RTX 5090 vs RTX 4090 for Local AI

Keep your RTX 4090 if your intended model, quantization and context already run entirely on the GPU at acceptable latency. Buy an RTX 5090 for a new local-AI host when its 32 GB memory solves a 24 GB limit; decline either purchase when the complete job still will not fit. The exact boards compared below are NVIDIA's US Founders Edition cards, with specifications and runtime documentation checked November 3, 2026.

Quick picks: buy, keep or decline

Compare specifications and all conditions

You own a working RTX 4090

Our pick

Keep it

What must be true before ordering

Your longest useful prompt and intended simultaneous requests meet your memory and latency targets

Your job exceeds the 4090's memory but fits the 5090's

Our pick

RTX 5090 Founders Edition

What must be true before ordering

Confirm the actual runtime allocation, host power and clearance

You are considering a used RTX 4090

Our pick

Conditional purchase

What must be true before ordering

The complete basket is worthwhile and written seller protections compensate for its warranty limitation

You want the specific 43 GB Llama artifact below entirely on one GPU

Our pick

Decline both

What must be true before ordering

Change the artifact or choose a larger-memory system

You only need a small model

Our pick

Compare smaller tiers first

What must be true before ordering

Use the GPU category guide to set a sensible memory target

Full comparison table ↔

Swipe to compare. Row labels stay visible.

Your situation	Our pick	What must be true before ordering
You own a working RTX 4090	Keep it	Your longest useful prompt and intended simultaneous requests meet your memory and latency targets
Your job exceeds the 4090's memory but fits the 5090's	RTX 5090 Founders Edition	Confirm the actual runtime allocation, host power and clearance
You are considering a used RTX 4090	Conditional purchase	The complete basket is worthwhile and written seller protections compensate for its warranty limitation
You want the specific 43 GB Llama artifact below entirely on one GPU	Decline both	Change the artifact or choose a larger-memory system
You only need a small model	Compare smaller tiers first	Use the GPU category guide to set a sensible memory target

Your situation	Our pick	What must be true before ordering
You own a working RTX 4090	Keep it	Your longest useful prompt and intended simultaneous requests meet your memory and latency targets
Your job exceeds the 4090's memory but fits the 5090's	RTX 5090 Founders Edition	Confirm the actual runtime allocation, host power and clearance
You are considering a used RTX 4090	Conditional purchase	The complete basket is worthwhile and written seller protections compensate for its warranty limitation
You want the specific 43 GB Llama artifact below entirely on one GPU	Decline both	Change the artifact or choose a larger-memory system
You only need a small model	Compare smaller tiers first	Use the GPU category guide to set a sensible memory target

RTX 5090 vs 4090: the differences that change the order

These are NVIDIA's RTX 5090 specifications and RTX 4090 specifications, not interchangeable dimensions for every partner board.

The 5090 adds 8 GB of documented memory, calculated from those capacities. Graphics power is a board specification; it is not a measurement of your inference job or wall consumption. NVIDIA's gaming chart uses DLSS and frame-generation settings, so it cannot establish a local-LLM speed ratio.

• RTX 4090 Founders Edition24GB
• RTX 5090 Founders Edition32GB

Source: NVIDIA's linked full specifications; memory capacity is not an inference benchmark

Start with the model, quantization and context

Write down the artifact/tag and digest, weight quantization, context limit, cache precision, simultaneous requests and required latency. A model's parameter count is not its running memory requirement. Ollama's library gives these concrete starting points:

These are published package sizes, not measured VRAM allocations. Budget for weights plus context/KV cache, runtime buffers and other GPU users, then validate the actual allocation. Ollama documents that longer context needs more memory and parallel requests increase context memory requirements (context guide, FAQ).

A worked context budget for Qwen3-32B

The model author's configuration specifies 64 layers, eight KV heads and a head dimension of 128. Using the documented key/value cache shape, our planning calculation for one request with an f16 cache is: two caches × 64 layers × 8 heads × 128 values × two bytes × context tokens.

These calculations exclude weights and buffers and are not measured fit guarantees. GB package sizes and GiB cache sizes use different units; do not simply subtract the table's numbers. They show why context changes the purchase even when the weights stay the same. The author's model card documents native context of 32,768 tokens.

Ollama also documents quantized KV caches with Flash Attention, with a memory/quality tradeoff. Trying that is a separate configuration to evaluate on your own tasks, not proof that any context will fit.

Buy for more memory: RTX 5090 Founders Edition

Our new-host pick is the 32 GB Founders Edition when the completed memory budget justifies it. Its strongest documented reason to replace a 4090 is more capacity for the chosen job. A larger-memory card can be the right purchase without a promised tokens-per-second multiplier.

The cost is a different host requirement: NVIDIA's full specifications list 575 W graphics power and 1,000 W system power. Its installation guide corroborates the power requirement. Include any PSU, original compatible cable and case changes in the decision.

Check the exact RTX 5090 Founders Edition and buying options at NVIDIA. Verify the selected seller, condition and board before checkout; this guide makes no stock claim.

Keep when it fits: RTX 4090 Founders Edition

The 24 GB Founders Edition remains a keep recommendation when your job meets its targets. Do not replace it solely because a model has “32B” in its name: the specific Qwen quantization above illustrates why the artifact and context matter.

For an additional purchase, judge the whole host and condition. Its 450 W graphics power and 850 W reference system requirement are lower than the 5090's documented figures, but that does not establish inference energy efficiency. We do not recommend paying a premium merely for an unopened older-generation card.

Check the exact RTX 4090 Founders Edition at NVIDIA, then evaluate the actual offer separately.

Board variants, clearance and power cables

Founders Edition versus partner cards: NVIDIA says add-in-card specifications vary by manufacturer. An OC label or a different cooler is a different board order; use that maker's dimensions, PSU instructions and support policy. “RTX 5090” alone does not identify the chassis fit. A card purchase also needs a compatible host; consult the home-server guide for the broader build.

For the 4090, NVIDIA specifies 304 × 137 × 61 mm plus 36 mm of power-cable space. For either host, measure with front radiators, drive cages and adjacent cards installed. Follow the selected cable's routing instructions and fully seat the connector; the 5090 guide explicitly requires a flush connection.

Order the PSU maker's cable approved for the exact PSU model and revision. Matching the GPU-side plug is insufficient: Corsair's compatibility page, for example, warns that its Type 3/4 cables are incompatible with Type 5 supplies. For the NVIDIA adapter route, use the included correct adapter and dedicated cables specified by the installation instructions.

Runtime support: check the actual environment

Ollama's hardware documentation lists both cards: the 5090 at compute capability 12.0, the 4090 at 8.9. Use a current NVIDIA driver supporting your card and the chosen runtime; do not assume an old container or extension supports the newer architecture.

For custom PyTorch applications, the PyTorch 2.15 release documents a CUDA 13.2 default wheel and CUDA 13.4 wheels. That is a specific supported software route, not a command to upgrade every working installation. Match the application, CUDA build, driver and any custom kernels together; validate the real inference job before retiring the old host.

Compare the complete purchase, condition and warranty

Use this worksheet for each actual offer. Record GPU board/condition; PSU model and original cable; case/fans; CPU/motherboard slot layout; RAM; SSD space; OS/runtime; shipping/tax; return window; and independent backup for configurations and irreplaceable data. Mark owned parts as reused only after checking compatibility. Compare the added basket after any realistic resale proceeds; evaluate operating electricity separately from graphics-power specifications.

New, refurbished and used are separate offers. NVIDIA's Founders Edition warranty states three years from purchase of a new covered product and excludes buyers acquiring it used. Its replacement service may supply refurbished parts without restarting the original warranty period. A seller's “refurbished” label does not itself establish NVIDIA coverage: record who provides that warranty and the written return terms. Partner-board policies need their own check.

For Black Friday or Cyber Monday, save the ordinary exact-board basket and compare the same seller, condition, included adapter, warranty and return window when an offer appears. A cheaper card requiring a new host part can change the decision. We claim no verified sale or discount here.

1. 1

   Define the job

   Fix artifact, quantization, context, cache type, concurrency and latency target.

2. 2

   Validate the host

   Resolve exact board clearance, PSU and original compatible cable before ordering.

3. 3

   Compare the actual offers

   Include required host changes, condition, seller protections and backup.

4. 4

   Keep the successful configuration

   Save the job settings and results after validating the delivered card.

Validate a reproducible inference job

For an Ollama example, download the chosen artifact with ollama pull qwen3:32b. Set parallelism explicitly for the server and cache type to f16 for comparison with the calculation above. Then make an explicit-context request using the documented generate API:

curl http://localhost:11434/api/generate -d '{
  "model": "qwen3:32b",
  "prompt": "Explain the difference between GPU memory and system memory.",
  "stream": false,
  "options": {"num_ctx": 8192, "num_predict": 128, "seed": 42}
}'
ollama ps

These are proposed job settings, not benchmark results. Use OLLAMA_NUM_PARALLEL=1 for the single-request case and OLLAMA_KV_CACHE_TYPE=f16 on the server; follow the FAQ's OS-specific configuration instructions. The PROCESSOR column should show 100% GPU if full GPU residency is your goal. Repeat with your real long prompt and intended simultaneous requests.

Save the model digest, driver/runtime versions and all settings. Compare the API's prompt_eval_count/prompt_eval_duration for prompt processing, eval_count/eval_duration for generation, and load_duration separately; durations are in nanoseconds. Record prompt_eval_cached_count too; compare prompt processing with equivalent cache reuse. Compare warmed jobs under identical conditions. Buy or keep based on those results and your required response time.

How we choose

This comparison uses official NVIDIA specifications, installation and warranty documents, runtime documentation and the model author's configuration. Recommendations prioritize a defined job's memory fit, then the complete host and purchase protections. The cache figures are disclosed calculations; we have not performed physical testing or published an inference benchmark. Documentation conflicts are identified above. See How we choose.

Verdict

Keep a satisfactory 4090. Choose the 5090 when its larger memory budget makes your specified job practical and the whole host passes its checks. Decline either offer when the exact artifact, context or seller protections fail your requirements. Spend on a validated workload, with a written return path.