and a reproducible purchase test.

Considering a newer but smaller-memory Blackwell card? Compare RTX 4090 24GB with RTX 5080 16GB after separating model fit from generation and measured speed.

Need the focused two-card decision? Read the dedicated RTX 4090 vs RTX 5090 comparison for official platform specifications, exact Qwen2.5-32B file-fit boundaries and matched-test controls.

Need the focused first-versus-third-generation decision? Read the dedicated RTX 3090 vs RTX 5090 comparison for official platform specifications, exact Qwen2.5-32B file-fit boundaries, used-card safeguards and matched-test controls.

Need the focused first-versus-Blackwell capacity fork? Read the RTX 3090 vs RTX 5080 comparison for official specifications, exact 14B and 32B file-fit boundaries, used-card safeguards and matched-test controls.

Adding a 24GB Radeon to the flagship decision? Continue with the RX 7900 XTX versus RTX 4090 comparison for the same file-fit boundary, different software gates and matched cross-backend controls.